COGNITIVE AMPLIFICATION FOR CONTEXTUAL GAME-THEORETIC ANALYSIS OF COURSES OF ACTION ADDRESSING PHYSICAL ENGAGEMENTS

Abstract

A method employing cognitive amplification that reasons within a comprehensive context suited to making decisions in a time constrained scenario, such as battle planning. Select embodiments meld military science with the military art needed for relevant and timely decision making. In select embodiments, a Battlefield Terrain Reasoning Awareness, Battle Command (BTRA-BC) Battle Engine (BBE) significantly reduces the time a staff requires for battle planning. BBE “cognitively amplifies” the ability of planners to conduct Intelligence Preparation of the Battlefield (IPB) and the Military Decision Making Process (MDMP). Consequently, a resultant “human-computer reasoning team” develops and analyzes tactical Courses of Action (COAs) much faster than humans alone and better than computers alone. By exhaustively comparing a multitude of variables that comprise Friendly Courses of Action (FCOAs) and Enemy Courses of Action (ECOAs), embodiments permit a user to expend intellectual energy considering the effect of these variables rather than trying to identify them.

Description

RELATED APPLICATIONS

Under 35 U.S.C. §119(e)(1), this application claims the benefit of prior co-pending U.S. Provisional Patent Application Nos. 61/081,262, Cognitive Amplification For Contextual Game-Theoretic Analysis Of Courses Of Action Addressing Physical Engagements, by Schlabach et al., filed Jul. 16, 2008, and 61/120,217, Cognitive Amplification For Contextual Game-Theoretic Analysis Of Courses Of Action Addressing Physical Engagements, by Schlabach et al., filed Dec. 5, 2008, both incorporated herein by reference.

STATEMENT OF GOVERNMENT INTEREST

Under paragraph 1(a) of Executive Order 10096, the conditions under which this invention was made entitle the Government of the United States, as represented by the Secretary of the Army, to an undivided interest therein on any patent granted thereon by the United States.

BACKGROUND

Battles are often decided based upon which side first reaches key terrain. It is unlikely to conceive a Union victory at Gettysburg if General Buford's Cavalry had required a few extra hours to arrive at the high ground south of town. And yet, using widely-accepted guidelines, it is estimated that over half of a modern Brigade's battle-preparation time is consumed by Brigade and Battalion staff planning, before the Company Commanders even receive the mission and start their own planning. So, any reduction in the amount of time a staff requires for planning results in the unit being faster into action.

The Battlefield Terrain Reasoning Awareness, Battle Command (BTRA-BC) Battle Engine (BBE) offers the potential for significant reductions in the amount of time a staff requires for battle planning. (See also Appendix F for a Glossary of Terms). The prototype BBE application represents an implementation of “Cognitive Amplification” for Contextual Game-Theoretic Analysis. (See also Appendix B for a detailed description of the BBE). As such, it presents a fully functional capability to explain and explore the advantages offered by this approach to military decision making. The BBE cognitively amplifies the ability of battle staff planners to conduct Intelligence Preparation of the Battlefield (IPB) and the Military Decision Making Process (MDMP). Consequently, a “human-computer reasoning team” develops and analyzes tactical Courses of Action (COA's) much faster than humans alone and better than computers alone.

Refer to FIG. 2 illustrating the relationship 200 defining Cognitive Amplification as may be employed in select embodiments of the present invention. Data 201 such as may be employed in the BBE is input to provide Information 202 usable by a human. From this information, Knowledge 203 is gained by both the system and a human user. This is the “science” of the process. The Knowledge 203 gleaned from manipulating the Information 202 via a computer increases the Knowledge 203 of the user that enables the user to employ the art (combining the human's experience, training and background with the Information 202 from the “science”) to yield Wisdom 204 that enables informed efficient planning. There is a distinction between Cognitive Amplification (CA) and Artificial Intelligence (AI). Unlike many AI efforts, CA both retains a human “in the loop” and assigns the human responsibility for the loop. In military applications, for example, the time saved in conducting IPB and MDMP results from computers executing “Military Science” significantly faster than a human while a human expert properly employs “Military Art” in a manner unable to be implemented with a computer. In all cases a human directs a computer's “battlefield reasoning,” thus the resultant “automated cognition” is that of a human, albeit assisted in the science of it by a computer.

Conventionally, there are three fundamental approaches to executing the Military Decision Making Process (MDMP) either in the field use or in research environments:

Human cognition without Computer Amplification. This is the dominant field approach, in which human experts conduct a significant majority of the MDMP cognitive processes manually. Computer Command and Control (C2) systems are used primarily to record results of human cognitive analysis or to aid in visualizing a human-conceived state of battle during a war gaming session. There are several prototypes that may assist in developing detailed plans, but these still require a human expert to input a COA concept in a Mission, Enemy, Terrain, Troops, and Time (METT-T) game context. Much time and intellectual energy is consumed in identifying, developing, and analyzing the variables that comprise Friendly COA's (FCOA's) and Enemy COA's (ECOA's). Advantages of select embodiments of the present invention over Human Cognition without Computer Amplification are speed, precision, and comprehensiveness. A human expert can develop appropriate COA concepts, in context, in a few minutes, the automated Terrain Informed War Game Model employed in select embodiments of the present invention conducts a war gaming analysis in under a second whereas an experienced human staff typically takes an hour or longer. (See also Appendix D for detailed explanation of the Terrain Informed War Game Model). Further, the precision of the war gaming results from employing select embodiments of the present invention is significantly greater than the roughly approximated attrition estimates from a typical manual war gaming session. Finally, select embodiments of the present invention achieve an extraordinary war gaming speed that facilitates a significant increase in comprehensiveness. Human war gaming sessions typically analyze only one battle in detail, i.e., an initially selected FCOA against a most likely ECOA. If the planners conduct any war gaming beyond that (e.g., against other ECOA's), it is typically highly abbreviated. Select embodiments of the present invention can fully war game analyze a large set of FCOA's against a large set of ECOA's in mere seconds.

FOX FOX is a family of research prototypes originally developed by one of the present inventors at the University of Illinois at Urbana-Champaign. FOX uses a “fast abstract” War Game Model to estimate the results of battle. The battle estimates coming out of FOX are inconsistent in their realism and reliability due to immature modeling of weapon and terrain effects for combat attrition. FOX also has a relatively crude mechanism for desired end state. This led to FCOA evaluations that had a large variance compared to a user's actual desired end-state. Select embodiments of the present invention offer a significantly improved, Terrain Informed, force-articulated combat attrition model, to include a set of end state options for a user's consideration. Thus select embodiments of the present invention closely model a user's actual needs. Since the FCOA's produced by the genetic algorithm (GA) employed in select embodiments of the present invention converge upon the end state evaluation criteria, the improved end state mechanism provides this invention significantly with more directability and agility than FOX. (See also Appendix A for a detailed discussion on genetic algorithms). Select embodiments of the present invention also have significantly improved COA Variables compared to FOX, also improving relative directability. Finally, FOX focuses on just the war gaming aspect of the MDMP process. Select embodiments of the present invention provide significantly expanded support to a greater subset of the MDMP process. Select embodiments of the present invention add a front end mission analysis, increased FCOA evaluation support tools, support to a user's initial decision via a risk deprecation analysis, and improved and expanded post-decision planning via support for the IPB Event Template. (See also Appendix E for a detailed discussion of deprecation analysis). This results in a comprehensive game-theoretic support system in which it is easier for a user to fully explore implications of a current METT-T scenario.

Modeling and Simulation (M&S) systems. The military M&S community maintains high-fidelity combat models suitable for use with FOX and select embodiments of the present invention.

These models support controlled MDMP experiments, and the training community often uses these models to support collective battle staff training, including training staff on MDMP procedures. The advantages of select embodiments of the present invention over an M&S approach are speed, usability, and game-theoretic comprehensiveness. The M&S models typically take at least an hour to run a single battle simulation, often requiring more time. Select embodiments of the present invention simulate battle and produce a battle script in a few milliseconds. The M&S systems also usually require a fair number of experts to execute a battle simulation. Even though select embodiments of the present invention are designed for collaborative use by several users (planners), it can be directed easily by one person, making it more usable than the most usable of the M&S systems. Finally, M&S systems are so resource intensive, particularly with respect to time, that they are seldom used to develop a game-theoretic context map of the best FCOA's and best ECOA's available in an engagement scenario. Select embodiments of the present invention excel at providing comprehensive game-theoretic (ECOA v. FCOA) context.

SHAKA (later renamed FOX), a prototype of the present invention, was impractical due to its inability to reason and plan within context, particularly regarding effects of terrain, in particular effects impacting combat tactics. The advantages of select embodiments of the present invention over FOX are realism, directability, and full game-theoretic feature support. FOX uses a relatively immature model for terrain effects. Select embodiments of the present invention leverage sophisticated terrain analysis products produced by the U.S. Army Topographic Engineering Center (TEC). In select embodiments of the present invention, these products include an “articulated” Modified Combined Obstacle Overlay (MCOO), with a Braswell reference of combat terrain effects for each maneuver corridor represented in the MCOO. (See also Appendix B for a detailed discussion of the Braswell Index). FOX also uses an immature model for combat attrition, whereas select embodiments of the present invention leverage the well-established Dupuy QJMA methodology. Select embodiments of the present invention offer significant improvement in the terrain and attrition models leading to highly realistic estimates.

Select embodiments of the present invention deploy a responsive, abstract, Terrain Informed war gaming engine that reasons within a comprehensive context particularly suited to making decisions in a time-constrained scenario, such as during battle preparations. Select embodiments of the present invention are more realistic than FOX and more “user friendly” than M&S systems. By exhaustively comparing the multitude of variables that comprise FCOA's and ECOA's, select embodiments of the present invention permit a user to expend intellectual energy considering the effect of these variables rather than trying to identify them in the first place.

BRIEF DESCRIPTION OF DRAWINGS

FIG. 1 is a flow chart that summarizes a process employed by select embodiments of the present invention.

FIG. 2 illustrates the relationship defining Cognitive Amplification as may be employed in select embodiments of the present invention.

FIG. 3 is a map overlaid with paths resulting from employing the Braswell Index with select embodiments of the present invention.

FIG. 4 is a screen print illustrating visualization of a battle snapshot as may be made available when employing select embodiments of the present invention.

FIG. 5A is an example output of the BTRA-BC MCOO-Maker accomplished by repeatedly using a routing algorithm to conduct a network-pulse analysis of mobility corridors, as employed in select embodiments of the present invention.

FIG. 5B represents an output of the network-pulse analysis of mobility corridors that yields a set of V-lanes that are logically-parallel routes through the network of mobility corridors, as employed in select embodiments of the present invention.

FIG. 6A illustrates an example of the battlefield-physics process the METT-T Parser uses to identify all possible instances of an important COA variable, Unit Boundaries, as employed in select embodiments of the present invention.

FIG. 6B depicts Pascal's Triangle for binomial expansion, a concept used in developing select embodiments of the present invention.

FIG. 7 is a screen print of a Graphic User Interface (GUI) for a Defensive COA selection as may be displayed by select embodiments of the present invention.

FIG. 8 is a screen print of a GUI for an Offensive COA selection as may be displayed by select embodiments of the present invention.

FIG. 9 lists component sub-processes within a Terrain Informed War Game Model as may be employed with select embodiments of the present invention.

FIG. 10 depicts a screen that may be used to establish criteria for a Desired End State as may be employed with select embodiments of the present invention.

FIG. 11 shows a subset of the diagram in FIG. 1 that emphasizes operation of the FCOA Evaluator in directing the Terrain Informed War Game Model to engage an FCOA chosen from the FCOA Candidates against all of the ECOA Candidates as may be employed with select embodiments of the present invention.

FIG. 12 is a screen print representing the predicted performance of a select FCOA given selected criteria for each of three representative ECOA's as may be employed with select embodiments of the present invention.

FIG. 13 depicts the use of an iteration cycle that both manual and automated optimization techniques may employ in select embodiments of the present invention.

FIG. 14 is a screen print of a page of a table summarizing a deprecated risk analysis as may be employed with select embodiments of the present invention.

FIG. 15 is a screen print of an example list of evaluation results showing the use of Red-Amber-Green color highlighting as used in select embodiments of the present invention.

FIG. 16 is a screen print example for a set of FCOA's generated by the genetic algorithm (GA) that also provides a button for performing a Pareto Analysis, as used in select embodiments of the present invention.

DETAILED DESCRIPTION

Select embodiments of the present invention envision a process that employs computer amplification of a human expert's cognitive reasoning to generate and analyze military Courses of Action (COA's) in a terrain informed, game-theoretic context. This cognitive amplification process leverages the institutionalized military procedures of Intelligence Preparation of the Battlefield (IPB) and the Military Decision Making Process (MDMP) to ensure the human expert and the computer share a common reasoning framework. Select embodiments of the present invention divide the cognitive tasks so that the human expert executes and directs military “art.” Military art requires judgment and decisions that are difficult for a computer to address in context thus the need for human judgment. In contrast, a computer is best suited to execute cognitive tasks associated with military “science.” Military science typically requires amounts of correlation and calculation that would be intolerable for a human expert to perform, especially under typical time constraints involved in battlefield scenarios.

Select embodiments of the present invention are designed to simultaneously provide: fast-abstract war gaming; realistic estimates of the combat effects of terrain; realistic combat attrition estimates; comprehensive integration of the MDMP and IPB doctrinal processes; easy usability; and computer reasoning in harmony with, and at the direction of, human users. Before development of select embodiments of the present invention some of these design goals were at odds with one another.

Select embodiments of the present invention, functionally described as human-computer cognitive amplification, provide a decision support system. In military applications, this system may be employed by a tactical commander, typically by his battle staff. The commander may use embodiments of the present invention to readily develop a comprehensive understanding of the relative strengths and weaknesses of candidate Friendly COA's (FCOA's) within the game-theoretic context of likely and dangerous Enemy COA's (ECOA's). That is, select embodiments of the present invention applied to military applications improve a commander's ability to decide, in a timely manner, optimum tactics to employ in battle.

Refer to FIG. 1, a flow chart summarizing a process employed by select embodiments of the present invention. In select embodiments of the present invention, semi-automated procedures that are steps in the process closely adhere to cognitive processes used for decision making (such as the military's doctrinal MDMP) and for employing intelligence (such as the military's doctrinal IPB).

The first four procedures (steps) of the process of FIG. 1, the articulated Modified Combined Obstacle Overlay (MCOO) and Braswell Index 101, the Enemy Order of Battle (OB) 102, the Friendly Order of Battle 103 and the Missions and Postures 104, are data inputs. The remaining steps comprise a mixture of automated procedures and human expert inputs. The process represented in FIG. 1 greatly increases the efficiency of decision making and use of background (“intelligence”), both heretofore depending almost exclusively on human cognition.

In select embodiments of the present invention inputting the Articulated MCOO and Braswell Index 101 establishes a game board upon which an automated planner, such as a Terrain Informed War Game Model 112, may develop attrition estimates of a battle between an FCOA and an ECOA. In select embodiments of the present invention, this input includes a Modified Combined Obstacles Overlay (MCOO), a basic product from the U.S. Army's Intelligence Preparation of the Battlefield (IPB) doctrinal process. An MCOO illustrates the maneuver options for units (tokens on the game board) in a given engagement (battle) situation by identifying obstacles to aggregated token (unit) movement, the mobility corridors 301 (FIG. 3) between those obstacles, and the logical groupings of mobility corridors 301 to form what were historically termed Avenues of Approach (AA's). Operational planners typically have used the MCOO to structure maneuver options for both offensive and defensive COA's.

Select embodiments of the present invention employ a modified (articulated) MCOO developed from a software application called the MCOO-Maker. The MCOO-Maker is part of the BTRA-BC program developed at the Topographic Engineering Center (TEC) of the U.S. Army Corps of Engineers Engineer Research and Development Center (ERDC). The BTRA-BC MCOO-Maker uses a logical partition of an area of operation (AO) known as the Braswell Index. In select embodiments of the present invention, the MCOO-Maker builds upon the Braswell Index to establish a logical infrastructure to support the development of Avenues of Approach (AA's) and Lines of Defensible Terrain (LDT's), consistent with the IPB doctrinal product, the Modified Combined Obstacle Overlay (MCOO). The products from the MCOO-Maker are in greater detail than a typical human-developed MCOO, thus this product is termed an Articulated MCOO. The articulated detail helps a computer explicitly reason through issues that human experts implicitly understand. As an example, experienced planners implicitly know how many subordinates could attack abreast in a given AA, whereas a computer must explicitly tag each AA with its ability to support side-by-side formations of subordinate units. Otherwise, the BBE could not match the ability of a skilled planner to develop COA's with feasible formations in each AA.

FIG. 3 shows one view of the Braswell Tactical Spatial Index (Braswell Index) that enables the BBE to “abstract away” detailed terrain features while retaining the terrain effects upon combat attrition modeling. The darker polygons 302 represent major obstacles to unit movement, while the network of heavy lines 301 represent the center line of the mobility corridors 301, i.e., the lines 301 are a one dimensional (1D) representation of a two dimensional (2D) corridor between every pair of obstacles 302. Combat actions are usually compartmented within those mobility corridors 301, so the BBE's attrition calculations may reference just the smaller data of combat effects for the pertinent mobility corridor 301. For example, a mobility corridor 301 with excellent Cross-Country Mobility (CCM) will increase the (otherwise) combat power of an aggressor. The BBE needs only to import that corridor's CCM combat multiplier rather than that corridor's detailed, geo-rectified CCM information. The BBE retains the feature class key of each mobility corridor 301 to support later visualization in a GIS program such as ArcMap.

Refer to FIG. 3, an annotated map 300 indicating with center lines 301 that indicate the center of the polygon representing the entire mobility corridor 301. In select embodiments of the present invention these maps 300 result from employing the Braswell Index. In select embodiments of the present invention, the Braswell Index establishes a connected network of mobility corridors 301 in and around obstacles 302 to token (unit) movement, consistent with military IPB MCOO doctrine. This abstraction is highly useful because combat between opposing forces typically occurs in an area between two obstacles 302. The Braswell Index identifies and establishes boundaries for this area in order to develop mobility corridors 301. These non-overlapping mobility corridors 301 (i.e., no center lines 301 cross) become a useful spatial index for abstracting (reducing) highly detailed terrain analysis information into compartments (corridors) likely to host engagements such as firefights (small battles).

The map 300 employs a visualization of “polylines” 301, 301A, certain center lines 301A of which bisect what one may consider “open” mobility corridors 301 (only the center lines 301 of which are visible) and others 301 of which represent “pinched” mobility corridors (hereafter all mobility corridors are generically identified as mobility corridors 301). In essence, this connected network of mobility corridors 301 defines the maneuver possibilities for units that want to move through an area. The mobility corridors 301 also define the physical boundaries that would likely contain local engagements, such as firefights. The polylines 301, 301A are the geo-rectified, abstract representations of the associated mobility corridors 301, 301A. This abstraction enables loading a game board into a computer's RAM, rather than the hard drive, literally resulting in a thousand fold increase in speed. This enables the Terrain Informed War Gamer 112 to fight fast, but still retain realism in combat attrition estimates.

In select embodiments of the present invention, the Braswell Index adds further value by providing an organizational and referencing scheme (an index) for a large catalogue of terrain analysis map overlays, such as cross-country mobility (CCMA, soil composition, vegetation, elevation, and the like. The Braswell Index allows for an efficient characterization of the combat effects of terrain in each mobility corridor 301. This improves efficiency because the fully geo-rectified dataset of terrain characteristics is very large, and thus inefficient for calculations that leverage all of that data. The abstracted data using the Braswell Index retains the meta-information required for effective modeling, while also supporting significantly more efficient calculations.

For example, the mobility corridor 301 with a narrow choke point represented by 301B (as compared to the open mobility corridor 301C) and highly-restricted cross country mobility (CCM) due to a large area obstacle 302A would greatly favor a defender over an attacker. In essence, this local terrain situation would increase the (otherwise) combat power of the defender, and decrease the (otherwise) combat power of the attacker. Another mobility corridor 301 might have the exact opposite combat effects, depending upon the local terrain situation. This Braswell Index of terrain analysis products enables the MCOO-Maker to send just abstracted combat effects rather than a much larger catalogue of terrain analysis products. In select embodiments of the present invention, the abstracted combat effects for each indexed mobility corridor 301 are a set of quantified multipliers that may be applied later to the relative combat powers of opposing forces engaged in that mobility corridor 301.

In select embodiments of the present invention, this highly abstracted index of terrain effects enables the Mission, Enemy, Terrain, Troops, and Time (METT-T) Parser 105 to load a still-realistic representation of the battlefield (game board) into a computer's basic memory (RAM), rather than onto a hard-drive. (See also Appendix C for a detailed description of the METT-T Parser). Employing “RAM only” results in simulations that run approximately a thousand fold faster than simulations that must access a hard drive. Computer access to RAM is measured in microseconds, whereas access to hard drives is measured in milliseconds due to the mechanical constraints of the hard drive.

METT-T is an institutional acronym used to denote the battlefield situation by listing the fundamental military components of that particular situation. In select embodiments of the present invention, METT-T Battle Context Mapping improves both speed and quantity employing a human-computer set of procedures (FIG. 1) that enables strong exploration of the game-theoretic dynamic of a pending engagement consistent with military MDMP and IPB doctrine. The mapping (survey) of this game-theoretic dynamic enables a computer to have an articulated appreciation of battle context in a knowledge format easily understandable by human experts directing the analysis. Using terminology from the study of chaos, select embodiments of the present invention allow a computer to acquire “emergent intelligence” about the engagement scenario (battlefield).

Refer to FIG. 4, a screen print 400 of a visualization of an engagement (battle) snapshot using the abstracted Braswell Index as the game board 403. The network edges (line segments) 401 behind the light colored boxes represent the set of Mobility Corridors 301 that constitute Lines of Defensible Terrain 2 (LDT-2). The dark blocks 402 represent attacking forces that have bypassed the defense on LDT-2, and are using other component Mobility Corridors 301 on their V-Lanes 501 to proceed to their attack objectives towards the right.

Refer to FIG. 5A, in which the BTRA-BC MCOO-Maker develops a highly articulated, but still doctrinal MCOO by repeatedly using a routing algorithm to conduct a network-pulse analysis of the Mobility Corridors 301. In select embodiments of the present invention, this analysis uses network routing since combat units (teams) may traverse the area of operation abreast, thus the displacement footprint of each unit must be considered. Furthermore, the routing analysis is “pulse” rather than continuous, since offensive combat units typically traverse an area once, in a predetermined formation.

Two examples of the additional battlefield physics information an Articulated-MCOO provides are Virtual (V) Lanes and Lines of Defensible Terrain (LDT's). A defender's LDT's are logically perpendicular to the attacker's V-Lanes 501 (FIG. 5), and constitute logical groupings of neighboring mobility corridors 301 upon which a coherent defense can be based. Again, a human expert can usually identify these at a glance, and implicitly reason through the battlefield physics of potential COA's. The MCOO-Maker provides an explicit representation of these LDT's, thus the computer can “reason” through the same battlefield physics as the expert uses.

Refer to FIG. 5B. V-Lanes 501, output from the Network-Pulse Routing Analysis, are explicitly more flexible than the Avenues of Approach (AA's) historically displayed on a classic MCOO. A trained human analyst who draws an AA on a MCOO does not annotate the AA by its ability to support two or more subordinate units attacking abreast. The experts that use the MCOO simply understand these implicit capabilities at a glance. The experts can reason through tactical Courses of Action (COA's) that consider the full capacity of that AA. A computer can not implicitly digest these AA capacities, so the BTRA-BC MCOO-Maker explicitly identifies the V-Lanes 501 that enable the computer to reason through the same battlefield physics that a human expert uses to identify potential COA's.

Refer to FIG. 5B. In select embodiments of the present invention, a network-pulse analysis of Braswell-established mobility corridors 301 yields a set of V-lanes 501 that are “logically parallel” routes through the network of mobility corridors 301 from the attacker's start line to the attacker's objective line. This is termed an articulated MCOO because it offers almost all the information of a doctrinal MCOO while providing information about the “battlefield physics” to “inform” the METT-T parser 105.

Refer again to FIG. 1. The METT-T Parser 105 and associated COA Variable Set 106, 107 provide a Terrain Informed articulation of the major elements of a user's (commander's) “abstracted” concept decision. The METT-T Parser 105 develops all possible battlefield physics instances for each COA Variable 106, 107, and arranges sets of instances to reasonably maximize neighborliness. This facilitates later FCOA optimization through the genetic algorithm (GA). Neighborliness is an informal term that describes the correlation between any two adjacent instances of COA Variables 106, 107 and their contributions to the final evaluation score of a solution when all other variables are controlled.

• Enemy Order of Battle (OB) 102. In select embodiments of the present invention, the second data-input is the Enemy Order of Battle (OB) file 102. In select embodiments of the present invention, an OB file is a representation of the number and types of equipment that comprise a unit. If one thinks of the Articulated MCOO and Braswell Index 101 as a game board, then the Enemy OB file 102 provides the Game Pieces (tokens) representing an opposing force.

In select embodiments of the present invention, a Terrain Informed War Game Model 112 employs an “attrition model” to determine likely results of combat. Before implementation of the Terrain Informed War Game Model 112, the two effects of “fast-abstract” and “Terrain Informed” were almost mutually exclusive for use in combat simulations. Select embodiments of the present invention integrate the two. In turn, an attrition calculation based on the attrition model requires estimates of relative combat power of opposing forces. Estimates must appropriately account for the number and types of weapon systems on each side. Select embodiments of the present invention leverage the well-established Quantitative Judgment Method of Analysis (QJMA), described by Colonel (Ret.) Dupuy. Dupuy, Trevor N., Numbers, Predictions, and War: Using History to Evaluate Combat Factors and Predict the Outcome of Battles, Bobbs Merrill, Indianapolis, Ind., ISBN 0-672-52131-8, 1979. The QJMA provides a strong historical basis for assessing the relative powers of individual weapons.

Select embodiments of the present invention receive as input the relative weapons estimates from a BTRA-BC application termed the BTRA-BC Battle Engine Weapons Assessment and Calculation Tool (B-WAC7). The B-WACT implements the QJMA concept to develop a basic relative combat power for individual weapons and weapon systems that aggregate weapons. For example, a heavy battle tank usually has a main gun and several machine guns that work in a synergistic fashion. A user provides a weapon's characteristics to the B-WACT which then provides a QJMA relative combat power for that weapon. The B-WACT also enables a user to aggregate weapons into larger weapon systems that also receive a QJMA relative combat power. Finally, the B-WACT publishes lists of weapon systems as a data file as a possible input. Select embodiments of the present invention enable a user to quickly build OB files for enemy units, thus the aggregated relative combat power of the enemy units is strongly grounded in a QJMA weapons estimate output from the B-WACT.

• Friendly Order of Battle (OB) 103. Similarly, in select embodiments of the present invention, the third data-input is the Friendly OB 103 that forms the set of “friendly game pieces” for use with the game board and the enemy game pieces. In select embodiments of the present invention, a user employs the same process described for the Enemy OB 102, except that a QJMA relative combat power is calculated for friendly units by aggregating the B-WACT estimates of relative combat power for the weapons within the friendly units.
• Missions and Postures 104. In select embodiments of the present invention, a user provides Mission and Posture 104 inputs, which equate to the beginning Game State, to extend the analogy of game pieces on a game board. In select embodiments of the present invention, a user assigns to both the Friendly 103 and Enemy OB 102 sets the following situational information:
  • Unit Strength, to reflect attrition from previous combat operations;
  • Unit Posture from a set of well-established tasks, such as Fortified Defense or Hasty Attack,
  • Unit Morale, supplements the Terrain Informed War Game Model 112 with the influential effects due to training, fatigue, leadership, psychology, moral issues, and the like;
  • Superiority Toggles for Intelligence, Surveillance, and Reconnaissance; for Command and
  • Control; and for Air Superiority; and
  • Game Time Slice; allows a user to affect the temporal resolution of the war game simulation.

The Unit Posture ratings influence the attrition calculations in the Terrain Informed War Game Model 112. For example, a “deliberate defense” emplaces weapon systems in protected firing positions prepared by combat engineers. Since those systems are now more combat effective, the defender receives a “multiplier.” A “hasty defense” typically does not have time to prepare such positions, and receives an appropriate multiplier based on the defender's ability to quickly take advantage of cover in terrain readily available in the immediate area.

The Unit Morale ratings supplement the Terrain Informed War Game Model 112. For example, U.S. forces in Operation Desert Storm enjoyed “Excellent Morale” by almost any historical standard. In contrast, the morale of the Iraqi Army ranged from “Good Morale” (for a handful of Republican Guards Units) to “Panicked Morale” for some poorly led, poorly trained, and poorly motivated reserve units. These panicked units were very hesitant to fully engage U.S. forces, so they did not employ their weapons to full potential. The Unit Morale setting allows the Terrain Informed War Game Model 112 to appropriately degrade the QJMA relative combat power.

The Superiority Toggle ratings supplement the Terrain Informed War Game Model 112. For example, if an enemy unit has local intelligence superiority due to a sympathetic populace, then its forces are assigned an appropriate combat multiplier in the Terrain Informed War Game Model 112.

The Game Time Slice ratings supplement the Terrain Informed War Game Model 112. For example, if a user sets the Game Time Slice to 18 minutes, then the Terrain Informed War Game Model 112 simulates unit movement through discrete displacements reflecting how far each unit can move in 18 minutes, assuming the unit is in maneuver mode. As explained below, after all units displace their appropriate 18-minute distances, the Terrain Informed War Game Model 112 updates attrition and other status information for all units.

The Missions and Posture 104 inputs from a user, together with the other three initial inputs 101-103 described above, provide game context to all subsequent battle reasoning. From the perspective of military science, those inputs 101 - 104 provide METT-T context.

• METT-T Parser 105. In select embodiments of the present invention the METT-T Parser 105, examines the “battlefield physics” of the above four inputs 101-104, and produces two sets of COA variables 106, 107, a set each for the attacker and the defender forces. Depending upon the mission of the friendly unit, one of these sets is designated as the Enemy COA (ECOA) Variable Set 106 and the other is the Friendly COA (FCOA) Variable Set 107. Select embodiments of the present invention incorporate an articulated, multi-criteria FCOA evaluation process that includes criteria for a Terrain Informed commander's Desired End State 114, and also a planner's ECOA IPB set 110. This evaluation process is user adjustable to ensure an optimization process is properly focused on user preferences. These variable sets 106, 107 establish two groups of decisions, one for the Commander's selection of the FCOA, and another decision for an intelligence officer to act as a “devil's advocate” representative for the enemy commander. In select embodiments of the present invention, the METT-T parsed options reflect the actual tactical decision(s) a commander faces in employing forces on a physical battlefield.

In select embodiments of the present invention, the Friendly Commander decides upon a tactical Friendly COA (FCOA) 107 by selecting a variable instance for each of the variables in the set. The commander's staff typically analyzes a set of candidate COA's from which the commander decides which tactic(s) to employ.

A Friendly Commander rarely has direct knowledge of the Enemy Commander's tactical decision, thus during staff analysis the intelligence officer(s) estimate a logical, representative set of Enemy COA (ECOA) 106 options, against which the friendly staff ideally conducts a risk analysis for each FCOA in the FCOA candidate set 111.

The doctrinal military term for analyzing candidate FCOA's against a set of ECOA options is “war gaming.” In select embodiments of the present invention, the METT-T Parser 105 identifies all possible battlefield physics options for each of the pertinent COA variables 106, 107. Note that there are “dominate” variables (upon which others may depend) and “dominated” variables (which may depend on other variables). As a result, the Terrain Informed War Game Model Model 112 analyzes the effect of reasonable “tactical dynamics” a commander and staff may analyze.

Refer to FIG. 6 A, illustrating an example of the battlefield physics process the METT-T Parser 105 uses to identify all possible instances of a COA variable, Unit Boundaries. The Unit Boundaries variable is used in both Offensive and Defensive COA's. For example, if a user plans an attack in two columns using the Num Abreast COA variable, a user must decide where to place the internal unit boundary between the two columns. In FIG. 6A, the user has six possible locations (between V-lanes #1-#6 501) for internal unit boundaries that have been identified in the Articulated MCOO and Braswell Index input 101. Assuming a user selected the #2 instance of the Num-Abreast COA variable, a user must further select one of these six possible internal boundary options before his total COA selection is complete. If a user has five available subordinate units, there will be an associated set of possible Boundary Variable instances for each of the five possible instances of the dominating Num-Abreast COA variable. As illustrated in FIG. 6B, these sets rigorously follow Pascal's Triangle for binomial expansion, a fact useful in organizing a computer's memory (RAM) for this COA Variable.

The number of available subordinate units in the Friendly OB 103 and the number of available V-Lanes 501 input in the Articulated MCOO and Braswell Index input 101 affect the number of possible instances in the Unit Boundaries COA Variable. A user can not provide for an attack three-abreast if there are only two available V-Lanes 501 or two available units. The METT-T Parser 105 accounts for this logic.

In select embodiments of the present invention using similar battlefield physics logic, the METT-T Parser 105 establishes sets of all possible instances for each of the COA Variables, for both attacker and defender as explained fully below. For example an Offensive (Attacker's) COA Variable Set is assigned to the Friendly Forces to structure the FCOA variable set 107, while the Defensive COA Variable Set is assigned to Enemy Forces to structure the ECOA variable set 106. The METT-T Parser 105 also supports the opposite “binding” (Enemy forces in attack and Friendly forces in defense).

• ECOA Variable Set 106. Refer to FIG. 7, a screen print 700 of a Graphic User Interface (GUI) for ECOA selection as may be displayed by select embodiments of the present invention. The lower ¾^th 701 of this screen offers the user a pull-down menu for each of the COA Variables. This enables the user to quickly select the variable instances desired in constructing an ECOA. The upper ¼^th 702 of the screen displays the current selection of ECOA's, each of which has an associated set of variable instance selections.

In the METT-T circumstance of this example screen, the enemy is in a defensive posture, has three available subordinate units, five V-Lanes 501, five LDT's, and five Task-Organizable Subordinates. From this input the METT-T Parser 105 populates the following ECOA Variables:

Total Unit Variables 701A: The METT-T Parser 105 establishes a set of ECOA Variables for the total unit. In the screen 700, options in the left panel 701 A change to reflect the ECOA selections:

• • Num Abreast: describes the number of columns the unit uses in its formation;
  • Unit Boundaries: describes the location of internal boundaries between subordinate units, subject to the Num-Abreast selection;
  • Unit Formation: describes a set of all possible arrays of subordinate units, given the selection for the Num-A breast COA Variable;
  • Unit Assignments: binds particular subordinate units to particular formation slots;
  • Anchor LDT: assigns a game board LDT as the location of the primary defensive array of units;
  • Priority of GS, by Formation Slot: Units often retain General Support (GS) units that support all subordinate units, rather than be task-organized to one of those units;
  • Severity of GS by Formation Slot: provides a percentage distribution of GS support for each subordinate unit;
• Subordinate Unit Variables 701 B: the METT-T Parser 105 establishes the following set of COA Variables for each of the subordinate units [in the screen 700 the pull-down options in the middle panel 701B change to reflect selections for the subordinate unit that is highlighted]:
  • Left and Right Boundaries: assigns control measures that further constrain the physical deployment of a subordinate beyond the assigned internal boundaries.
  • Anchor Line Setback: assigns a physical distance that the selected unit should displace behind mid-point of the Anchor Line LDT selection;
  • Reinforce Policy: assigns Neither, Left, Right, or Both as a policy for the selected subordinate that when NOT attacked, allows it to reinforce a neighboring defense under attack;
  • Withdrawal Criteria: assigns in a range from 95% to 5% strength an attrition threshold that when met, directs the Terrain Informed War Game Model 112 to withdraw the unit from combat;
  • Delay or Reserve: directs the unit to either a Delay or a Reserve mission, if it is not participating in the main anchor line defense, as prescribed by the formation selection;
  • Delay Depth: directs the depth of a delay if the subordinate unit is assigned that task in the Delay-or-Reserve COA Variable;
  • Reserve Lag Distance: directs the distance of reserve emplacement behind the anchor line, if the selected subordinate unit is assigned the reserve task in the Delay-or-Reserve COA Variable;
  • Reserve Threshold: directs the threshold of total unit attrition required before the commitment of the reserve, if the selected subordinate unit is assigned the Reserve task in the Delay-or-Reserve COA Variable;
  • Reserve Guidance: directs the employment philosophy of the selected subordinate unit, when committed, if the selected subordinate unit is assigned the Reserve task in the Delay-or-Reserve COA Variable;
  • Reserve Lane: directs the V-Lane emplacement of the selected subordinate unit, if this selected unit is assigned a Reserve mission by the Delay-or-Reserve COA Variable;
  • Upon Penetration: directs the actions of the selected subordinate unit if penetrated by an attacking force, assuming the subordinate is selected as part of the anchor-line defense;
• Task Organizable Units 701C: This set of COA Variables displays the tactical assignment of selected small units to larger subordinate units (which are still components of the total unit). These variables are fully implemented in the Terrain Informed War Game Model 112.

Again referring to FIG. 7:

• • Num Abreast is mitigated by the number of available subordinate units and number of available V-Lanes 501. In the above example of FIG. 7 there are three possible instances.
  • The distribution of instances for Unit Boundaries follows Pascal's Triangle for Binomial Expansion, as described above.
  • The distribution of instancing for Unit Formation also follows Pascal's Triangle for Binomial Expansion. In this example there is only one possible formation instance for a One Abreast selection, i.e., the three subordinate units' in column formation. If the Num-Abreast selection is two, then two possible instances developed by the METT-T Parser 105 are displayed. These are the third unit behind the left leading unit, and the third unit behind the right leading unit. There would be only one possible formation for a three-Abreast selection, i.e., the three available units abreast. The number of possible formations significantly increases if the user employs five available subordinate units.
  • Unit Assignments may plan a column of units in order A-B-C that is a distinctly different COA selection than a column of units in order C-A-B. The METT-T Parser 105 develops instances for all possible bindings, which is exactly n-factorial in number, where n equals the number of subordinate units. For example, a unit with five subordinate units would have 120 possible bindings.
  • With the Anchor LDT, the METT-T Parser identifies as possible options all LDT's input to a game board.
  • Anchor Line Setback represents a common military technique, and the METT-T Parser 105 provides the set of instances to enable the commander to model that option for each selected subordinate unit.
  • A typical military GS unit is field artillery, but the concept extends beyond that. A user may assign GS priorities to preemptively de-conflict simultaneous calls for artillery support from subordinate units. The number of possible instances for this COA variable is again n-factorial, where n is the number of subordinate units. The instance set is identical to the
  • Unit Assignment instance set, but a user is not constrained to the same selection for these independent variables.
  • Conventionally, the first possible instance delivers an equal distribution of GS resources, where the last priority unit receives almost as much GS support as the first priority unit. The last variable instance delivers an unequal distribution, whereas the first priority unit receives the vast majority of the GS capabilities of the whole unit.
  • The Left and Right Boundaries variables cooperate to identify a set of contiguous V-Lanes 501 for unit deployment, all inside the assigned boundaries for the subordinate unit.
• FCOA Variable Set 107. Refer to FIG. 8, a screen print representing the display for the FCOA selection. The lower ¾^ths of this screen offers a user a pull-down menu for each of the COA Variables. This enables a user to quickly select the variable instances he or she desires in constructing an FCOA. The upper ¼^th of this screen displays the current selection of candidate FCOA's, each of which has an associated set of “variable instance” selections (choices).

The variables for Offensive (attack) COA's are similar to those used in the Defensive COA's described above in FIG. 7. FIG. 8 represents variables for use in planning for a friendly unit in an offensive (attack) posture. The example has four available subordinate units, five V-Lanes 501, and six Task Organizable Subordinates 801C. From this input the METT-T Parser 105 populates the following FCOA Variables 107:

• • Total Unit Variables (left panel) 80IA: The METT-T Parser 105 establishes the following set of (Offensive) COA Variables for the total unit. The pull-down options in the left panel 801 A change to reflect the COA selections:
    • Num Abreast: describes the number of columns the selected unit will use in its formation as mitigated by the number of available subordinate units and number of available V-Lanes 501;
    • Unit Formation: describes a set of all possible arrays of subordinate units, given the selection for the Num-Abreast COA Variable in which the METT-T Parser 105 uses the same Pascal Triangle protocols described for FIG. 7 above;
    • Unit Boundaries: describes the location of internal boundaries between subordinate units, subject to the Num-Abreast selection as described above and the METT-T Parser 105 uses the same Pascal Triangle protocols;
    • Unit Assignments: binds particular subordinate units to particular formation slots and the METT-T Parser 105 uses the same n-factorial protocols for this offensive COA Variable as described above for FIG. 7;
    • Priority of GS, by Formation Slot: establishes the selected subordinate unit priorities for allocation of general support (GS) resources and the METT-T Parser 105 uses the same n-factorial protocols for this offensive COA Variable as described above for FIG. 7;
    • Severity of GS by Formation Slot: provides a percentage distribution of GS support for each of the selected subordinate units and the METT-T Parser 105 uses the same protocols for both the offensive and defensive instancing of this COA Variable as described for FIG. 7 above;
    • Subordinate Unit Variables 801B (middle panel): the METT-T Parser 105 establishes the following set of COA Variables for each of the total unit's subordinate units whereby pull-down options in the middle panel 801B change to reflect selections for a selected subordinate unit:
      • Left and Right Boundaries: assigns control measures that further constrain physical deployment of a selected subordinate beyond assigned internal boundaries whereby these variables identify a set of contiguous V-Lanes 501 for unit deployment that are inside the assigned boundaries for the selected subordinate unit;
      • Stutter Start: For lead attacking subordinate units, this COA Variable specifies a wait-time before initial movement enabling the major unit to create common military formations for movement;
      • Bypass Criteria: establishes a policy for how much defensive force a selected attacking subordinate unit can bypass once the defense has been breached;
      • Withdrawal Criteria: assigns an attrition threshold that when met, directs the Terrain Informed War Game Model 112 to withdraw the selected subordinate unit from combat;
      • Follow and Support (F&S) or Reserve: directs the selected subordinate unit to either a Follow-and-Support or a Reserve mission, if it is not participating as a committed attacking unit, as prescribed by the formation selection;
      • Reserve Lane: directs the V-Lane emplacement of the selected subordinate unit, if the F&S-or-Reserve COA Variable assigns the selected unit a reserve mission;
      • Reserve Threshold: directs the level of overall unit attrition that must be tolerated before committing the selected subordinate unit if the selected subordinate unit is assigned a reserve task;
      • Reserve Guidance: directs the employment philosophy of the selected subordinate unit, when committed, if the selected subordinate unit is assigned the reserve task in the F&S-or-Reserve COA Variable;
      • Reserve Lag Distance: directs the distance of reserve emplacement behind the anchor line if the selected subordinate unit is assigned the reserve task in the F&S-or-Reserve COA Variable;
      • Upon Penetration: directs the actions of the selected attacking subordinate unit should it penetrate a defense employing four policy instances of Stay (stop), Left Envelop (turn left), Right Envelop (turn right), and Turn Deep (go straight);
      • At OBJ: directs the actions of the selected attacking subordinate upon reaching the assigned objective via two alternatives of Stay (at the objective) or Expand (to neighboring objectives, with respect to Unit Boundaries);
      • Task Organizable Units 801 C (right panel): displays the tactical assignment of selected small units to larger subordinate units that are components of the major unit.

In practice, Bypass Criteria has a strong effect on the offensive tactic. A low Bypass Criterion is a conservative tactic that typically produces modest results with modest risk. A high Bypass Criterion is typically considered a high-risk/high-payoff tactic.

For Reserve Guidance, the four possible employment philosophies are Stay in Lane, Best Dent, Best Hole, and First Hole. The first three philosophies control whether and where a reserve unit commits after the Reserve Threshold has been met. The first two philosophies do not require a penetration of opposing defenses for commitment. The Best Dent philosophy directs the reserve unit to the defense location closest to penetration. The Best Hole philosophy directs the reserve unit to the most significant penetration, whereas the First Hole philosophy commits the selected reserve subordinate unit to the first penetration, regardless of whether the Reserve Threshold has been met. The METT-T Parser 105 provides a full set of instances for this variable.

• IPB ECOA Selection 108. As described above for FIG. 7, the top ¼^th of the screen 700 displays the active set of ECOA's. A user develops these ECOA's by clicking the submit (“+”) button after selecting appropriate choices (instances) in each of the pull-down menus.

As represented by the “Manual Input” shape of the IPB ECOA box 108 in the FIG. 1 flow chart, a user may develop ECOA's by hand-selecting constituent COA Variables. In limited performance testing, an experienced user was able to accomplish this task in well under five minutes. This compares favorably to current practices that involve hand drawing COA graphics on an acetate overlay or on a computerized map.

• Reverse IPB 109. Instead of hand-selecting the IPB set of ECOA's using pull-down menus in the screen 700, a user may use select embodiments of the present invention to conduct a Reverse IPB analysis 109. This is done by using a basic process of select embodiments of the present invention from the perspective of an opposing force (e.g., an enemy commander). For example, if a friendly unit is on offense, then an intelligence planner responsible for conducting an IPB may run the process in reverse, using the same terrain game board as input with the Articulated MCOO and Braswell Index 101, and swapping the two unit's OB's 102, 103 and Mission Postures 104. The friendly (nee enemy) unit is now on defense, and the enemy (nee friendly) unit is now on offense. A Reverse-IPB procedure through the recursive use of select embodiments of the present invention enables a planner to better identify a strong reasonable ECOA IPB set 110 available to the opposing force. This provides a better mapping (survey) of the game-theoretic context of the engagement situation.

In select embodiments of the present invention, an intelligence planner now employs select embodiments of the present invention to produce a set of friendly defensive COA's that in reality become the IPB set of ECOA's 108 the planner will use. The basic departure of the Reverse-IPB process 109 from the FIG. 1 flow chart procedures is at 122, 123, where a user (intelligence planner) now selects a set of representative offensive COA's, rather than a single COA for execution. This Reverse-IPB procedure 109 also provides an intelligence planner the ability to identify all reasonable options available to an opposing force (e.g., an enemy commander). All the other advantages as described above would also accrue to this IPB ECOA set 108. Thus the game-theoretic analysis is more rigorous, in turn enabling development of a comprehensive set of FCOA's.

• Representative ECOA Set (IPB) 110. In select embodiments of the present invention, whether accomplished through Reverse IP B 109 or hand-selection via IPB ECOA 108, an intelligence planner need decide upon a representative ECOA IPB set 110 for an upcoming conflict. As noted above, these ECOA's are displayed in the upper ¼^th of the screen 700.

In select embodiments of the present invention, unlike the counterpart COA selection set for FCOA candidates 111, a user stabilizes the ECOA IPB set 110 for much of the game-theoretic analysis. In other words, select embodiments of the present invention reject a co-evolutionary paradigm, in which a late-generation FCOA may be vulnerable to an early-generation ECOA. Since early-generation ECOA's are often not represented in later generations, a user (commander) may be unaware of this vulnerability when relying upon a “co-evolutionary” analysis.

Therefore, in order to provide a standardized analysis of each considered FCOA, an FCOA Evaluator 115 provides an end-state estimate of a submitted FCOA for each ECOA in the ECOA IPB set 110. A user retains the ability to modify the ECOA IPB set 110 until ready to start systematic FCOA evaluation, at which time the ECOA IPB set 110 is “locked.”

In select embodiments of the present invention, it is sometimes preferable to modify the IPB ECOA set 108 after an initial analysis of the relative merits of two or more FCOA's has been completed. If a user later adds to or modifies the IPB ECOA set 108, all relevant changes to FCOA's should be re-submitted to the FCOA Evaluator 115 to insure a corresponding updated evaluation.

In select embodiments of the present invention, the standardization of the evaluation metric is guaranteed by “locking in” the IPB ECOA set 108 as well as the Desired End State evaluation criteria 114 at the FCOA Optimization thru the Genetic Algorithm (GA) 119. Locking in the IPB ECOA set 108 insures that all FCOA's considered by the GA use the same evaluation metric.

Select embodiments of the present invention allow for a secondary, co-evolutionary-like analysis at the FCOA Vulnerability Analysis 124. The FCOA Vulnerability Analysis 124 uses steps of the process recursively, such as the Reverse IPB 109 procedures, to develop ECOA's optimized against a chosen FCOA. An automated FCOA Vulnerability Analysis 124 allows a planner to gain a comprehensive set of Most Dangerous ECOA's and associated (Battle) Scripts. This significantly improves a unit's ability to prepare countermeasures for each vulnerability.

In select embodiments of the present invention, if an intelligence planner has established a truly representative IPB ECOA set 108, the FCOA Vulnerability Analysis 124 will yield a similar ECOA set. If the FCOA Vulnerability Analysis 124 does identify a new reasonable ECOA or ECOA set, then a user (commander) should consider re-initiating the entire planning process from the ECOA IPB set 110, adding the newly identified ECOA or ECOA set to the previous ECOA IPB set 110. Otherwise, a selected FCOA may be vulnerable.

• FCOA Candidates Set 111. Unlike the relatively static ECOA IPB set 110, the FCOA Candidates Set 111 is dynamic. The purpose of the Manual FCA Optimization 118 and the FCOA Optimization thru Genetic Algorithm 119 is to use the FCOA Evaluator 115 to submit “improved” FCOA's to the FCOA Candidate Set 111. These FCOA's are displayed in the upper ¼^th 702 of the screen 700.
• Terrain Informed War Game Model 112. In select embodiments of the present invention, the Terrain Informed War Game Model 112 provides the Game Rules for the Game Board established in the Articulated MCOO and Braswell Index 101 inputs, and the Game Pieces established in the Enemy 102 and Friendly 103 OB's. Either a user, or an automated process working on behalf of a user, selects a single ECOA from the IPB ECOA set 108 and a single FCOA from the FCOA Candidates set 111. From the perspective of classical game theory, these COA selections correspond to two pure strategies (tactics) for a game (battle) whose outcome is non-zero sum and deterministic (i.e., has no uncertainty).

In select embodiments of the present invention, the Terrain Informed War Game Model 112 conducts a fast-abstract simulation of combat for the selected COA's and outputs a time-phased estimate of the location and strength of all selected subordinate units during engagement (battle). The simulation is fast, typically on the order of 10 milliseconds, because the Game Board, Game Pieces, Game Strategies (tactics), and Game Rules are all highly abstracted to support fast calculations in RAM of estimates such as attrition.

In select embodiments of the present invention, time-phased snapshots of selected unit disposition and strength that are output from the Terrain Informed War Game Model 112 are realistic estimates of combat. This is because abstractions of all game objects are crafted to retain only that information pertinent to aggregate unit attrition and maneuver posture.

A realistic, 10-millisecond estimate of combat is a useful tool in the tactical planning domain. In limited performance testing of select embodiments of the present invention by domain experts, it is common for users to direct war gaming analysis of thousands of battles in a few minutes. Select embodiments of the present invention employ repeated, game-theoretic submissions of FCOA's and ECOA's to a Terrain Informed War Game Model 112. Since a battlefield is a chaotic system, context-appropriate tactics are not derivable from first principles. The speed of a Terrain Informed War Game Model 112, directed by a suitable optimization strategy such as the FCOA Optimization thru a Genetic Algorithm 119, facilitates development of an “emergent intelligence” on the appropriate tactic to use in a particular METT-T situation. In limited performance testing, this emergent intelligence is subjectively comparable to the insight of experienced domain experts.

The ability of select embodiments of the present invention to develop appropriate tactics in a given situation does not replace human experts. Rather, select embodiments of the present invention Cognitively Amplify the judgment of human experts to produce an FCOA analysis that is more rigorous, more comprehensive, and completed much faster than possible previously.

Refer to FIG. 9 listing component sub-processes within the Terrain Informed War Game Model 112 employed with select embodiments of the present invention. These include:

• • Array Initial Tokens (Units) IAW COA Variable Settings 112A: the Terrain Informed War Game Model 112 translates directions from the submitted ECOA IPB set 110 and FCOA set 111, and develops appropriate tokens (units) from the Enemy 102 and Friendly 103 OB's, and deploys those tokens to their start positions on the Articulated MCOO Game Board;
  • Increment Time-Slice Counter 112B: inputting Missions and Postures 104 a user specifies the desired game “time slice” and the Terrain Informed War Game Model 112 iterates a time-phased series of sub-steps 112B through 112G until termination criteria are met;
  • Move forces IAW current situation and COA Variable Settings 112C: Within the constraints of battlefield physics (e.g., units require a minimum amount of time to maneuver through a given piece of terrain), the Terrain Informed War Game Model 112 moves each token in this sub-step IAW unique selections of COA Variables;
  • Calculate Attrition for Tokens (Units) in Contact 112D: when opposing forces have moved within a pre-determined “engagement distance,” in the same mobility corridor 301 the Terrain Informed War Game Model 112 places those tokens in a firefight.
  • For Each Token (Unit), Assess Attrition and Update Status 112E: if two tokens are participating in an active firefight, then the Terrain Informed War Game Model 112 assesses attrition by appropriately reducing the current strength of participating tokens (units);
  • Create Snapshot(s) of Token (Unit) Locations and Status 112F: a record of the Game Board location and status of every token during a particular time slice;
  • Battle Termination Criteria Test 112G: the Terrain Informed War Game Model 112 updates to determine if the engagement (battle) should be terminated;
  • Output Battle Snapshot Set 112H: when the engagement (battle) has terminated, the Terrain Informed War Game Model 112 finalizes the set of snapshots for later visualization or evaluation.

If a user executes a war game for the purpose of Visualization 113, the Terrain Informed War Game Model 112 outputs the entire set of battle snapshots to the visualization service. If the purpose of the war game is to support the FCOA Evaluator 115, the Terrain Informed War Game Model 112 outputs only the last snapshot of the engagement, since the FCOA Evaluator 115 focuses only on end state (final snapshot). This technique reduces CPU time and conserves computer memory, enabling evaluation of thousands of FCOA's per minute.

• Sub-steps 112B-112G facilitate maneuvering and engaging tokens (units) in accordance with stored COA policies, thresholds, and guidance for specific engagements. The net effect of this iterated sequence is to model the engaging and maneuvering of all tokens (units) through the duration of the engagement.

Calculate Attrition for Tokens (Units) in Contact 112D is a key aspect of the “Terrain Informed” feature of the Terrain Informed War Game Model 112. In establishing an engagement (e.g. a firefight), the Terrain Informed War Game Model 112 determines the relative combat power of each token (unit), as modified by the local terrain effects abstracted by the Braswell Index in that mobility corridor 301. In select embodiments of the present invention, this simulation rewards tokens (units) that intelligently leverage terrain, and appropriately punishes tokens (units) that disregard terrain characteristics. In select embodiments of the present invention, the Terrain Informed War Game Model 112 then consults an implementation of the well established Dupuy QJMA attrition model to determine how much attrition each token should suffer during subsequent time slices. Since the Terrain Informed War Game Model 112 assesses attrition during each of many time slices, the net effect is a discretized approximation of the widely-accepted Lanchester Differential Equation for combat attrition.

For Each Token (Unit), Assess Attrition and Update Status 112E: in select embodiments of the present invention after attrition has been assessed for all active participants, the Terrain Informed War Game Model 112 compares the status of each token (unit) with the criteria, thresholds, and policies in the executing COA, and takes appropriate action. For example, if a token has suffered attrition to below the Withdrawal Criteria specified for that token in the corresponding COA Variable, then the Terrain Informed War Game Model 112 withdraws that token from engagement (combat), and de-activates the engagement (e.g., afirefight) as appropriate. Withdrawn tokens are not eligible for any movement or other actions for the remainder of the engagement.

Snapshots are “taken” during the course of an engagement (e.g., a battle) and the Terrain Informed War Game Model 112 develops a time-phased set of these snapshots, one per time slice, that is used for later evaluation (FCOA Evaluator 115) or Visualization 113.

Engagement (Battle) Termination Criteria Test 112G is exercised by the Terrain Informed War Game Model 112. Examples of termination criteria may be: all tokens have withdrawn from engagement; all offensive tokens have withdrawn from engagement, and defensive actions have ended; most tokens have withdrawn and remaining tokens are in reserve and thresholds have not been exceeded (and logically can no longer be exceeded). In select embodiments of the present invention, if the Engagement (Battle) Termination Criteria have not been met, the Terrain Informed War Game Model 112 returns its processing flow 901 to the Increment Time Slice Counter 112B. This iterates another sequence of token (unit) movement, attrition, and status updates described in Sub-steps 112C through 112F. If the current engagement (battle) state passes the Engagement (Battle) Termination Criteria Test 112G, then the Terrain Informed War Game Model 112 exits the loop 901, and finalizes the Snapshot S et 112H for use by the FCOA Evaluator 115 and for Visualization 113.

• (Battle) Visualization 113. Eventually, a user wishes to see an actual animation of the engagement (e.g., a battle). In select embodiments of the present invention a simple Visualization 113 displays an abstracted Game Board 403 (FIG. 4), with simple controls for directing which engagement (battle) snapshot to render. FIG. 4 is an example Visualization 113 of an engagement at time slice 38 that corresponds to an estimate of token (unit) locations and status 7 hours and 36 minutes into the engagement (battle) when employing 12-minute time slices. In FIG. 4, the slider bar 404 at the bottom of the screen 400 allows a user to quickly run the animation both directions in time.

In select embodiments of the present invention, Visualization 113 allows a user to gain a time-space appreciation of the engagement (battle) dynamics for the selected FCOA 111 and ECOA 110 IPB sets. This is particularly important if the FCOA candidate 111 is computer nominated, from the FCOA Optimization thru a Genetic Algorithm 119. The animation allows a user to quickly understand the strengths and weaknesses of computer-nominated FCOA candidates 111.

• Desired End State 114 (FIG. 1). Select embodiments of the present invention support the doctrinal Military Decision Making Process (MDMP) that requires an analysis, via war gaming, of FCOA candidates 111 against the ECOA IPB set 110 developed during the IPB. Select embodiments of the present invention provide an FCOA Evaluator 115 that uses a Commander's Desired End State 114 as evaluation criteria. This is convenient since Desired End State 114 is a major component of the “Commander's Intent” component of MDMP doctrine.

Refer to FIG. 10, a sample screen print 1000 of a screen that a user may employ to establish Total Evaluation Criteria 1001 for a Desired End State 114. In select embodiments of the present invention a user (commander) may use this screen to alter default criteria. In select embodiments of the present invention, major criteria categories for a user's (commander's) consideration may be:

• • Overall Unit Criteria Candidates 1002: used to establish a goal of optimizing overall percentage end strength of either an attacker or a defender;
  • Time Criteria Candidates 1005: used to appropriately “recognize” (reward or punish) performance of FCOA candidates 111 in an engagement (battle), depending upon a user's (commander's) selection;
  • Specific Unit Criteria Candidates 1004: establishes goals of optimizing percentage end strength of specific tokens (subordinate units) of either an attacker or a defender; and
  • Mobility Corridor Criteria Candidates 1003: establishes “terrain-based objectives” with goals of optimizing the percentage of total attacker or defender end strength at particular mobility corridors 301 in the terrain game board.

In select embodiments of the present invention a user (commander) develops a Desired End State 114 by selecting a combination of these criteria that reflects how a user would like the game board (battlefield) to look at the end of a successful mission. A user (commander) may establish a weighting scheme to reflect relative preferences for each of the criteria.

Select embodiments of the present invention automatically establish a default set of criteria for a Desired End State 114 to support staff planning and analysis prior to a user's (commander's) formal establishment of criteria. The un-weighted default set may include optimization of overall friendly strength and minimization of overall enemy strength. If a friendly unit is in attack, the default set also may include criteria for maximizing attacker end strength at mobility corridors 301 that end each of the V-Lanes 501, roughly equating to optimizing forces on mission objectives. If a friendly unit is in defense, a default set includes a minimization of attacking end strength at the ends of the mobility corridors 301 of the V-Lanes 501.

Overall Unit Criteria 1002 form the basis of an “enemy based objective” in which attrition of an enemy force is the prime consideration.

Time Criteria Candidates 1005 allow a user to select how time, a critical consideration in accomplishment of a mission, affects performance. This variable allows a user (commander) to explicitly model the importance of time.

With Specific Unit Criteria 1004 a user (commander) may specify a particular token (unit) regardless of employment or may specify uncommitted reserves, regardless of which tokens (units) the COA assigns as a reserve.

• FCOA Evaluator 115 (FIG. 1). Refer to FIG. 11, depicting the operation of the FCOA Evaluator 115 in directing the Terrain Informed War Game Model 112 to engage a to-be-evaluated FCOA from the FCOA Candidates 111 against the ECOA IPB set 110. During each iteration 1101 the FCOA Evaluator 115 compares the Desired End State 114 against the final snapshot produced by the Terrain Informed War Game Model 112. Using a set of unique protocols, the FCOA Evaluator 115 provides a numeric score for each of these criteria. In select embodiments of the present invention for example, if a user (commander) selects Maximize Overall Attacker Strength 1002, an evaluation protocol sets (divides) the final end strength of the attacker to the starting end strength, and then multiplies that figure by the weight assigned to that criterion. In select embodiments of the present invention, other evaluation protocols are similarly straightforward and intuitive, except that “minimization” goals are subtracted from 100% to ensure that high scores are always good from the perspective of the commander of the friendly tokens (units). For example, if friendly tokens (units) have reduced enemy strength to zero, this results in a “before weighting” score of 100 rather than zero.

In select embodiments of the present invention, the FCOA Evaluator 115 returns the result as the score for that evaluation criterion against that particular “evaluated” ECOA from the ECOA Candidates 110. Refer to FIG. 12, a sample screen print 1200 illustrating a screen representing the predicted performance of a first considered FCOA, FCOA-1, given selected evaluation criteria 1201 in which a first criterion (Maximize Overall Attacker Strength) delivers scores 1203 of approximately 55.0, 62.8, and 73.1 for each of three ECOA's (ECOA-1, ECOA-2, ECOA-3) 1202 in the ECOA IPB Set 110. A user (commander) now knows that by selecting FCOA-1 for battle while the enemy selects ECOA-1, the friendly (attacking) strength at the end of the engagement (battle) will likely be 55%.

• FCOA Evaluation 116 (FIG. 1). When the FCOA Evaluator 115 finishes evaluating a submitted FCOA against each of the ECOA's in the ECOA IPB set 110, the FCOA Evaluator 115 saves the resulting evaluation scores into a two-dimensional matrix, an example of which is displayed in FIG. 12. A user (commander) may assign “relative probability” weights to each of the ECOA's 1202 in the ECOA IPB set 110, to ensure the FCOA evaluation is not skewed by a less likely ECOA. The FCOA-Evaluator 115 appropriately sums the weighted scores into a single, cumulative score for the entire FCOA, given the user's (commander) Desired End State 114 and the representative IPB ECOA set 110. In example screen print 1200 this cumulative score is displayed in the bottom-most, right-most table cell 1204. Either the user or the FCOA Optimization thru Genetic Algorithm 119 may employ this score to assist in the FCOA optimization process.
• FCOA Optimization Technique 117 (FIG. 1). In a typical scenario, the number of possible FCOA's is on the order of 1060, using the super-articulated definition of a distinct COA as employed with select embodiments of the present invention. That is an impractical number for an exhaustive search in a chaotic environment such as a battlefield. Thus, an optimization technique is required to quickly find a finite number of “sufficient” candidates for a user's (commander) consideration. Select embodiments of the present invention offer at least two techniques: Manual FCOA Optimization 118, and FCOA Optimization thru a Genetic Algorithm 119. Refer to FIG. 13, depicting use of an iteration cycle 1301 that both optimization techniques may employ. In the manual mode a user may repeat this cycle 1301 up to about twenty times, depending upon the available time. The FCOA Optimization thru a Genetic Algorithm process 119 typically iterates the cycle thousands of times, although the incremental quality improvements in the FCOA are usually much smaller than that attainable by a skilled planner in the manual mode. The FCOA Optimization thru a Genetic Algorithm process 119 normally acquires a higher final FCOA score than a human planner, but it can not account for intangibles that are not modeled by the Terrain Informed War Game Model 112. Since both techniques have their advantages and limits, a savvy user (commander) probably employs a combination of both. An example of this is using the GA to establish an initial Candidate FCOA set 111, and then switching to manual optimization to adjust for any intangibles.
• Manual FCOA Optimization 118 (FIG. 1). In select embodiments of the present invention, if a user decides to manually optimize FCOA's, a FCOA Candidate set 111 is submitted to the FCOA Evaluator 115. A user ascertains the strengths and weaknesses of the current Candidate FCOA set 111 by studying the results from the FCOA Evaluation 116 and any animation using the Visualization step 113. A user then develops new FCOA's for evaluation by adjusting the COA Variables in the previous generation of FCOA Candidates 11 in an attempt to reduce weaknesses and reinforce strengths. See FIG. 16 for an example screen print of a screen 1600 showing the result of a GA “game run” It is impractical for a user to repeat this process a few thousand times. Unlike the GA automated optimization technique, an expert user typically recognizes and manually addresses the weaknesses of an FCOA in significantly fewer iterations. This is because the GA has to re-learn tactics in every game run, whereas a human expert remembers appropriate tactics from training and experience.
• FCOA Optimization thru a Genetic Algorithm 119 (FIG. 1). In select embodiments of the present invention, if a user decides to employ the FCOA Optimization thru a Genetic Algorithm process 119, the representative IPB ECOA Set 110 is locked down, as well as the user's (commander) evaluation criteria for Desired End State 114. This ensures a standardized evaluation of all FCOA Candidate sets 111. In select embodiments of the present invention, a user also has an option of immediately employing the GA with default settings modifying the settings for the search parameters such as population size, selection technique, crossover technique, mutation rate, replacement policy and the like. Select embodiments of the present invention employ a common GA implementation to “breed” successively stronger FCOA Candidate sets 111 by mixing selections of FCOA Variables 107 of successful “parent” FCOA's. Unlike an experienced user, a GA cannot gain immediate insight from a small set of evaluated FCOA Candidate sets 111. The GA compensates by directing the Terrain Informed War Game Model 112 to engage thousands of FCOA's from a dynamically evolving FCOA Candidate set 111. Fortunately, the fast-abstract Terrain Informed War Game Model 112 can do this in just a few minutes, comparing favorably to manual FCOA Optimization 118. Using well understood processes such as Holland's Schema Theorem, the GA learns how combinations of FCOA Variables 107 perform in a particular METT-T context. For select embodiments of the present invention, in limited performance testing by experienced users, GA-gained insight correlates well to intuition of a human expert.

In select embodiments of the present invention, the GA typically finds some FCOA solutions that may not have immediately occurred to the human expert and serves as a check on the human planning process. Also, the GA typically does a better job than the human expert in fine tuning obvious tactical concepts into higher-scored FCOA Candidate sets 111. However, the GA cannot optimize the intangibles that are not explicitly represented in the Terrain Informed War Game Model 112, such as the personalities of subordinate unit commanders and staffs. Thus a user should exercise judgment when reviewing scores from the FCOA Evaluation 116. In summary, for select embodiments of the present invention it is generally prudent to employ a combination of manual and GA optimization to develop a “best” FCOA Candidate set 111.

• FCOA Candidates Set 111 (FIG. 1). For select embodiments of the present invention, when a user decides an FCOA Candidates Set 111 is sufficiently optimized, given available time, the FCOA Optimization thru Genetic Algorithm process 119 is stopped if it is still running. Eventually, a user (commander) makes his MDMP FCOA decision by selecting (or modifying) one of the FCOA's in the refined FCOA Candidate set 111. Further, select embodiments of the present invention provide at least two additional techniques to further analyze the relative merits of the candidate FCOA's. These techniques consider factors that were previously encapsulated in the single, cumulative FCOA score. In select embodiments of the present invention, after the optimization process has delivered a set of FCOA's, this additional analysis of previously abstracted information helps to better inform the Commander's FCOA Decision 122.
• Risk Deprecation Analysis 120 (FIG. 1). In select embodiments of the present invention, the single, cumulative FCOA score in the FCOA Evaluation 116 reflects a war gaming analysis against all ECOA's within the representative IPB ECOA set 110. That approach is appropriate during the early stages of analysis since the GA is still “learning” the tactical situation. The final FCOA Candidate set 111 should be mature enough to have incorporated the appropriate tactics for the current scenario, but the candidates within that set will have different characteristics relative to one another. For example, FCOA-54 might perform very well against ECOA-1 and ECOA-2, but only moderately well against ECOA-3. Conversely, FCOA-55 might perform exceedingly well against the ECOA-3, but only moderately well against ECOA-1 and ECOA-2. Given this situation, a user (commander) might decide to take a calculated risk that an opposing force (enemy) will not select ECOA-3, and thus employ FCOA-54. This “trade-off” analysis can not be performed against a single encapsulated score of an FCOA Evaluation 116 since the ECOA scores are aggregated. Therefore, select embodiments of the present invention offer a Risk Deprecation Analysis 120.

With select embodiments of the present invention, when a user initiates the Risk Deprecation Analysis 120, a computer re-evaluates each Results Matrix of the FCOA Evaluation 116 a number of times, “deprecating” a different ECOA Results column (FIG. 12) each time. If there are 100 candidate FCOA's in the FCOA Candidate set 111 and three ECOA's in the ECOA IPB set 110, this yields 300 deprecated result matrices as explained below. As an example, FCOA-54 will first be re-evaluated through a deprecation of ECOA-1 that has only the results of FCOA-54 vs. ECOA-2 and ECOA-3, dropping (deprecating) ECOA-1. Likewise, FCOA-54 will have two more “analysis” (deprecated) matrices from deprecating each of ECOA-2 and ECOA-3, respectively yielding a second deprecated FCOA-54 analysis matrix comparing ECOA-1 and ECOA-3 and a third deprecated FCOA-54 analysis matrix comparing ECOA-1 and ECOA-2. An articulated, automated Risk Deprecation 120 analysis enables a user (commander) to quickly understand the risk of each of the FCOA candidates 111 relative to any ECOA in the ECOA IPB set 110.

Refer to FIG. 14, a sample screen print 1400 of a page of a table summarizing a deprecated risk analysis as may be employed with select embodiments of the present invention. The first column in this sample 1400 displays the FCOA Name. In this example it is apparent that there are three rows of deprecated analyses for each individual FCOA. The second column, titled D-ECOA 1401, gives the ECOA that is deprecated for that row. The third column, title OR 1402, shows the Original Ranking (OR) of the un-deprecated analysis for that individual FCOA. The fourth column, titled DR 1403, shows the Deprecated Ranking (DR) reflecting the “merit” of that individual FCOA relative to the other candidate FCOA's when the deprecated ECOA is temporarily dropped (i.e., deprecated) from the ECOA IPB set 110. For example, the merit of CN-476 (Gen-2) in row 1 is 30 compared to others but its original ranking was 7. The fifth column, titled CR 1404, shows Change-In-Rank (CR) from the deprecated analysis. For the example of row 1 above, the change is 7-30 or -23. Another example demonstrates an advantage of a deprecated analysis. The candidate FCOA CN-367 (Gen-2) (rows 12-14) is ranked 12^th when evaluated against the entire ECOA IPB set, 110, but moves up to first when the ECOA termed “Strong Right” (row 13) is deprecated. This indicates that CN-367 (Gen-2) is particularly vulnerable to the Strong-Right ECOA, but is otherwise an extraordinary FCOA. Note that select embodiments of the present invention provide for weighting both the DR and the CR as indicated in the columns designated DRW 1405 and CRW 1406, respectively. The sample 1400 also indicates an opportunity for a user (commander) to highlight 1407 values in a column to further facilitate the decision making process by choosing any of “greater than selection,” “lesser than selection,” or “no highlight.”

With select embodiments of the present invention, the Risk Deprecation Analysis 120, allows a user (commander) to quickly find and consider risks associated with the FCOA's that show deprecations of interest, like CN-367 (Gen-2). For example, an intelligence planner may estimate that a Strong Right ECOA is not very likely and a commander may decide to accept that risk. This risk may be more readily accepted if the intelligence planner assures the commander that intelligence is available to determine if the enemy has selected a Strong Right ECOA prior to having to engage a contingency (branch) plan optimized against the Strong Right ECOA. Evaluation Criteria Deprecation Analysis 121 (FIG. 1). With select embodiments of the present invention, a user (commander) has the option (recommended) of also conducting an Evaluation Criteria Deprecation Analysis 121, a counterpart of the Risk Deprecation Analysis 120. The Evaluation Criteria Deprecation Analysis 121 deprecates a user's (commander) Evaluation Criteria for the Desired End State 114, one at a time, rather than the ECOA's from the ECOA IPB set 110. Typically, a user (commander) may have established the original Evaluation Criteria for the Desired End State 114 without significant analysis of the tactical feasibility. Thus, the Evaluation Criteria Deprecation Analysis 121 enables a user (commander) to fully evaluate the “cost” of each evaluation criterion in terms of finding an FCOA that would otherwise score well against the remaining (non-deprecated) evaluation criteria. As a result of this second deprecation analysis, a user (commander) may decide to accept an FCOA with an otherwise low score, since the cost of a particular deprecated evaluation criteria is much more than originally anticipated, given a rigorous war gaming analysis of the tactical situation. In select embodiments of the present invention, the automation for the Evaluation Criteria Deprecation Analysis 121 parallels the Risk Deprecation Analysis 120 and the table (matrix) screen is similar to that of the sample 1400 in FIG. 14. An automated, articulated Evaluation Criteria Deprecation Analysis 121 allows a user (commander) to quickly understand the relative cost of each of the criteria used to evaluate the FCOA's for the original Desired End State 114.

• Commander's Decision FCOA 122 (FIG. 1). Select embodiments of the present invention have other FCOA comparison tools to assist a user (commander). Refer to FIG. 15, a screen print 1500 of a sample list of evaluation results for FCOA Tukhachevsky, showing the use of color coding (shading) 1501 as selected via two buttons 1501, 1503 permitting values above selected limits to be hilited in Red-Amber-Green color coding as used in select embodiments of the present invention. Other tools include filters to include COA-variable filters (not shown separately). After having studied the FCOA Candidate Set 111, and the results of the two deprecation analyses (e.g., sample 1400), a user (commander) typically selects an FCOA for token (unit) implementation in a planned engagement. The decision of the user (commander) represents the Military Art. Employing select embodiments of the present invention, the decision is supported with both objective and subjective criteria facilitated by an extraordinary volume of rigorous analysis, much of which may be considered the Military Science.
• Selected FCOA 123 (FIG. 1). At this step, employing select embodiments of the present invention a commander's staff may delete all others from an FCOA Candidate set 111 and take the necessary steps to implement a selected FCOA.
• FCOA Vulnerability Analysis 124. With select embodiments of the present invention one of the “necessary steps” in implementation is conducting an FCOA Vulnerability Analysis 124 of a selected FCOA. Employing select embodiments of the present invention, a user may execute this in the same manner as the Reverse IPB process 109, except that the user (planner) submits only the selected FCOA to become a single “to-be-re-evaluated” ECOA in a “revisited” representative ECOA IPB set 110. A user (planner) then conducts an optimization analysis to find those FCOA's that are particularly optimized against a single selected ECOA. Typically, a user (planner) uses the FCOA Vulnerability Analysis 124 to identify those ECOA's that are optimized against a selected FCOA. This allows a user (planner) to enumerate vulnerabilities of a selected FCOA and, in turn, prompts a staff to take appropriate countermeasures to reduce identified vulnerabilities. In select embodiments of the present invention, a user completes the FCOA Vulnerability Analysis 124_by submitting each of those “identified” ECOA's to the Terrain Informed War Game Model 112 to produce snapshot sets that “pre-inform” intelligence collection activities.
• Projected Scripts for Most Dangerous ECOA's v. Selected FCOA 125 (FIG. 1). One countermeasure to a Dangerous ECOA is to quickly identify if an opposing force is executing a Dangerous ECOA. Employing this process with select embodiments of the present invention provides a commander sufficient time to react to a potential vulnerability. In select embodiments of the present invention, Scripts are snapshot sets produced by the Terrain Informed War Game Model 112 in the last phase of the FCOA Vulnerability Analysis 124 after the Most Dangerous ECOA's have been identified. With select embodiments of the present invention, these Scripts contain information facilitating development of a focused IPB Event Template 128 and an Event Matrix associated therewith. Specifically, the Scripts maintain a time-phased estimate of the location and status of all tokens (units) relative to the mobility corridors 301 on the game board 403. Select embodiments of the present invention provided fast, comprehensive support to a planner's preparation of the IPB Event Template 128 and Event Matrix via use of Most-Dangerous 125 and Most Likely 127 Projected (Battle) Scripts.
• Representative ECOA Set (IPB, from 108 thru 110) 126 (FIG. 1). If an opposing force commander possessed 100% knowledge of a friendly commander's FCOA selection, then the representative ECOA IPB set 110 used from an earlier analysis would comprise the actual Most Dangerous ECOA's. Fortunately, standard procedures in today's military insure that the opposing forces commander will not know the decision of the friendly commander before engagement. Typically, the best an opposing force commander can do is to develop a reasonable estimate of FCOA's and supplement that estimate with focused intelligence activities, leaving a finite amount of uncertainty. Thus, a diligent planner with the friendly force maintains the original ECOA IPB set 110 that represents a best estimate of an opposing force commander's Most Likely Candidate COA's. In this step a user (planner) retrieves the representative IPB ECOA Set 110 to resubmit to the Terrain Informed War Game Model 112 along with the commander's selected FCOA.
• Projected Scripts for Most Likely ECOA's v. Selected FCOA 127 (FIG. 1). Using the same process described in Projected Scripts for Most Dangerous ECOA's v. Selected FCOA 125, the sets of snapshots produced in Projected Scripts for Most Likely ECOA's v. Selected FCOA 127 become a second set of Scripts - the Most Likely Scripts. Like their Most Dangerous counterparts, these Most Likely Scripts maintain a time-phased estimate of the location and status of all tokens (units), relative to the mobility corridors 301 on the game board 403.
• IPB Event Template 128 (FIG. 1). One of the very last steps of military IPB doctrine is to develop an IPB Event Template 128 and corresponding Event Matrix. In select embodiments of the present invention, the IPB Event Template 128 shows where to collect information that indicates which COA the opposing force has adopted. The Event Matrix supports the IPB Event Template 128 by providing narrative details. Together, these two products pre-inform intelligence collectors by focusing collection requirements. A user (planner) manually develops these IPB products by comparing relative disposition of forces in both sets of Scripts 125, 127. In this comparison a user (planner) conducts a straightforward differential analysis to find unique (“telling”) disposition indicators. For example, a user (planner) realizes that an opposing force deploying more than company size tokens (units) into a particular mobility corridor 301 uniquely indicates the commander's adoption of a particular ECOA generated from either the Most Dangerous 125 or Most Likely 127 IPB sets. In select embodiments of the present invention, a user (planner) establishes on the IPB Event Template 128 a Named Area of Interest (NAI) as a polygon (not shown separately) at the entry point of that mobility corridor 301. In select embodiments of the present invention, a user (planner) also records into the Event Matrix the associated activity, Deployment of larger-than-company force indicates ECOA-2. The IPB Event Template 128 and corresponding Event Matrix a user (planner) builds pre-informs and focuses a token's (unit) intelligence collection activities.

In summary, select embodiments of the present invention are much faster and provide significantly more game-theoretic comprehensive analysis than existing approaches. In select embodiments of the present invention, the collective set of procedures (FIG. 1) provides Cognitive Amplification for MDMP and IPB planners, permitting better and faster analysis of FCOA's and ECOA's in significantly greater quantities than heretofore possible. Select embodiments of the present invention allow human experts to concentrate on conducting Military Art and a computer to execute Military Science at the direction of human users. Conventional computer reasoning processes are incapable of knowledge level interactions since the engagement context is only available in the minds of skilled planners, such as those military planners conducting MDMP and IPB. With select embodiments of the present invention, providing computer-based emergent intelligence quantifies many more reasoning processes beyond “fast and good” MDMP and IPB.

The abstract of the disclosure is provided to comply with the rules requiring an abstract that will allow a searcher to quickly ascertain the subject matter of the technical disclosure of any patent issued from this disclosure. (37 CFR §1.72(b)). Any advantages and benefits described may not apply to all embodiments of the invention.

While the invention has been described in terms of some of its embodiments, those skilled in the art will recognize that the invention can be practiced with modifications within the spirit and scope of the appended claims. For example, although the system is described in specific examples for decision support to military battalions, brigades, and division staffs, for both collective training and real operations, it may be also employed in such diverse applications as military institutional training of personnel on the MDMP process and Military Art; informal assessments of individual and staff capabilities in both Military Art and Military Science, similar to the U.S. Navy's Top Gun program and the U.S. Army's Combat Training Centers for entire units or the Ender's Game novel; decision support to military small unit leaders; establishing topographical mapping requirements for contingency support; support to operations research and systems analysis on tactical command and control systems; and commercial computer and video games both for recreation and for training.

In the claims, means-plus-function clauses are intended to cover the structures described herein as performing the recited function and not only structural equivalents, but also equivalent structures. Thus, although a nail and a screw may not be structural equivalents in that a nail employs a cylindrical surface to secure wooden parts together, whereas a screw employs a helical surface, in the environment of fastening wooden parts, a nail and a screw may be equivalent structures. Thus, it is intended that all matter contained in the foregoing description or shown in the accompanying drawings shall be interpreted as illustrative rather than limiting, and the invention should be defined only in accordance with the following claims and their equivalents.

APPENDIX A

Genetic Algorithm

A Genetic Algorithm (GA) is a well-understood optimization technique for problem solving, as described by David E. Goldberg. Goldberg, David E., Genetic Algorithms in Search, Optimization, & Machine Learning, Addison-Wesley Publishing Company, Inc., Reading, Mass., ISBN 0-201-15767-5, 1989. (Goldberg). Genetic Algorithms emulate the mechanics of natural selection and natural genetics by “breeding” better solutions to a problem from the genetic material of previous solutions. As described on page 33 of Goldberg's text, the foundation for GA theory is governed by John Holland's “schema theorem” that relates the expected effects on a population from the manipulation of basic genetic operations: selection, cross-over, and mutation.

Select embodiments of the present invention implement a “Simple GA” (SGA) as described in Goldberg. This SGA represents all COA solutions, both FCOA's and ECOA's, as “bit strings” which the genetic operators can manipulate. A bit string is a structured sequence of binary bits (“0” or “1”). This implementation structures the bit string so that each COA variable is represented by a component bit string, mapped to a known position on the bit string representing the total COA solution. For example, a COA variable like “offensive reserve guidance” has four possible instances, and is represented by a two-bit segment, where “00” maps to the first possible instance (stay in lane), “01” maps to the second possible instance (best dent), and so on. A COA variable like unit assignments might have 120 possible instances, so it would be represented by a seven-bit segment, where “0000000” maps to the first possible instance, and the last (120^th) possible instance maps to “1110111” (“119” in decimal, which is the 120^th solution when counting starts at “0”). The remaining eight possible numbers in a seven-bit string (“1111000” through “1111111”) are ignored by this implementation, since they do not map to solutions. This implementation retains a “genetic reference map” of where bit strings are located on the total solution bit string, so that any bit string of the proper length can be de-referenced as a viable FCOA or ECOA for use with the Terrain Informed War Game Model 112.

Like any SGA described by Goldberg, this implementation breeds potential new solutions from bit strings by executing genetic operators. “One-point Crossover” is a basic example of a genetic operator, where two “parent” solutions “0000000” and “1111111” are crossed at the third position to create two “children” solutions of “0001111” and “1110000.” Early in the breeding program, one of the children is likely to produce a superior solution to both parents, and one of the children is likely to be inferior to both parents. To retain a constant-sized breeding pool, two solutions of this nuclear family are retained for future generations, and two are discarded. For a properly structured optimization problem (like selected embodiments of the present invention), the schema theorem guarantees the mathematical expectation that “good” schemas will increase in successive generation. This roughly translates into the notion that if a solution has a good “idea” of a particular variable instance, or combination of variable instances, that good idea can be expected to at least survive, and probably thrive in successive generations. Performance testing of select embodiments of the present invention has repeatedly validated this proposition, which means the SGA within this invention is likely to find very good FCOA's and ECOA's, if very good COA's can be reasonably found in a battle context.

Of course, this begs the question of what constitutes a good solution or COA. Select embodiments of the present invention enable a user to specify a desired end state for a battle situation. This desired end state is the commander's preferred disposition (location on the BBE Game Board) as relates the strength of both friendly and enemy forces at the end of a battle. A user may choose and weight a set of evaluation criteria unique to a particular situation. The categories of evaluation criteria are:

Maximize/Minimize Battle Time, in which the length of the battle is measured as a ratio against an exemplary long battle. An FCOA solution that produces long battles, relative to the exemplary long battle, against the ECOA IPB set 110 contributes to a higher FCOA score if a user has selected to maximize battle time. If a user has chosen to minimize battle time the contribution to the total FCOA score is 1.0 minus the ratio of battle time to the exemplary length battle.

Overall Unit End Strength provides for a user selecting either the total enemy unit or the total friendly unit for maximization or minimization. Select embodiments of the current invention measure the ratio of the designated unit strength at the end of battle to its strength at the beginning of the battle. If a user wishes to maximize that end state, then the contribution is the ratio (multiplied by 100 for normalization, as are all ratios in this part of the protocols). If a user desires to minimize the end state then the contribution is 1—the ratio (multiplied by 100).

Specific Unit End Strength allows a user to select a particular enemy or friendly unit for minimization or maximization, using the same start/end strength ratio described above.

Uncommitted Reserve End Strength allows a user to calculate the strength of all friendly (or enemy) units that are uncommitted at the end of battle and then divided by the start strength of the total set of friendly (or enemy) units to develop a customized ratio for a user that wants to maximize (or minimize) the amount of forces that are immediately ready for a follow-on battle.

Mobility Corridor End Strength allows a user to select a set of mobility corridors 301 on the BBE Game Board 403 and also to minimize (or maximize) enemy (or friendly) forces in that mobility corridor 301 at the end of battle. The end strength of the selected opponent in that mobility corridor 301 is calculated as a ratio of the start strength of the entire opponent start strength. This enables a user to specify mobility corridors 301 to secure (retain possession of) or deny to the enemy.

A user weights each of the selected evaluation criteria and the total set of evaluation protocols is considered in each battle for the FCOA evaluation. The resultant evaluation includes comparing an FCOA against each of the ECOA's in the ECOA IPB set 110. Further, a user may weight those ECOA's according to their relative importance or probability of adoption in the total evaluation. The FCOA Evaluator 115 then calculates an FCOA Evaluation 116 matrix for the evaluated solution (FCOA). This evaluation includes a single score that can roughly be interpreted as the “goodness” of the FCOA, with respect to a user's Desired End State 114, for the given METT-T battle situation. To ensure that scores of all FCOA's may be compared, select embodiments of the present invention “lock” the evaluation criteria and ECOA IPB set 110 before running the GA. This avoids the distraction of “co-evolution” in which ECOA's are allowed to evolve against evolving FCOA's. Co-evolution makes comparison of FCOA's extremely difficult because of inconsistent evaluation standards.

The integration of evaluation criteria with the GA makes select embodiments of the present invention adaptable to the METT-T battle context as well as enabling a user to change evaluation criteria to explore options. If a user decides to value the left side of the battlefield more than the right side, the corresponding mobility corridors 301 are selected as evaluation criteria, and the GA breeds FCOA's that are more likely to satisfy the modified criteria. As an additional example, if a commander decides to destroy a particular enemy unit he can manipulate the evaluation criteria so as to promote the GA to breed FCOA's that are more likely to produce that result.

APPENDIX B

The Braswell Index and the BBE Game Board

Military doctrine such as Army Field Manual 34-130, Intelligence Preparation of the Battlefield, 1994, strongly recommends detailed terrain analysis prior to the development of COA's. The U.S. Army Corps of Engineers' Topographic Engineering Center has developed a powerful suite of semi-automated terrain analysis tools to support this task, but the resulting products are extremely large, typically measuring tens or hundreds of megabytes. Battle simulations that use these products generally produce more realistic estimates of combat attrition but those simulations run extremely slow relative to select embodiments of the present invention. This is due to the need for the simulation to continually access terrain analysis data on a computer's hard drive rather than in RAM.

Refer to FIG, 3. The Braswell Index retains pertinent effects of the terrain analysis by grouping them according to mobility corridors 301 between obstacles (to Unit Maneuver) 302. On a map those mobility corridors 301 are represented by polylines 301. Since units can maneuver around obstacles 302 and through mobility corridors 301, the representative polylines 301 form a network topology that may be used to support maneuver analysis for tactical units. This maneuver network forms an infrastructure for the BBE Game Board 403 (FIG. 4) that calculates unit maneuver via the polylines 301, although the real-world representation of that abstraction is a unit that maneuvers through the represented mobility corridor 301 represented by the polylines 301 as the “center” of the mobility corridor 301. The Braswell Index further empowers the BBE Game Board 403 by characterizing the combat effects of terrain in each two-dimensional (2D) mobility corridor 301 along each representative one dimensional (1D) polyline 301. For example, if the cross country mobility (CCM) characteristics of the terrain within a mobility corridor 301 significantly favor the defense, then the Braswell Index records an appropriately high combat multiplier for the BBE to use in calculating attrition for any simulated combat in that mobility corridor 301. The defender's combat strength will correspondingly increase due to having appropriately leveraged the characteristics of terrain. If combat moves to another mobility corridor 301 that favors the attacker rather than the defender select embodiments of the invention may calculate attrition with an appropriate multiplier for the attacker rather than the defender.

The Braswell Index calculates these terrain effects for the major elements of a tactical terrain analysis and records multipliers for both attacker and defender in the reference for each mobility corridor 301. Because the Braswell Index stores the combat effects as simple multipliers in each mobility corridor 301 rather than the associated geo-spatially rectified terrain analysis product, the size of the BBE Game Board 403 is typically several thousand times less than traditional terrain analysis products—kilobytes compared to megabytes. As a result, the Terrain Informed War Game Model 112 loads the BBE Game Board 403 into RAM (fast) memory (microsecond access) rather than having to rely upon a slow hard drive (millisecond access). This is why the battle simulations in select embodiments of the present invention estimate combat attrition several thousand times faster than other combat attrition models and still retain realistic modeling of the terrain's substantial influence (effects) on force attrition.

Refer to FIGS. 5A and 5B. The Braswell Index forms the basis for the BBE Game Board 403, depicting where units can maneuver. However, military units typically do not maneuver independently in combat. They typically maneuver as part of a formation of cooperating units. The movement of these unit formations requires more terrain analysis than the Braswell Index provides. This additional analysis is provided in the Articulated MCOO 101 produced by the MCOO-Maker application developed by the Topographic Engineering Center. The Modified Combined Obstacles Overlay (MCOO) is a traditional Army IPB product (FM 34-130) that enables a planner to understand the total unit maneuver formations supported by the terrain. Military units typically attack from a line of departure (LD), which can be considered the start line for attack, and they maneuver towards a finish line, which is usually called the “objective.” The Braswell Index only shows options for maneuver, whereas the MCOO displays the routes of connected mobility corridors 301 from the anticipated starting line or area (LD) to an anticipated end point or area (objective). These routes are represented by associated polylines 301 as shown in FIG. 5A.

Conventionally, cooperating military units do not cross paths in an attack since that would significantly increase the risk of fratricide (friendly fire casualties), with almost no tactical advantage. Therefore, the purpose of an MCOO is to show how a formation of units may traverse an area of operation (AO) without crossing paths. This infers that network flow algorithms are more appropriate than normal path finding algorithms. However, an attacking unit “flows across” an AO only once rather than continuously. This merits an adjustment of the analysis from a network flow algorithm to a “network pulse” calculation. The MCOO-Maker conducts this network pulse calculation on the Braswell Index, and considers how many units can conceivably attack abreast in each mobility corridor 301. In FIG. 5A three units can attack abreast in the mobility corridor 301 that converges the three routes. The MCOO-Maker further develops logically-parallel routes using the V-lanes 501 that do not cross paths, but may share the same mobility corridor 301 on occasion. In those events, the mobility corridor 301 is virtually divided into sub-mobility corridors to support multiple units maneuvering through the same mobility corridor 301. The MCOO-Maker further refines this Articulated MCOO 101 by separating the logical paths into geo-spatially rectified paths of connected mobility corridors termed Virtual (V) Lanes 501 that form a combat maneuver matrix for the BBE to employ.

In select embodiments of the present invention, V-Lanes 501 form logical maneuver options for an attacking unit whereas the Lines of Defensible Terrain (LDT's) 401 FIG. 4 form employment options for a defending unit. If the essence of the attack is to deliver an effective network pulse, then the essence of the defense is to deploy an effective “network block” that prevents any of the attacking pulse from reaching the objective line. The LDT's 401 are a cooperating set of mobility corridors 301 that effectively form a network block of the MCOO's network-pulse attack analysis. As a result, the Articulated MCOO is the meaningful addition to the Braswell Index of V-Lanes 501 (for formation attack analysis) and LDT's 401 (for formation defense analysis). Together, the Articulated MCOO and the Braswell Index form the basic Game Board 403 for the BBE war games.

APPENDIX C

METT-T Parser

The Mission, Enemy, Terrain, Troops, and Time (METT-7) Parser 105 employed with select embodiments of the present invention evaluates battlefield options in the form of COA variables for both attackers and defenders. A Course of Action (COA) may be either an offensive or defensive COA; assigned to an enemy as an Enemy COA (ECOA), or to a Friendly as a Friendly COA (FCOA). As a set, the FCOA variables for a situation define the major options for a friendly commander in developing an Operations Plan (OPLAN) that directs a unit in combat. COA variables are discussed in the Detailed Description above.

COA Variables are highly dependent upon the METT-T situation. For example, if an area of operation (AO) is small a commander has limited options and if the force comprises three subordinate units instead of five, options are further constrained. The METT-T Parser 105 conducts an inventory of the terrain Game Board 403 by employing the Articulated MCOO and Braswell Index 101. The more V-Lanes 501 available in an Articulated MCOO the more options a friendly commander has to consider. Several LDT's 401 provide more options for a defending unit whether friendly or enemy. The METT-T parser 105 conducts an inventory of the game pieces (units) by examining the Friendly OB 103 and Enemy OB 102. The METT-T Parser 105 then consults the Missions and Postures 104 identified by a user. This indicates which side is attacking and which side is defending.

The METT-T Parser 105 then develops a master FCOA Variable Set 107, as well as a master ECOA Variable Set 106. These sets include all possible instances for each COA Variable in each set. Refer TO FIG. 6A. For example, if there are seven V-Lanes 501 in an area of operations, and a commander desires to attack two units abreast, this automatically implies one subordinate unit boundary between the two lead subordinates. The METT-T Parser 105 develops and maintains a set of options for this COA Variable for all possible locations of that one subordinate unit boundary. In this example there are six possibilities, one each between each pair of neighboring V-Lanes 501. If the METT-T Parser 105 receives nine V-Lanes 501 from the Articulated MCOO, then the METT-T Parser 105 develops eight possible instances for that COA Variable. In the same manner, the METT-T Parser 105 develops all possible instances for all other COA variables in the battle situation.

By developing and maintaining all possible variable instances, select embodiments of the present invention enable a user to model any possible COA for selection into a ECOA IPB Set 110 and the FCOA Candidates Set 111. The Terrain Informed War Game Model 112 then provides a user the ability to understand tactical consequences of selected COA's.

APPENDIX D

Terrain Informed War Game Model

Refer to FIG. 1. The Terrain Informed War Game Model 112 is a fast, abstract battle simulator that produces realistic estimates on final disposition and strength of opposing units, given a select FCOA from the FCOA Candidates set 111 and a select ECOA from the ECOA IPB Set 110. This battle simulator is abstract in that it represents only a small percentage of terrain and force information available in conventional training simulations. It is a realistic simulation because the information it maintains and uses is only that most pertinent to calculating attrition and maneuver estimates. The resultant simulation is fast compared to conventional simulations, e.g., a battle is simulated in a few milliseconds vice a few hours. As implemented in select embodiments of the present invention, the Terrain Informed War Game Model 112 provides cognitive amplification for a user, enabling development and analysis of a far greater number of COA's than otherwise possible.

The Terrain Informed War Game Model 112 provides this cognitive amplification by working together with other elements of select embodiments of the present invention. A user provides Mission, Enemy, Terrain, Troops, and Time (METT-T) battle context to this game “engine” by establishing a game board 403 from the Articulated MCOO and Braswell Index 101, as well as game pieces (tokens) from the Enemy Order of Battle 102 and the Friendly Order of Battle 103. A user also provides a purpose to the war game through a Missions and Postures input 104. The METT-T Parser 105 refines these inputs into a form usable by the Terrain Informed War Game Model 112 by developing the ECOA Variable set 106, FCOA Variable set and the METT-T context (or battle situation) for the Desired End State interface 114. The ECOA Variable Set 106 enables a user to develop an ECOA IPB set 110 to facilitate analysis of various scenarios. The ECOA IPB set 110 represents an opposing force's (enemy commander's) major options in an upcoming engagement (battle, firefight, and the like). A user may also employ elements of select embodiments of the present invention to conduct a Reverse IPB process 109 to provide a reasonable survey of enemy tactical options. The FCOA Variable Set 107 enables a user, or the Genetic Algorithm 119 to develop a set of FCOA's that provide options (FCOA's) to employ.

A user has some pre-conceived notions about the relative merit of various FCOA's and ECOA's being considered but these fall short of a comprehensive analysis in a game-theoretic context. The Terrain Informed War Game Model 112 assists a user in conducting a comprehensive analysis by developing a reasoned estimate of the likely outcome of any selected FCOA against any selected ECOA. Since the ECOA Variable Set 106 and FCOA Variable Set 107 support the development of any and all COA's, within the limits of a combat model, the Terrain Informed War Game Model 112 examines any possible combat interaction in a particular METT-T circumstance. A user may employ the Terrain Informed War Game Model 112 to verify any pre-conceived notions about the strengths of particular FCOA's against select ECOA's. A user then “fine tunes” preferences through manual FCOA Optimization 118. Alternatively, or in addition to, a user may employ the Genetic Algorithm (GA) 119 to automatically survey the tactical METT-T context and determine which FCOA's will succeed against the ECOA IPB Set 110.

As will be described later in this appendix, the Terrain Informed War Game Model 112 maneuvers units across the BBE Game Board 403 in accordance with the governing COA for that unit, i.e., the “selected” FCOA for friendly tokens and the “selected” ECOA for enemy tokens. When opposing tokens engage within the same mobility corridor 301, the Terrain Informed War Game Model 112 estimates the duration of the ensuing engagement, as well as the likely attrition probability for each token, given the current strength of the units, their posture, and the corresponding tactical effects of the local terrain, the latter being a reference “call” to the appropriate Braswell Index multiplier for that mobility corridor 301. After each discrete “clock tick” in the simulation, the Terrain Informed War Game Model 112 checks conditional rules found in the governing FCOA and ECOA. These rules have thresholds and other conditions that may trigger a withdrawal or a call for local reinforcement by a reserve unit.

Depending upon the governing COA, units may stay in local engagement for several clock ticks before a terminating event like withdrawal, bypass, or annihilation. If a defender has chosen an appropriate defensive COA against an attacker's offensive COA, then no attacking units may reach the objective. If the defensive COA is mis-matched against the offensive COA, then some or all of the attacking units may reach the objective.

The Terrain Informed War Game Model 112 terminates the engagement when all units on both sides have finished their maneuvers and “contacts.” The Terrain Informed War Game Model 112 then delivers “(battle) snapshots” to other elements of select embodiments of the present invention. A snapshot is a simple report for every token of its percentage strength and disposition (location) with respect to the respective mobility corridors 301. If the Terrain Informed War Game Model 112 is reporting to the (Battle) Visualization service 113, then it sends a snapshot for every clock tick of the simulated engagement for subsequent animation and visualization. If the Terrain Informed War Game Model 112 is reporting to the FCOA Evaluator 115, then it only sends a snapshot for the final clock tick of the engagement for comparison to the Desired End State 114.

As described in Appendix A the Genetic Algorithm employed with select embodiments of the present invention allows a user to specify evaluation criteria for the Desired End State 114. The FCOA Evaluator 115 uses these evaluation criteria to provide an objective, normalized score for each engagement. If the score is high, then the FCOA accomplished most of the criteria established in the Desired End State 114 against the ECOA selected for that engagement. However, an enemy may not select that particular ECOA in actual battle, so military IPB Doctrine (e.g., Army doctrine specified in FM 34-130) recommends that a user war game an FCOA against all options represented by the ECOA IPB Set 110. The FCOA Evaluator 115 automatically engages the Terrain Informed War Game Model 112 to produce each score for the “evaluated” FCOA against each of the ECOA's in the ECOA IPB set 110. This results in a matrix, an example of which is shown in FIG. 12. The cumulative score 1204 for the FCOA represents an objective assessment of that FCOA's goodness as measured against all evaluation criteria and against all war gamed ECOA's. As described in Appendix A the Genetic Algorithm uses this cumulative score 1204 as fitness criteria when searching for better FCOA's.

Refer to FIG. 9. The section above explained how certain elements of select embodiments of the present invention employ the Terrain Informed War Game Model 112. The following explains the internal workings of Terrain Informed War Game Model 112 in more detail. The Terrain Informed War Game Model 112 initializes the METT-T context of a battle by importing the Articulated MCOO and Braswell Index 101, the Enemy OB 102, the Friendly OB 103, a selection from the ECOA IPB set 110, and a selection from the FCOA Candidates 111. As displayed at 112A, the Terrain Informed War Game Model 112 then consults the two governing COA's to array initial game pieces at their starting locations. As an example, a Defensive COA, either the ECOA or FCOA, specifies the Line of Defensible Terrain (LDT) 401 along with the specification of which forces should be arrayed along that LDT 401 and where.

The Terrain Informed War Game Model 112 then initiates a “maneuver-attrit-react” cycle for each clock tick in the simulated engagement. When a user establishes the Missions and Posture 104 for the simulation, a “time slice” or clock tick duration is established that may vary from six minutes to 30 minutes. Thus, an engagement that lasts three hours has 30 clock ticks if the time slice is set to six minutes. If the time slice is set to 30 minutes that engagement has only six clock ticks. The maneuver-attrit-react cycle displayed in FIG. 9 is executed once for each clock tick.

At step 112B the Terrain Informed War Game Model 112 increments the clock tick to indicate another time slice should be simulated. At step 112C the Terrain Informed War Game Model 112 checks the status of each token with respect to the governing COA's directions. If the token is not engaged and the governing COA directs forward movement along a V-Lane 501 (FIG. 5), then the Terrain Informed War Game Model 112 calculates a forward displacement for that token consistent with its movement speed and the ability of the mobility corridor 301 to facilitate movement. Prior to reaching the calculated displacement location, the token may come within engagement range of an opposing token. In that event, the token stops and engages with the opposing token. Every token continues in this manner for step 112C of every clock tick in accordance with the directions of the governing COA.

At step 112D the Terrain Informed War Game Model 112 assesses attrition for all tokens engaged that clock tick. The amount of attrition is a function of the relative strengths of the opposing tokens and also the effect of the local terrain within the mobility corridor 301 to support the appropriate operations. The “current strength” property of each token is adjusted downwards appropriate to the attrition inflicted by the opposing token. In select embodiments of the present invention, this attrition model is derived from the Dupuy QJMA methodology, as explained in the Detailed Description. Since attrition is assessed every clock cycle, the result is a discrete approximation of the Lanchester Differential Equations, dA/dt=kD and dD/dt=k′A, where A is the strength of the attacker and D is the defender's strength. (Dupuy 1979, page 148). Some approximation of the Lanchester Differential Equations is preferred by most operations analysts in the community.

At step 112E the Terrain Informed War Game Model 112 compares the status of each token with the directions in the governing COA. As an example, both offensive and defensive COA's specify withdrawal thresholds for each subordinate unit. When the subordinate unit's strength is reduced below a threshold specified in the governing COA, the Terrain Informed War Game Model 112 changes its posture to withdrawn, marking it as ineligible for any engagement. Similarly, there is a threshold for the commitment of a reserve along with policy guidance for how to employ that reserve when committed. Depending upon the directions of the governing COA, the Terrain Informed War Game Model 112 may change an activated reserve outside its current V-Lane 501.

At step 112F the Terrain Informed War Game Model 112 creates a snapshot of the current game state. This snapshot is a record of each token's current mobility corridor 301, the progress through the mobility corridor 301, and its strength. If the simulation is to support later visualization, this snapshot is attached at the end of a set of snapshots for possible animation on a geospatial information system (GIS). If the simulation is for evaluation purposes only the last snapshot of the engagement is archived for later submission to the FCOA Evaluator 115.

At step 112G the Terrain Informed War Game Model 112 checks to see if the engagement termination criteria are met. If any token is still engaged the engagement continues and the simulation cycles control 910 back to step 112B, Increment Time Slice Counter. If all tokens are withdrawn or are at their final destinations (objective line), or are reserves that will never be committed (having not met their thresholds), then the engagement may terminate and the appropriate snapshot records returned to either the Visualization service 113 or the FCOA Evaluator 115.

APPENDIX E

Deprecation Analysis

Select embodiments of the present invention produce sets of FCOA's for a commander's consideration. The Commander's FCOA Decision 122 is the point within the Military Decision Making Process (MDMP) where the commander selects one FCOA 123 from the FCOA Candidates 111 for implementation as the unit's Operation Plan (OPlan). Elements of select embodiments of the present invention provide three tools to assist a commander in determining the best FCOA: a Risk Deprecation Analysis 120, an Evaluation Deprecation Analysis 121, and a Pareto Analysis (not shown in FIG. 1 but referenced as a “button” 1601 in FIG. 16).

The FCOA Evaluator 115 fires the evaluation protocols established by a user in the Desired End State 114 to acquire an objective score of a selected FCOA's relative merit against a selected ECOA. To fully evaluate an FCOA, the FCOA Evaluator 115 directs a full evaluation as illustrated in FIG. 11. The feedback arrow 1101 shows how the FCOA Evaluator 115 initiates a simulation between an evaluated FCOA and each of the ECOA's in the ECOA IPB Set 110. This more comprehensive evaluation of the FCOA results in a cumulative evaluation score for the evaluated FCOA.

Consider a situation where there are two FCOA's being evaluated against an IPB set of three ECOA's, both FCOA's having a total evaluation score of 200. At first glance, these FCOA's seem equivalent but upon further examination of this surprisingly typical case the first FCOA, “FCOA-Home-Run” achieves a cumulative result by scoring 100 normalized points against ECOA-1, 95 points against ECOA-2, and only five points against ECOA-3. In contrast, the other FCOA option, “FCOA-Base-On-Balls,” achieves a cumulative result by scoring a more even distribution of 66, 67, and 67 points against the respective ECOA's. Clearly the two FCOA's are not equivalent in spite of having the same cumulative score of 200. FCOA-HR does extremely well against the first two enemy options but is annihilated by the third. FCOA-BB does medium well against the entire set of ECOA's.

The cumulative score hides this clear distinction between the two FCOA's, a distinction that a commander would want to consider before making a decision, since the HR option provides high-risk for high-gain, whereas the BB option provides low risk for medium gain. Before making a decision, a typical commander would discuss with his intelligence officer the probability that the enemy would employ its third option (ECOA-3), and the likelihood that re-directed reconnaissance assets could verify or deny enemy employment of that option in a timely enough manner to switch game plans (from HR to BB). Unfortunately, the commander and intelligence officer might not think to have this discussion if they only consider the false equivalent cumulative score of 200 for both FCOA's. The Risk Deprecation Analysis tool 120 provides the commander the analysis required to discover these false equivalencies and to quickly understand the dynamic relationships between each FCOA and each of the ECOA's in the IPB set.

FIG. 14 shows the results of a Risk Deprecation Analysis 120 in cooperation with other elements of select embodiments of the present invention. The Terrain Informed War Game Model 112 has conducted simulations between every FCOA candidate 111 and every ECOA in the IPB set. In this case there are 420 FCOAs in consideration, and three ECOA's (named “Strong Right,” “Strong Left,” and “Balanced”). Thus, there have been 1260 simulations and evaluations (420×3). FIG. 14 shows the details of some of the 1260 evaluations but these evaluation details are somewhat counter-intuitive and require explanation.

The first column gives the FCOA's name, all of which are “computer nominated” (CN) in this example. Since there are three ECOA's in the IPB set, each FCOA undergoes deprecation analysis three times, thus each FCOA is listed in three rows. The second column 1401 lists the name of the deprecated ECOA for that line's analysis. In the example of the first row the Strong Right ECOA has been deprecated, meaning that row shows the cumulative results of FCOA-CN-476 against the other two ECOA's, but not the deprecated ECOA-Strong Right. The third column 1402 shows the original, non-deprecated ranking of that FCOA, which in the case of the first row is 7^th out of 420. The fourth column 1402 shows the new, deprecated ranking of the FCOA, which in the case of the first row is now 30^th out of 420. In other words, when ECOA-Strong Right is thrown out of the IPB set, FCOA-CN-476 does not do as well as it did with the non-deprecated ECOA. The fifth column 1404 shows a change of ranking of −23, meaning the FCOA (CN-476) dropped from 7^th to 30^th. The tactical meaning of this analysis of the first row is that CN-476 does extremely well against ECOA-Strong Right, but not so well against the other two ECOA's. Columns 1405 and 1406 show the Deprecated Roulette Wheel and Change in Roulette Wheel rankings used to calculate the ordinal rankings in the previous columns.

In select embodiments of the present invention, a user does not need to study each of the 1420 lines in this table. When a user clicks on the column headings 1401 through 1406, the table re-orders itself in either ascending or descending order by that column's value. In other words, by clicking on the Change in Rank (CR) 1404 heading, the deprecated FCOA that benefited the most by dropping out an ECOA from evaluation will now be at the top of the table. If a user clicks again on the CR 1404 heading, the table re-orders itself in descending CR value, meaning the deprecated FCOA that was hurt the most by a missing ECOA is now at the top. When a user employs this re-ordering tool along with the greater-than and less-than highlighting tool 1407 at the bottom of the window, FCOA's with “interesting” sensitivities that are sensitive to specific ECOA's are quickly located. In limited performance testing of select embodiments of the present invention experienced users found extremely interesting FCOA-ECOA dynamics in less than one minute. This provides for a more informed command decision on which FCOA is appropriate for a mission.

The Evaluation Criteria Deprecation Analysis 121 works in a similar manner, but instead of deprecating ECOA's from the total evaluation, the Evaluation Criteria themselves are deprecated, one at a time. FIG. 12 shows an evaluation matrix for a typical FCOA, where the ECOA's are listed in the middle columns 1202, and the evaluation criteria are listed in the middle rows. The Risk Deprecation analysis 120 re-computes that matrix by dropping the middle columns 1202 (ECOA's) one at a time, whereas the Evaluation Criteria analysis 121 re-computes that matrix by dropping the middle rows (Evaluation Criteria) one at a time. The Risk Deprecation Analysis 120 quickly gives a user an idea of each FCOA's sensitivity to each ECOA, whereas the Evaluation Criteria Deprecation Analysis 121 quickly gives a user an idea of each FCOA's sensitivity to each Evaluation Criteria. For example, an Evaluation Criteria Deprecation Analysis 121 of FCOA-1 may show that FCOA-1 is unusually sensitive to Maximize Atk Strength at sub-MC 773, and that FCOA-1 would be an extraordinary solution if it were not for that one Evaluation Criterion. The commander may decide to trade off that Evaluation Criterion for superior expected performance in the other criteria. However, most commanders would want to consider that option, if it were available quickly and simply, which is the result of employing select embodiments of the present invention.

A third FCOA analysis tool, not displayed in FIG. 1 but inferred in FIG. 16 at 1601, is Pareto Analysis, a more “formal” analysis of the trade offs involved in FCOA evaluations. Consider the comparatively simple example of buying a car. In advance a buyer might state that she prefers quality over price and is willing to pay extra for a superior product. However, there might be a car that independent consumer agencies rank as 99% as good as another car, but at only half the cost. If a salesman (or a software cognitive amplification agent) consistently applies her a priori statement about preferring quality over price, then he (or it) would not present that second car as an option. But, common sense suggests that the buyer would certainly want to know about such an intriguing option.

A Pareto Analysis examines a set of solutions (cars, in the above example) by a relative comparison of each evaluation criteria (price and quality, in the above example), and eliminates Pareto-dominated solutions from consideration, in favor of Pareto-dominating solutions. In the car example, a third car might be better than the second car in both price and quality, in which case the second car is completely dropped from consideration since a rational consumer would consider the product in every evaluation criteria.

In this trivial example, a hundred cars could be Pareto-analyzed to find the two cars that together Pareto-dominate the other 98 but do not Pareto-dominate each other. One car is superior to the other in price and the other is superior in quality. The simplification of the large set of 100 cars to the small set of two cars is called the Pareto-optimal front, enabling a consumer to make a much simpler decision in confidence that she is getting one of the best possible cars out of 100 models, given her personal preferences.

Select embodiments of the present invention implement a Pareto-Analysis in a manner similar to the car example described above, but an increased number of evaluation criteria significantly increases the size of the Pareto optimal front. In limited performance testing of select embodiments of the present invention, an initial FCOA set of 420 solutions eliminates about 150 Pareto-dominated FCOA's from consideration, leaving about 270 remaining FCOA solutions on the Pareto-optimal front. Select embodiments of the present invention extend this multi-dimensional Pareto analysis (one dimension for each evaluation criteria). This Pareto functionality enables a user to determine the Pareto displacement of any Pareto dimension (evaluation functionality) between any two FCOA's on the Pareto optimal front.

Although this pair wise evaluation technique is difficult to use in a comprehensive manner against a large set of FCOA's, it is excellent for understanding the evaluation trade offs between any two FCOA's from a small set. In other words, after using the simple deprecation analysis tools to reduce the FCOA's in consideration from several hundred to just a handful, a user may employ the Pareto analysis tool to find interesting trade offs between FCOA's with respect to evaluation criteria. As an example, a user might discover that between two “finalist” FCOA's, one of the FCOA's is massively better in three evaluation criteria, whereas the second finalist FCOA is only moderately better in the remaining five evaluation criteria.

The net effect of these three tools enables users to quickly gain a sophisticated understanding of the relative advantages and disadvantages of a large number of FCOA's with respect to a large number of evaluation criteria and a large number of IPB ECOA's.

APPENDIX F

Glossary

• AA: Avenue of Approach. An IPB product that identifies a potential Axis of Advance. Often confused with an Axis of Advance, since the acronym is the same (AA).
• AA: Axis of Advance. An offensive control measure used for an attack, often confused with the IPB Product Avenue of Approach, since the acronym is the same (AA).
• AI: Artificial Intelligence. A field of computer science that provides a set of techniques to automate reasoning and inference at the knowledge-level (in the data-information-knowledge-wisdom spectrum). BOM uses AI techniques to cognitively amplify (or multiply) human-thought, rather than replace humans. It defers wisdom-level decisions to a human user.
• Algorithm: A term used in Mathematics and Computer Science for a rigorous set of procedures which can be analyzed for speed and memory performance. Algorithmic (Asymptotic) analysis is highly useful for understanding the trade-off in high-resolution v. low-resolution (abstract) modeling.
• AKA: Also known as.
• Animation: A set of tactical overlays, arranged in chronological order, portraying the movement of units across a map to visualize a battle or operation.
• BAE: BTRA-BC Analysis Engine. The human-computer application that will quickly analyze large numbers of battlefield reports to appropriately answer commander's PIR's in NRT. The objective is to significantly reduce information overload for modern command posts.
• BBE: BTRA-BC Battle Engine. The human-computer application that will generate and analyze Friendly COA's during MDMP to accelerate and enhance a commander's decision. The objective is to accelerate the MDMP (OODA) process so that the ⅓rd-⅔rds rule for mission-time allocated to staff planning becomes the ⅕^th-⅘^th's rule.
• BBN: Bayesian Belief Net. An AI technique that uses conditional probabilities (using the theorem of Reverend Thomas Bayes) to propagate inferences. BBN's are highly useful for METT-T contextual analysis (evidential reasoning). The University of Illinois used a BBN as the inference engine for RA VEN and CoRA VEN, the precursor projects to the BAE.
• BML: Battlefield Management Language. A data specification designed to speed the automated information exchange of military Operations Orders. This specification is particularly useful in communicating knowledge-level tasks using a 5W (Who, What, When, Where, Why) format. This is the precursor to the GeoBML data specification that will be integrated with the BOM.
• BOM: BBE Object Model. A set of Java class specifications that will support both the BAE and BBE applications.
• BRP: BBE Review Panel. A group of experienced military SME's who review and direct BOM development from a user perspective, particularly on the issue of trading off high-resolution accuracy in domain modeling against low-resolution (abstract) application speed.
• BTRA-BC: Battlespace Terrain Reasoning Awareness—Battle Command. A four-year R&D program administered by the U.S. Army Corps of Engineers Topographic Engineering Center (TEC). The BTRA-BC program sponsors BBE development to demonstrate the power of leveraging its automated terrain analysis applications (BTRA-Classic Applications).
• BTRA-Classic Applications: A set of terrain analysis applications developed in the precursor (BTRA) program to BTRA-BC that serves as the fundamental source for BOM knowledge objects.
• BTRA-BC continues to refine these applications.
• C41: Command, Control, Communications, Computers, and Intelligence.
• Clock Tick: The unit of time a user sets within the BBE application to model the time elapsed between simulation updates of maneuver and combat resolution. AKA Time Slice when identifying a particular one or range.
• CM: Collection Management. The set of doctrinal procedures employed by a staff to direct intelligence collection operations, with the objective of quickly answering the commander's PIR's. See FM 34-2, “Collection Management and Intelligence Synchronization.”
• COA: Course of Action. An option for unit employment a staff develops and analyzes as part of the MDMP. A staff typically develops multiple COA's for a commander's consideration, in making a formal decision as to which COA will best accomplish the mission. The command decision is the point within the MDMP where the art of war is formally considered and risk is calculated. The BBE will use the more straightforward discipline of military science to generate and analyze COA's for the commander's evaluation and judgment.
• COA Concept: A coarse description of the basic movements of major subordinate units, and other major employment considerations, for a considered COA. A staff typically analyzes just the COA concepts for the commander's decision. After the decision the staff refines the COA concept into a full COA, described in an OPORD or OPLAN. AKA Cartoon Sketch, Major Muscle Movements.
• Cognitive Amplifier: The implementation of AI techniques employed in the BOM in which a goal is to have a computer conduct the same knowledge processing that a human expert would have accomplished, given time. This enables a human to process much greater amounts of knowledge in a given amount of time. Or, to process the same amount of knowledge much faster than without the cognitive amplifier. AKA Cognitive Multiplier.
• Combat Multiplier: A technique the UCM uses to appropriately integrate terrain, weather, and other METT-T considerations into the combat resolution calculations for attrition and battle duration. For example, a mobility corridor that is particularly favorable for an attacker might merit a strong combat multiplier of 1.2. The UCM would then multiply the raw combat power of the attacking unit by 1.2, making that unit 20% stronger than it otherwise would be, at least while it is attacking within that mobility corridor. The Combat Multiplier concept enables the BOM to efficiently and appropriately reward units that intelligently leverage terrain.
• Combination: The mathematical analysis of the number of ways to choose a number of elements out of a group. Along with permutations, this analysis is instrumental in identifying how to allocate boundaries within an Area of Operation (AO), and how to task organize subordinates. It is important for SME's to understand this general idea when considering trade offs between BBE speed and accuracy. AKA Combinatorics.
• Commander's Intent: A commander's criteria for a successful unit mission It is to be used by subordinate commanders and leaders when executing the mission, particularly when confronted with an unanticipated situation while out of communications with the commander. The BBE and BOM will simplify the Commander's Intent to Desired End State which will then be used as the evaluation criteria the BBE uses to assess considered FCOA's.
• Convergence: The point in the run of a genetic algorithm (GA) when the returned solutions are only marginally better than the already found solutions. On a graph with time on the abscissa and solution quality on the ordinate, this is the point where the uphill turns into a plateau. This point is widely interpreted to mean that continued running of the GA will net little to no improvement in solution quality (although some problems can have false convergence).
• COO: Combined Obstacles Overlay. An abstract analytic and display method that depicts a set of terrain features around which military forces maneuver since they can not practically maneuver through the obstacle. The area between two obstacles defines a mobility corridor around the obstacles and the entire set of obstacles forms an initial game board for maneuver battle analysis. This initial game board is further analyzed to identify AA's and LDT's in order to develop the MCOO, which is the final game board.
• COP: Common Operational Picture. A tactical overlay and narrative that portrays a commander's and staff's current understanding of friendly and enemy forces relative to the terrain. A staff typically goes to great lengths to ensure the entire unit can simultaneously see the current COP. CoRAVEN: Collaborative RAVEN. A precursor to the BAE, developed at the University of Illinois at Urbana-Champaign in the late 1990's. CoRAVEN demonstrated the automated NRT analysis of battlefield messages, with corresponding answers to PIR's to a specific METT-T battle at the National Training Center. The intelligence conclusions developed by CoRAVEN matched the conclusions of an experienced Army military intelligence O\officer. Unfortunately, the developers were unable to generalize CoRA VEN to handle any and all METT-T battles, since the amount of time to develop the underlying conditional probabilities was highly prohibitive (hundreds of man-hours for a 4-hour battle). In the BOM approach, software quickly generates these conditional probabilities based upon an analysis by a post-Command-decision BBE analysis. Sometimes referred to as RA VEN, an earlier version of the same project.
• Downstream: A process or a product developed later in the MDMP process.
• DP: Decision Point. A staff-planned point in time and/or space of a COA in which a decision needs to be made about further unit employment (e.g., commit the reserve). These points are important within BOM since they typically drive PIR's and other collection requirements which in turn form the knowledge-substrate for downstream analysis of battlefield reports.
• Desired End State: An important element of a Commander's Intent in which a commander identifies where friendly and enemy units should be located and in what strength at the end of an engagement or operation. The BBE uses Desired End State as a reasonable proxy for the full commander's intent.
• ECOA: Enemy Course of Action. A COA option available to an enemy commander, typically developed by the S2/G2 (Intelligence Staff) to support analysis of FCOA's during the MDMP. It is important that the S2/G2 develop a representative set of ECOA's, reflecting all major tactical options available to an enemy commander for input into the BBE analysis so that a friendly commander considers risks associated with each BBE-analyzed FCOA. For example, if the S2 fails to include a defense-in-depth ECOA as input to the BBE, then all returned FCOA's may be vulnerable to that ECOA.
• Evidence Tree: The data structure, in the shape of an upside down tree or bush which the BOM uses to format collection requirements. The PIR will be at the top (root) node, and the SIR's and NAI's will be at the bottom (leaf) nodes. This data format enables the BAE to quickly and contextually relate battlefield activities to relevant PIR's, ECOA's, DP's, and the like.
• FCOA: Friendly Course of Action. A COA option available to a friendly commander, as opposed to an ECOA. Staff planners typically refer to FCOA's as COA's and use only the term ECOA for differentiation of enemy COA's.
• FOX: A precursor to the BBE, developed at the University of Illinois at Urbana-Champaign in the late 1990's. The FOX application generates and analyzes tactical ground maneuver offensive courses of action, at an abstract (coarse grade) resolution through the use of a fast, abstract war game simulation and a GA.
• GA: Genetic Algorithm. An AI technique that mimics natural selection and breeding to generate new solutions using existing solutions. GA's are highly useful for METT-T contextual planning since they are directly analogous to the time honored staff planning technique of, “Can we develop a COA-3 that combines the best traits of COA-1 and COA-2?” The University of Illinois used a GA as COA-development engine for FOX.
• Gene: The portion of a GA's bit string that maps to a COA variable. A GA exchanges, combines, and mutates genes in order to develop better solutions from existing solutions.
• GeoBML: Geographic Battle Management Language. A geo-rectified update to an existing BML data specification.
• GUI: Graphic User Interface. The interactive screen display used to direct a computer application.
• Heuristics: Domain knowledge, typically acquired from a SME as a conditional rule of thumb, used to guide a computer's reasoning process.
• Illinois Architecture: A conceptual, terrain-centric framework used to guide the development of basic objects and algorithms used by FOX and CoRA VEN/RA VEN. The BOM employs this same basic framework, slightly modified due to lessons learned from earlier applications. The BOM framework will also evolve the Illinois Architecture to integrate the sophisticated terrain analysis products the BTRA-Classic applications produce.
• Information Overload: A term used to describe a frustrating phenomenon in modern command posts in which a significantly larger number of battlefield reports come than can be analyzed by the staff, even with automated tools. The FCS requirement is for a brigade intelligence staff (six personnel, six workstations) analyzing 170,000 messages per hour to produce a COP in NRT. BAE directly addresses this requirement.
• Intelligence Fusion: A term used to describe intelligence analysis of large numbers of battlefield reports about enemy activities in an attempt to solve information overload. The BOM approach assumes fusion level-0 (on-sensor signal processing) and level-1 (correlation of entities) have been accomplished prior to input into the BAE evidential reasoning application. The BAE application simultaneously analyzes fusion level-2 through level-4 in a holistic analysis.
• IPB: Intelligence Preparation of the Battlefield. The doctrine and TTP described in FM 34-130 used by a staff to develop appropriate ECOA's to support other MDMP processes such as FCOA analysis, collection management, and intelligence analysis.
• IR: Information Requirement. An executive level question about enemy intentions or activities on the battlefield. According to Appendix A of FM 34-8, Combat Commander's Handbook for Intelligence, an IR should be linked one-for-one with a DP. An IR differs from a PIR only in that it lacks command priority.
• K-Partition: Kuchinski Partition. A set of the maximum number of parallel maneuver lanes from a start line (e.g., LD/LC) to a finish line (e.g., LOA) that can fit within an area of operation. This partition is produced from a BTRA-Classic application developed by Adam Kuchinski. It forms the maneuver substrate on the BBE game board for IPB Avenues of approach. Using IPB terminology, the K-Partition is an interim product between the COO and the MCOO. AKA K-Lane, Physical Lane.
• LDT: Line of Defensible Terrain. This is a set of obstacles, typically perpendicular to offensive AA's, around which a defending force can form a coherent defense. LDT's are the defensive counterpart to the Avenue of Approach for offensive COA's, and both are identified on the MCOO.
• MC: Mobility Corridor. The terrain between two obstacles through which a military force can practically maneuver. MC's are the fundamental building block for MCOO-based Maneuver COA's. The offensive force tries to link MC's together from LD/LC to LOA, whereas the defensive force tries to link a coherent chain of MC's together from left boundary to right boundary. Obstacles and MC's are a basic product from BTRA-Classic applications, and are increasingly used as a spatial index to many more terrain analysis products.
• MCOO: Modified Combined Obstacle Overlay. The set of LDT's and AA's that form the game board substrate for defensive and offensive maneuver options (COA's). Explained in great detail in the IPB manual (FM 34-130).
• MDMP: Military Decision Making Process. The Army doctrine and TTP a commander and his staff use to assess the METT-T situation and develop an appropriate OPLAN or OPORD, directing unit employment. The BOM architecture assumes that users will employ the BBE and BAE tools during the regular conduct of the MDMP.
• METT-T: Mission, Enemy, Terrain, Troops, and Time. Military acronym typically used to describe context by naming the major situational considerations for battle.
• NAI: Named Area of Interest. A control measure used to direct intelligence collection activities. The presence or absence of predicted enemy activities within an NAI typically confirms or denies an ECOA, or important elements of an ECOA. An NAI is highly associated with indicators (general descriptions of enemy activities that can cue analysis) and SIR's (typically the specific description of enemy activity expected in an NAI).
• NRT: Near Real Time. The characterization of a system or a process that develops an output product almost immediately after the input. The ultimate objective of intelligence fusion applications, like the BAE, is to analyze large numbers of multi-source battlefield reports to produce a COP in NRT.
• Niching: A GA technique used to ensure the set of returned solutions are not all minor variations of the same, best-found COA. This is directly analogous to the MDMP effort to find distinct COA's.
• OODA Loop: Observe, Orient, Decide, Act. A concept that echoes the MDMP process, but directly implies a continual re-initiation of a unit's mission cycle in response to a fluid, evolving situation. The concept was developed in the 1980's by Colonel (Ret.) John Boyd, USAF who wrote the landmark aerial fighter tactics manual after a tour as “40-second Boyd” at Nellis AFB in the late 1950's. He developed an energy maneuverability theory that revolutionized aircraft design in the late 1960's and drove the design of the F-15 and F-16 fighter aircraft in the 1970's. Boyd concluded that the force with the fastest OODA Loop had a decisive advantage since it could “turn inside” its opponents' OODA Loop (decision cycle). In a nutshell, the BOM follows the letter of AirLand Battle theory, particularly in the area of synchronization, while pursuing the OODA spirit of ultra-fast decisions and mission cycles.
• OPORD: Operation Order. A formatted directive that a commander issues to his subordinates describing the actions and tasks required to execute the selected COA.
• OPLAN: Operation Plan. Same format as an OPORD, but an OPLAN is not a tasking to subordinates until a Fragmentary Order (FRAG) is issued to execute the OPLAN.
• P-Lane: Pascal Lane. The BOM term used to describe groupings of (physical) Kuchinski Lanes (K-lanes) that in turn identify the location of boundaries between subordinate units. The selection of a set of P-Lanes is the same as selecting a set of boundaries between subordinates and is a major COA variable for both offense and defense.
• Pareto Analysis: The academic term for analyzing trade offs. For example, in a particular METT-T situation a small compromise in achieving a terrain objective might result in a large payoff in the destruction of enemy forces and preservation of friendly forces. AKA Trade Off Analysis.
• Pareto-Optimal Front: The academic term for the set of COA's that cannot be improved without trading one pay off for another. For example, COA-1 might be better than COA-99 in all evaluation criteria, which would eliminate COA-99 from the Pareto optimal front. If no COA can be found that bests COA-1 in all evaluation criteria, then it becomes part of the Paretofront.
• Pascal Transformation: The BOM process that uses Pascal's Triangle (Binomial Theorem) to transform Kuchinski Lanes into Pascal Lanes (implying subordinate unit boundaries that may have multiple K-Lanes).
• Permutation: The mathematical analysis of the number of ordered ways to choose a number of elements out of a group. Along with combinations, this analysis is instrumental in identifying how to allocate boundaries within an AO, and how to task organize subordinates. It is important for SME's to understand this general idea when considering the trade offs between BBE speed and accuracy.
• PIR: Priority Intelligence Requirement. An executive level, command-priority question about enemy intentions or activities on the battlefield. According to Appendix A of FM 34-8, Combat Commander's Handbook for Intelligence, a PIR should be linked one-for-one with a DP. A PIR differs from an IR only in that it has command priority.
• RAVEN: A pre-cursor to BAE developed at the University of Illinois. See CoRA VEN.
• Sheherazade: A FOX-like, fast-abstract war gaming engine developed by the Battle Command Battle Lab (Huachuca), Army Research Lab, Electronic Warfare Associates, and the University of Arizona. Sheherazade was designed to support analysis of options in asymmetric operations where demographic analysis is more important than terrain analysis.
• SIR: Specific Information Requirement. A specific question about enemy activities, typically associated with a PIR or IR in a many-to-one relationship (one PIR or IR will have many SIR's), and associated with an NAI in a one-to-one relationship. While a PIR (or IR) focuses an entire set of collection taskings, an SIR typically focuses a single collection asset (although multiple collection assets can work the same SIR). Also, a PIR/IR may not be directly observable from one collection asset (e.g., will the enemy Army Group place its main effort to the East or the West of the river?), whereas an SIR can typically be answered by a single, well placed (and well timed) collection mission (e.g., will road-intersection #5 be defended by more than a platoon?).
• SME: Subject Matter Expert. A person highly skilled and experienced within a certain domain of knowledge. For the BOM, this domain is military staff planning and analysis.
• TTP: Tactics, Techniques, and Procedures. Army Field Manuals contain both Doctrine and TTP. U.S. Army Doctrine is descriptive and not prescriptive, i.e., it allows soldiers considerable latitude in how they proceed in their tasks depending upon the contextual METT-T situation. TTPs are common recommendations and ideas on how to accomplish doctrinal tasks.
• UCM: Underlying Combat Model. The model the BOM uses to calculate attrition and duration of engagement between opposing units within a single MC.
• Upstream: A process or product developed earlier in the MDMP process.
• War Game: The procedure a staff uses, as part of the MDMP process, to analyze an FCOA through simulating battle with a selected ECOA. According to FM 34-8, Combat Commander's Handbook for Intelligence, a staff is supposed to war game each considered FCOA against (at least) the most likely and most dangerous ECOA's.
• Yellow Sticky Drill: A war gaming technique named after the once common practice of using yellow sticky notes to represent friendly and enemy forces on an acetate map overlay (typically the MCOO)(or a Butcher Board sketch of the MCOO). A staff planner from the operations shop would move the “blue” yellow stickies to simulate the maneuver of friendly subordinate units, while a planner from the intelligence section would move the “red” yellow stickies to simulate enemy maneuver. The staff typically consulted Ft Leavenworth Student Texts (ST 100-3 and ST 100-9) to determine unit attrition. The BBE automates this process, and replaces the FLKS Student Texts with the more sophisticated, terrain aware UCM. AKA Butcher Board Drill.

Claims

1. A system employing cognitive amplification allowing planners to efficiently conduct Intelligence Preparation of the Battlefield (IPB) and the Military Decision Making Process (MDMP), said system enabling reasoning within at least one context and facilitating decision making in time constrained scenarios, comprising:

at least one specially programmed computer;

computer readable media in operable communication with said computer; and

a Battlefield Terrain Reasoning Awareness, Battle Command (BTRA-BC) Battle Engine (BBE) contained on said computer readable media for processing on said computer,

wherein said computer compares a multitude of variables that comprise Courses of Action (COAs) including at least Friendly Courses of Action (FCOAs) and at least Enemy Courses of Action (ECOAs), and

wherein said system melds military science with military art needed for relevant and timely decision making, and

wherein said system significantly reduces the time said planners require for battle planning by cognitively amplifying the ability of planners to conduct said IPB and said MDMP, and wherein a human-computer reasoning team employing said system develops and analyzes said COAs much faster than humans alone and better than said computer alone, and

wherein said system permits a user to expend intellectual energy considering the effect of said variables rather than trying to identify all said variables.

2. A process facilitating timely and efficient mission planning in context, comprising:

providing fast-abstract war gaming scenarios;

providing realistic estimates of combat effects of terrain for said war gaming scenarios;

providing realistic combat attrition estimates for said scenarios;

providing comprehensive integration of MDMP and IPB doctrinal processes in said scenarios; and

providing computer reasoning in harmony with, and at the direction of, human users to facilitate evaluation and comparison of various said scenarios.

3. A semi-automated method, steps in said method closely adhering to cognitive processes used in exploiting intelligence and in decision making, comprising:

providing at least one specially programmed computer and computer readable media;

on said at least one computer, performing a network-pulse analysis of mobility corridors to yield an Articulated Modified Combined Obstacle Overlay (MCOO) as a set of virtual lanes (V-lanes) on a Game Board establishing a network of mobility corridors from a start line to an objective line,

wherein said analysis offers approximately all the information of a doctrinal said MCOO while providing information about battlefield physics;

providing a Mission, Enemy, Terrain, Troops, and Time (METT-T) Parser loaded on said computer readable media,

wherein said METT-T Parser examines battlefield physics of inputs thereto, producing at least an Enemy Course of Action (ECOA) Variable set and a Friendly Course of Action (FCOA) variable set, and

wherein said METT-T Parser establishes sets of all possible instances for each of said ECOA and said FCOA Variables;

providing to said METT-T Parser first data from an MCOO data base developed from a software application termed MCOO-Maker, said first data provided together with a Braswell Index establishing a logical partition of an area of operation (AO);

wherein articulated detail of said Articulated MCOO aid a computer to explicitly reason through issues that human experts implicitly understand, and wherein said Articulated MCOO establishes maneuver options for units by identifying obstacles to movement, said mobility corridors between said obstacles, and logical groupings of said mobility corridors, and

wherein said Articulated MCOO used together with said Braswell Index establishes a game board upon which an automated planner develops attrition estimates for an engagement established via at least one pre-specified Friendly Course of Action (FCOA) and at least one pre-specified Enemy Course of Action (ECOA), and

providing second data to said (METT-T) Parser from at least one Enemy Order of Battle (EOB) data base,

wherein said EOB is a representation of equipment quantity and type that may be displayed as Game Pieces on said Game Board;

providing third data to said (METT-T) Parser from at least one Friendly Order of Battle (FOB) data base;

wherein said FOB data base provides a set of friendly said game pieces for use with said game board and said EOB game pieces,

providing fourth data to said (METT-T) Parser from at least one Missions and Postures data base,

wherein said Mission and Posture inputs set the beginning Game State, and wherein said Mission and Posture inputs provide game context to said METT-T, and

wherein said user assigns ratings to both said FOB and said EOB sets comprising: Unit Strength ratings,

wherein said Unit Strength ratings at least reflect attrition from previous combat operations; Unit Posture ratings,

wherein said Unit Posture ratings are provided from a set of well-established tasks; and Unit Morale ratings,

wherein said Unit Morale ratings supplement said Terrain Informed War Game Model with effects due to training, fatigue, leadership, psychology, moral issues, and combinations thereof; and

wherein said Braswell Index provides an abstracted index of terrain effects that enables said METT-T Parser to load a realistic representation of said game board into RAM, and

wherein METT-T Battle Context Mapping employs a human-computer set of procedures enabling exploration of a game-theoretic dynamic of a pending engagement consistent with said military MDMP and IPB doctrine, and

wherein said METT-T Parser and associated said FCOA and ECOA Variable Sets provide a Terrain Informed Articulation of major elements of an abstracted concept decision, and

wherein said METT-T Parser develops possible battlefield physics instances for each said ECOA and each said FCOA Variable, and arranges sets of instances to reasonably maximize neighborliness describing the correlation between any two adjacent instances of said FCOA and said ECOA Variables and their contributions to the final evaluation score of a solution when all other variables are controlled, and

wherein said process facilitates FCOA optimization through a genetic algorithm;

providing a Terrain Informed War Game Model on said computer readable media, said War Game Model employing an attrition model to determine likely results of combat;

wherein an attrition calculation based on said attrition model employs estimates of relative combat power of opposing forces, and

wherein said War Game Model employs the Quantitative Judgment Method of Analysis (QJAM), that incorporates an historical basis for assessing relative power of individual weapons;

receiving as input to said War Game Model relative weapons estimates from a BTRA-BC Battle Engine Weapons Assessment and Calculation Tool (B-WACT),

wherein said B-WACT implements said QJMA concept to develop relative combat power for individual weapons as well as weapon systems that aggregate said weapons, and

wherein a user may provide characteristics of a weapon to said B-WACT and said B-WACT provides a QJMA relative combat power for said weapon, and

wherein said B-WACT enables said user to aggregate weapons into weapon systems that also receive a QJMA relative combat power, and

wherein said B-WACT publishes lists of weapon systems as a data file for use as a possible input;

wherein said user employs the same process described for said Enemy OB, except that said QJMA relative combat power is calculated for friendly units by aggregating estimates from said B-WACT of relative combat power for weapons within said friendly units comprising said FOB;

inputting Superiority Toggle ratings to said Terrain Informed War Game Model, said ratings comprising: ratings for Intelligence, Surveillance, and Reconnaissance; ratings for Command and Control; and ratings for Air Superiority; and

Game Time Slice;

wherein said Game Time Slice supplements said Terrain Informed War Game Model model allowing said user to adjust temporal resolution;

inputting to said method 5th data comprising criteria for a Desired End State, wherein said 5th data facilitates an articulated, user-adjustable multi-criteria process to evaluate said FCOAs;

inputting to said method 6h data comprising said user's ECOA IPB set; wherein said ECOA and said FCOA variable sets enable user selection of at least one FCOA and at least one ECOA.

providing a Graphic User Interface (GUI) for visualization, displaying selection of at least said ECOAs,

wherein a pull-down menu is displayed for each of said ECOA Variables, enabling selection of variable instances desired in constructing and displaying each said ECOA with an associated set of variable instance selections;

selecting a variable instance for each of said variables in said FCOA variable set to assert a said FCOA by: analyzing a said set of candidate FCOAs by comparing selected said FCOAs to selected said ECOAs in said War Game Model; deciding which tactics to employ;

choosing options from a said FCOA Variable set to establish FCOAs to form an FCOA candidate set;

conducting a risk analysis for each said FCOA in said FCOA candidate set;

identifying possible battlefield physics options via said METT-T Parser for each said ECOA and each said FCOA variable set;

wherein dominate variables are defined as those upon which others may depend and dominated variables as those that may depend on other variables;

analyzing the effect of reasonable tactical dynamics a user may employ; and

estimating a logical, representative set of options for said ECOAs against which a battle analysis is conducted for each said FCOA in a said FCOA candidate set;

producing a set of defensive said ECOAs that become said ECOA IPB set, wherein said user selects a set of offensive said FCOAs, rather than a single said FCOA for possible execution;

selecting a representative said ECOA IPB set and a said FCOA set for a specified engagement,

wherein said ECOA IPB set is stabilized for much of a game-theoretic analysis, thus avoiding a co-evolutionary paradigm in which a late-generation said FCOA may be vulnerable to an early-generation said ECOA; and

implementing an FCOA Evaluator that estimates an end state of a submitted said FCOA for each said ECOA in said ECOA IPB set,

wherein said user retains an ability to modify said ECOA IPB set until the start of a systematic evaluation of said FCOAs, at which time said ECOA IPB set is locked, and

wherein if said user later adjusts said IPB ECOA set, all relevant changes to said FCOAs are re-submitted to said FCOA Evaluator to insure a corresponding updated said evaluation, and

wherein standardization of an evaluation metric is guaranteed by locking in said IPB ECOA set as well as evaluation criteria for said Desired End State with a process termed FCOA Optimization Employing a Genetic Algorithm (GA), and

wherein locking in said IPB ECOA set insures that all said FCOAs considered by said GA use the same evaluation metric;

standardizing said evaluation metric by locking in said IPB ECOA set and said evaluation criteria for said Desired End State via said FCOA Optimization Employing a Genetic Algorithm (GA),

wherein said locking in of said IPB ECOA set insures that all said FCOAs considered by said GA use the same evaluation metric;

performing an automated secondary analysis termed an FCOA Vulnerability Analysis that employs steps of said method recursively similar to a Reverse IPB Analysis, to establish those said ECOAs that are optimized against a selected said FCOA,

wherein said FCOA Vulnerability Analysis outputs a set of Most Dangerous ECOAs with associated scripts;

establishing a first said ECOA IPB set;

employing said FCOA Vulnerability Analysis to yield a second said ECOA set;

comparing said first and second ECOA sets;

re-initiating said step if said FCOA Vulnerability Analysis identifies a, said ECOA or said ECOA set different from said ECOA IPB set;

adding said newly identified ECOA or ECOA set to original said ECOA IPB set;

employing Terrain Informed War Game Model to provide Game Rules for said Game Board established in said Articulated MCOO and said Braswell Index inputs, and Game Pieces established in said Enemy and said Friendly OBs,

wherein said user, or an automated process working on behalf of said user, selects one said ECOA from said ECOA IPB set and one said FCOA from said FCOA Candidates set;

employing said Terrain Informed War Game Model to conduct a simulation of combat for selected said ECOAs and said FCOAs and outputting a time-phased estimate of location and strength of selected said subordinate units during said engagement,

wherein said simulation is fast because said Game Board, said Game Pieces, said Game Strategies, and said Game Rules are abstracted to facilitate fast calculations in said RAM;

employing said Terrain Informed War Game Model to output time-phased snapshots of disposition and strength of selected said units,

wherein abstractions of all said Game Pieces are crafted to retain only that information pertinent to aggregate both attrition and maneuver posture of said selected units;

employing repeated, game-theoretic submissions of said FCOAs and said ECOAs to said Terrain Informed War Game Model directed by strategy of said FCOA Optimization Employing a Genetic Algorithm,

wherein said submissions facilitate development of an emergent intelligence on appropriate candidate tactics to use in a situation specified by said METT-T Parser;

implementing said FCOA Evaluator to direct said Terrain Informed War Game Model to engage to-be-evaluated said FCOAs iteratively from said FCOA Candidate set against said ECOA IPB set,

comparing said Desired End State against a final said snapshot produced by said Terrain Informed War Game Model during each said iteration; and,

employing a set of unique protocols and weights assigned to specified criteria for

said Desired End State to yield a numeric score for said criteria of said Desired End State;

implementing an articulated, automated Risk Deprecation Analysis, comprising: iterating evaluation results incorporated in each Results Matrix associated with said FCOA Evaluation for a plurality of iterations, deprecating a different said ECOA Results column from a said Results Matrix each time to yield a Deprecated Ranking (DR) score reflecting the merit of an individual said FCOA relative to other candidate said FCOAs when said deprecated ECOA is deprecated from said ECOA IPB set and a Change in Ranking (CR) reflecting the change in said DR score from a non-deprecated analysis,

wherein said Risk Deprecation analysis enables said user to quickly understand the risk of each said FCOA candidate relative to any ECOA in said ECOA IPB set, and wherein said Risk Deprecation Analysis allows trade-off analysis of alternatives to address inherent limitations of said ECOA IPB set and said aggregated scores of said FCOAs, and

wherein each said DR score in said FCOA Evaluation reflects a war gaming analysis against non-deprecated said ECOAs within said ECOA IPB set; implementing an automated, articulated Evaluation Criteria Deprecation Analysis, comprising: deprecating said criteria for said Desired End State individually rather than deprecating each said ECOA from said ECOA IPB set,

wherein said Evaluation Criteria Deprecation Analysis enables said user to fully evaluate the cost of each said evaluation criterion for said Desired End State in terms of finding a said FCOA that would otherwise score well against remaining non-deprecated said evaluation criteria; and

wherein, as a result of said Evaluation Criteria Deprecation Analysis,, said user may decide to accept a said FCOA with an otherwise low score, since the cost of a specific said deprecated evaluation criterion is much more than originally anticipated, and

wherein automation for said Evaluation Criteria Deprecation Analysis parallels said Risk Deprecation Analysis with said Results Matrix for each said Evaluation Criteria Deprecation Analysis and said Risk Deprecation Analysis being similar, and

wherein said Evaluation Criteria Deprecation Analysis allows said user to quickly understand the relative cost of each of said criteria used to evaluate said FCOA candidate set for the original said Desired End State; selecting a said FCOA evaluation criterion and deleting all other said FCOA evaluation criteria and implementing said selected FCOA evaluation criterion in said simulation; conducting an FCOA Vulnerability Analysis of a selected said FCOA,

wherein said user executes said FCOA Vulnerability Analysis in the same manner as said Reverse IPB process, except that said user submits only said selected FCOA versus a single to-be-re-evaluated said ECOA in a said ECOA IPB set; conducting an optimization analysis to find those said FCOAs that are optimized against a selected said ECOA, wherein said user employs said FCOA Vulnerability Analysis to identify said ECOAs optimized against said selected FCOA, and

wherein said identification of said ECOAs optimized against said selected FCOA enumerates vulnerabilities of said selected FCOA and facilitates employment of countermeasures to reduce said vulnerabilities, and submitting each said identified ECOA to said Terrain Informed War Game Model to produce snapshot sets that pre-inform intelligence collection activities; developing Projected Scripts for at least one said Most Dangerous ECOAs v. said selected FCOA, said Projected Scripts comprising: snapshot sets produced by said Terrain Informed War Game Model in a last phase of said FCOA Vulnerability Analysis after said at least one Most Dangerous ECOA has been identified; information facilitating development of an IPB Event Template with associated Event Matrix,

wherein employing said Projected Scripts for at least one said Most Dangerous ECOA provides said user sufficient time to react to a potential vulnerability, and wherein said Projected Scripts for at least one said Most Dangerous ECOA maintain a time-phased estimate of location and status of all said units relative to said mobility corridors; developing Projected Scripts for Most Likely ECOAs v. said selected FCOA, said Projected Scripts for Most Likely ECOAs comprising: snapshot sets produced by said Terrain Informed War Game Model in a last phase of said FCOA Vulnerability Analysis after said Most Likely ECOAs have been identified; information facilitating development of said IPB Event Template with said associated Event Matrix,

wherein employing said Projected Scripts for Most Likely ECOAs provides said user sufficient time to react to a potential vulnerability, and

wherein said Projected Scripts for Most Likely ECOAs maintain a time-phased estimate of location and status of all said units relative to said mobility corridors; maintaining a subset of said ECOA IPB set that represents Most Likely Candidate ECOAs,

wherein said user retrieves said subset of said ECOA IPB Set to resubmit to said Terrain Informed War Game Model with said selected FCOA; and developing two IPB products, an IPB Event Template and corresponding Event Matrix in accordance with military IPB doctrine,

wherein said IPB Event Template displays where to collect information indicating said COA adopted by said opposing force, and

wherein said Event Matrix supports said IPB Event Template by providing narrative details, and

wherein said IPB Event Template and said Event Matrix together pre-inform intelligence collectors by focusing collection requirements, and

wherein said user manually develops said IPB products by comparing relative disposition of forces in both said Most Dangerous ECOAs and said Most Likely Candidate ECOAs Scripts by conducting a differential analysis to find unique disposition indicators, and

wherein said user establishes a Named Area of Interest (VAI) on said IPB Event Template as a polygon at an entry point of said mobility corridor, and

wherein said user records into said Event Matrix associated activity.

4. The method of claim 3 in which said METT-T Parser populates the following said ECOA Variables:

Total Unit Variables,

wherein said METT-T Parser establishes at least one ECOA Variables set for a total unit, and

wherein a user chooses instances of said ECOA variables in each of multiple pull- down menus by clicking a submit button after selecting said instances;

Variables for Units Subordinate to a Total Unit,

wherein said METT-T Parser establishes a set of said ECOA Variables for each said subordinate unit;

Task Organizable Units Variables,

wherein said Task Organizable Units Variables assigns selected smaller said subordinate units to larger said subordinate units as implemented in said Terrain Informed War Game Model.

5. The method of claim 3 in which said ECOA variables comprise:

Num Abreast,

wherein said Num Abreast variable describes the number of columns said unit employs in formation;

Unit Boundaries,

wherein said Unit Boundaries variable describes the location of internal boundaries between subordinate said units, subject to selection of said Num-Abreast variable;

Unit Formation,

wherein said Unit Formation variable describes a set of possible arrays of subordinate units, given selection for said Num-Abreast COA variable;

Unit Assignments,

wherein said Unit Assignment variable binds specified said subordinate units to specified formation slots;

Anchor LDT,

wherein said Anchor LDT variable assigns a game board LDT as the location of a primary defensive array of said subordinate units;

Priority of General Support (GS) Units by Formation Slot,

wherein said GS Units by Formation Slot variable facilitates supporting all said subordinate units;

Severity of GS by Formation Slot;

wherein said Severity of GS by Formation Slot variable provides a percentage distribution of said GS for each said subordinate unit.

6. The method of claim 3 in which said support unit FCOA variables comprise:

Left and Right Boundaries variable,

wherein said METT-T Parser assigns control measures that constrain physical deployment of each said subordinate unit beyond assigned said left and right boundaries;

Anchor Line Setback variable,

wherein said METT-T Parser assigns a physical distance that a selected said subordinate unit should displace behind mid-point of said Anchor Line LDT;

Reinforce Policy variable,

wherein said METT-T Parser assigns categories of Neither, Left, Right, or Both as a policy for a selected said subordinate unit that when not attacked, allows said selected subordinate unit to reinforce a neighboring said subordinate unit on defense;

Withdrawal Criteria variable,

wherein said METT-T Parser assigns said Withdrawal Criteria in a range from approximately 95% to approximately 5% strength, an attrition threshold that, when met, directs said Terrain Informed War Game Model to withdraw a said unit from combat;

Delay-or-Reserve variable,

wherein said METT-T Parser directs a selected said unit to either a Delay or a Reserve mission, if said selected unit is not participating in a main anchor line defense, as prescribed by a formation selection;

Delay Depth variable,

wherein said METT-T Parser directs the depth of a delay if a selected said subordinate unit is assigned that task in said Delay-or-Reserve Variable;

Reserve Lag Distance variable,

wherein said METT-T Parser directs the distance a reserve emplacement is located behind an anchor line, if said selected subordinate unit is assigned a reserve task in said Delay-or-Reserve Variable;

Reserve Threshold variable,

wherein said METT-T Parser directs a threshold of total unit attrition required before commitment of a said subordinate unit in reserve, if said selected subordinate unit is assigned a Reserve task in said Delay-or-Reserve COA Variable;

Reserve Guidance variable,

wherein said METT-T Parser directs the employment philosophy of said selected subordinate unit, when committed, if said selected subordinate unit is assigned a Reserve task in said Delay-or-Reserve Variable;

Reserve Lane variable,

wherein said METT-T Parser directs V-Lane emplacement of said selected subordinate unit, if a specified said selected unit is assigned a Reserve mission by said Delay-or-Reserve COA Variable; and

Upon Penetration,

wherein said METT-T Parser directs actions of said selected subordinate unit if penetrated by an attacking force, assuming said selected subordinate unit is selected as part of an anchor-line defense.

7. The method of claim 6 in which said Left and said Right Boundary variables cooperate to identify a set of contiguous said Virtual (V)-Lanes for deploying each said unit, all said units inside assigned said boundaries for each said unit.

8. The method of claim 3 in which the distribution of said instances for said Unit Boundaries and said Unit Formation variables follows Pascal's Triangle for Binomial Expansion.

9. The method of claim 3 in which said METT-T Parser develops said instances for n-factorial bindings, where n is the number of said subordinate units.

10. The method of claim 3 in which said METT-T Parser, with said Anchor LDT, identifies as possible options all said LDTs input to a said game board.

11. The method of claim 3 in which Anchor Line Setback represents a common military technique, and said METT-T Parser provides a set of instances enabling modeling of said Anchor Line Setback option for each said selected subordinate unit.

12. The method of claim 3 in which Offensive said COAs are similar to Defensive said COAs.

13. The method of claim 3 in which said METT-T Parser populates said FCOA Variables comprising:

Total Unit variables,

wherein said Total Unit variables establish a set of FCOA Variables for a total unit;

Subordinate Unit Variables,

wherein said METT-T Parser establishes a set of said FCOA Variables for each said subordinate unit;

Task Organizable Unit Variables,

wherein said Task Organizable Units display a tactical assignment of selected small said subordinate units to larger said subordinate units as implemented in said Terrain Informed War Game Model.

14. The method of claim 3 in which said METT-T Parser establishes a set of Offensive FCOA Total Unit Variables, comprising:

Num Abreast Variables,

wherein said Num Abreast variables describe the number of columns said unit employs in formation as mitigated by the number of available said subordinate units and number of available said V-lanes;

Unit Boundary Variables,

wherein said Unit Boundary variables describe the location of internal boundaries between subordinate said units, subject to selection of said Num-Abreast variable;

Unit Formation Variables,

wherein said Unit Formation variables describe a set of possible arrays of subordinate units, given selection for said Num-Abreast variable;

Unit Assignment Variables,

wherein said Unit Assignment variables bind specified said subordinate units to specified formation slots;

Priority of General Support (GS) Units by Formation Slot Variable,

wherein said Priority of GS Units variables establish priorities of selected said subordinate units for allocation of general support resources and said GS Units support all said subordinate units;

Severity of GS by Formation Slot Variable;

wherein said Severity of GS by Formation Slot variable provides a percentage distribution of said GS for each said subordinate units.

15. The method of claim 3 in which said METT-T Parser establishes a set of Offensive FCOA Variables reflecting user selections for each said subordinate unit, comprising:

Subordinate Unit Variables,

wherein said METT-T Parser establishes said FCOA Variables for each said subordinate unit to reflect selections of a user for each selected said subordinate unit.

Left and Right Boundary Variables,

wherein said METT-T Parser assigns control measures that constrain physical deployment of a said selected subordinate unit by identifying a set of contiguous said V-Lanes for said selected subordinate unit to deploy in;

Stutter Start Variable,

wherein said METT-T Parser specifies a wait-time before initial movement for lead attacking said subordinate units, enabling said total unit to create common military formations for movement;

Bypass Criteria Variable,

wherein said METT-T Parser establishes a policy for how much defensive force an attacking said selected subordinate unit can bypass once said defense force has been breached;

Withdrawal Criteria Variable,

wherein said METT-T Parser assigns an attrition threshold that when met, directs said Terrain Informed War Game Model to withdraw said selected subordinate unit from combat;

Follow-and-Support (F&S) or Reserve Variables,

wherein said METT-T Parser establishes guidance to direct said selected subordinate unit to either a F&S or a Reserve mission if said selected subordinate unit is not attacking;

Reserve Lane Variable,

wherein said METT-T Parser directs emplacement of said selected subordinate unit in a said V-Lane, if said F&S-or-Reserve Variable assigns said selected unit a reserve mission;

Reserve Threshold Variable,

wherein said METT-T Parser directs the level of overall unit attrition that must be tolerated before committing said selected subordinate unit if said selected subordinate unit is assigned a reserve task;

Reserve Guidance Variable,

wherein said METT-T Parser establishes the employment philosophy of said selected subordinate unit when committed to attack if said selected subordinate unit is assigned said reserve task in said F&S-or-Reserve Variable;

Reserve Lag Distance Variable,

wherein said METT-T Parser establishes the distance of reserve emplacement behind said anchor line if said selected subordinate unit is assigned said reserve task in said F&S-or-Reserve Variable;

Upon Penetration Variable,

wherein said METT-T Parser establishes actions of attacking said selected subordinate unit should said attacking subordinate unit penetrate a defense, employing four policy instances of Stay (stop), Left Envelop (turn left), Right Envelop (turn right), and Turn Deep (go straight);

At OBJ Variable,

wherein said METT-T Parser establishes the actions of attacking said selected subordinate unit upon reaching an assigned objective via alternative said policy instances of said Stay (at the objective) or Expand (to neighboring objectives), with respect to said Unit Boundary Variables; and

Task Organizable Unit Variables,

wherein said METT-T Parser enables display of the tactical assignment of selected small units to larger said subordinate units that are components of said total unit.

16. The method of claim 15 in which said METT-T Parser provides a full set of instances for said Reserve Guidance variable in four alternatives comprising:

Stay in Lane,

wherein said Stay in Lane alternative directs said selected reserve unit to remain in said initial V-lane;

Best Dent,

wherein said Best Dent alternative directs said selected reserve unit to the defense location closest to penetration;

Best Hole,

wherein said Best Hole alternative directs said selected reserve subordinate unit to the most significant penetration, and

First Hole,

wherein said First Hole alternative commits said selected reserve subordinate unit to the first penetration, regardless of whether said Reserve Threshold has been met.

17. The method of claim 3 in which a user develops ECOAs by hand-selecting ECOA Variables.

18. The method of claim 3 in which a user develops said ECOA IPB set by conducting a Reverse IPB analysis made from the perspective of an opposing force, comprising:

running said IPB process in reverse to render choices, using the same said terrain game board as input with said Articulated MCOO and said Braswell Index;

swapping friendly and enemy Orders of Battle and Mission Postures; and

recursively employing said Reverse-IPB procedure to enable said user to identify at least one said ECOA from said ECOA IPB set available to an opposing force,

wherein said Reverse IPB analysis optimizes mapping of the game-theoretic context of an engagement.

19. The method of claim 3 in which said user manually adjusts said ECOA IPB set after an initial analysis of the relative merits of two or more FCOAs,

wherein, if said user manually adjusts said ECOA IPB set, changes to said FCOAs are re-submitted to said FCOA Evaluator to update said evaluation.

20. The method of claim 3 in which said user employs Manual FCA Optimization, said FCOA Optimization thru a Genetic Algorithm and said FCOA Evaluator to submit improved said FCOAs to said FCOA Candidate Set.

21. The method of claim 3 in which said Terrain Informed War Game Model employs at least sub-processes comprising:

Arraying Initial said game pieces in accordance with settings of said ECOA and said FCOA Variables,

wherein said Terrain Informed War Game Model translates directions from submitted said ECOA IPB and said FCOA sets, and develops appropriate said game pieces representing said units from said Enemy and said Friendly OBs, deploying said game pieces to start positions on said Articulated MCOO Game Board;

Incrementing a Time-Slice Counter,

wherein said user inputs said Missions and Postures and specifies a desired game time slice and said Terrain Informed War Game Model iterates a time-phased series of sub-steps until termination criteria are met, said sub-steps facilitating maneuvering and engaging said game pieces in accordance with stored policies, thresholds, and guidance for specific engagements, said sub-steps comprising:

positioning said game pieces in accordance with current situation and Variable Settings of said FCOAs and said ECOAs,

wherein said Terrain Informed War Game Model acknowledges physical constraints and moves each said game piece in accordance with said user's selections of said FCOA and said ECOA Variables;

Calculating Attrition for Game Pieces in Contact,

wherein said Terrain Informed War Game Model places selected said game pieces of friendly forces in a firefight when said game pieces of opposing forces move within a predetermined engagement distance in the same said mobility corridor occupied by selected said game pieces of friendly forces, and

wherein said Terrain Informed War. Game Model compares the status of each said game piece with said Desired End State criteria, said thresholds, and said policies in executing each said selected FCOA and simulates action;

Assessing Attrition and Updating Status for Each said Game Piece, wherein if two said game pieces are participating in an active firefight, then said Terrain Informed War Game Model assesses said attrition by reducing current strength of participating said game pieces;

Creating Snapshots of Locations of said Game Pieces and Stat using Same, wherein a record is created of location and status of every said game piece on said game board during a specified said time slice;

Testing of Battle Termination Criteria,

wherein if said Engagement Termination Criteria have not been met, said Terrain Informed War Game Model iterates at said step of Incrementing a Time Slice Counter and when said current engagement passes said Testing of Battle Termination Criteria, then said Terrain Informed War Game Model finalizes said Snapshot Set for use by said FCOA Evaluator and for said Visualization;

Outputting Battle Snapshot Sets,

wherein said Terrain Informed War Game Model develops a time-phased set of said snapshots taken during the course of said engagement, one per said time slice, that is used for later evaluation by said FCOA Evaluator, for said Visualization, or for both, and wherein said Terrain Informed War Game Model finalizes a set of said snapshots upon termination of said engagement, and

wherein if said user executes a war game for the purpose of said Visualization, said Terrain Informed War Game Model outputs an entire set of said snapshots to a Visualization device, and

wherein said Visualization device provides a display of said Game Board with controls for directing which said snapshot to permit said user to quickly run an animation on, said animation able to be presented in either forward or reverse, and

wherein if a purpose of said sub-steps is to support said FCOA Evaluator, said Terrain Informed War Game Model outputs only the last said snapshot of said engagement.

22. The method of claim 21 in which said Terrain Informed War Game Model in Calculating Attrition for Game Pieces in Contact performs the steps of:

determining relative combat power of each said game piece, as modified by local terrain effects abstracted by said Braswell Index in a pre-specified said mobility corridor,

wherein said step of determining relative combat power upgrades said game pieces that leverage terrain, and downgrades said game pieces that disregard terrain characteristics;

consulting an implementation of the Dupuy QJMA attrition model to determine how much said attrition each said game piece should suffer during subsequent said time slices,

wherein since said Terrain Informed War Game Model assesses attrition during each of a plurality of said time slices, yielding a discretized approximation of the Lanchester Differential Equation for combat attrition.

23. The method of claim 3 supporting said doctrinal Military Decision Making Process (MDMP) requiring an analysis, via war gaming, of said FCOA candidates against said ECOA IPB set developed during said IPB by providing said FCOA Evaluator with a Desired End State as an evaluation criterion.

24. The method of claim 3, said Desired End State criteria further comprising:

Overall Unit Criteria,

wherein said Overall Unit Criteria Candidates are used to establish a goal of optimizing overall percentage end strength of a said unit, and

wherein said Overall Unit Criteria forms an enemy-based objective in which said enemy attrition is a prime consideration;

Time Criteria,

wherein said Time Criteria are employed to establish performance indicators for specified said FCOA candidates, and

wherein said Time Criteria allow said user to visualize how time affects performance;

Specific Unit Criteria,

wherein said Specific Unit Criteria establish goals of optimizing end strength of specified said subordinate units of a said unit, and

wherein said Specific Unit Criteria permit a user to specify a said unit regardless of employment or may specify uncommitted said reserve units, regardless of which said units said FCOA assigns as a reserve unit;

Mobility Corridor Criteria,

wherein said Mobility Corridor Criteria establish terrain-based objectives with goals of maximizing or minimizing end strength of a said unit at specified locations within said mobility corridors on said game board, and

wherein said user develops said Desired End State by selecting a combination of said Desired End State criteria that reflects how said user prefers said game board to appear at the end of a successful mission, and

wherein said user establishes a weighting scheme to reflect relative preferences for each of said Desired End State criteria.

25. The method of claim 24 providing a default set of said Desired End State criteria, wherein said default set supports planning and analysis prior to formally establishing said Desired End State criteria.

26. The method of claim 3 implementing an FCOA optimization technique to facilitate finding a finite number of sufficient FCOA candidates to consider, said FCOA optimization technique selected from the group consisting of: Manual FCOA Optimization, FCOA Optimization Employing a Genetic Algorithm, and any combination thereof in any order of implementation.

27. The method of claim 26 providing said FCOA optimization technique as Manual FCOA Optimization, employing said ECOA IPB set and said FCOA candidate set via said Terrain Informed War Game Model as input to said FCOA Evaluator together with input for said Desired End State to accomplish one or more evaluations of said FCOA candidates;

wherein said Manual FCOA Optimization iterates a reasonable number of times to permit timely evaluation.

28. The method of claim 26 providing said FCOA optimization technique as said FCOA Optimization Employing a Genetic Algorithm, employing said ECOA IPB set and said FCOA candidates via said Terrain Informed War Game Model as input to said FCOA Evaluator together with input for said Desired End State to accomplish one or more evaluations of said FCOA candidates,

wherein said FCOA Optimization thru a Genetic Algorithm may iterate as many as several thousand times employing automation available on said specially programmed computer.

29. The method of claim 3 providing techniques to further analyze relative merits of said FCOA candidate set, said techniques considering factors that were previously encapsulated in a cumulative score for said FCOA candidate set, said techniques consisting of additional analysis of previously abstracted information selected from the group consisting of: Manual FCOA Optimization, FCOA Optimization Employing a Genetic Algorithm, and any combination thereof in any order of implementation,

wherein said techniques to further analyze relative merits of said FCOA candidates facilitate selection of a said FCOA.

30. The method of claim 29, stopping said FCOA Optimization Employing a Genetic Algorithm when said user decides an FCOA Candidate Set is optimized,

wherein said user makes an MDMP FCOA decision by selecting or modifying one of said FCOAs in said optimized FCOA Candidate set.

31. The method of claim 29 providing at least two techniques to analyze relative merits of said FCOA candidates: risk deprecation and evaluation criteria deprecation, wherein said two techniques consider factors previously encapsulated in said cumulative score, and

wherein said two techniques optimize selection of an FCOA.

32. The method of claim 3 further providing opportunity for said user to highlight values in a column of said Results Matrix to further facilitate decision making by choosing any selections from the group consisting of: “greater than selection,” “less than selection,” or “no highlight.”

33. The method of claim 3 further providing for FCOA comparison tools selected from the group consisting of: color coded highlighting, filters and combinations thereof.

34. The method of claim 33 said color coded highlighting comprising Red-Amber-Green color coding and said filters as COA-variable filters.

35. The method of claim 3 in which one said countermeasure is to identify within a pre-specified time interval if an opposing force is executing a Dangerous ECOA.