Abstract: A system, methods and apparatus are described involving the self-organizing dynamics of networks of distributed computers. The system is comprised of complex networks of databases. The system presents a novel database architecture called the distributed transformational spatio-temporal object relational (T-STOR) database management system (dbms). Data is continuously input, analyzed, organized, reorganized and used for specific commercial and industrial applications. The system uses intelligent mobile software agents in a multi-agent system in order to learn, anticipate, and adapt and to perform numerous functions, including search, analysis, collaboration, negotiation, decision making and structural transformation. The system links together numerous complex systems involving distributed networks to present a novel model for dynamic adaptive computing systems, which includes plasticity of collective behavior and self-organizing behavior in intelligent system structures.

Abstract: A system, methods and apparatus are described involving the self-organizing dynamics of networks of distributed computers. The system uses intelligent mobile software agents in a multi-agent system to perform numerous functions, including search, analysis, collaboration, negotiation, decision making and structural transformation. Data are continuously input, analyzed, organized, reorganized, used and output for specific commercial and industrial applications. The system uses combinations of AI techniques, including evolutionary computation, genetic programming and evolving artificial neural networks; consequently, the system learns, anticipates and adapts. The numerous categories of applications of the system include optimizing network dynamics, collective robotics systems, automated commercial systems and molecular modeling systems. Given the application of complexity theory and modal and temporal logics to self-organizing dynamic networks, a novel model of intelligent systems is presented.

Abstract: A system for managing a weapon system is provided. The system includes a number of mobile robotic vehicles (MRVs), a number of squads, each squad having a lead MRV and member MRVs, a number of central control systems, a number of reactive control systems, a central planning control configured to control the plurality of central control systems, a behavior-based reactive control configured to control the plurality of reactive control systems, an intermediated control layer configured to control the central planning control and the behavior-based reactive control, a number of hybrid control models configured to communicate with the intermediate control layer, the hybrid control models including a planning driven model and an adaptation model, a number of synthetic control models configured to communicate with the hybrid control models, and a number of synthetic hybrid control models configured based on combinations of the hybrid control models and the synthetic control models.

Abstract: A hybrid router for dynamical control systems is described. The mobile hybrid software router (MHSR) combines distinctive computational and mathematical techniques, including evolutionary computation (EC), probabilistic simulations (PS), machine learning and artificial neural networks (A-NNs), in order to solve unique problems encountered in an unknown environment in real time. Embodied in intelligent mobile software agents (IMSAs), the MHSR operates within a multi-agent system (MAS) to continually optimize system operation. The MHSR is applied to several major complex system categories. In one embodiment of the system, the MHSR is implemented in hardware, including continuously programmable field programmable gate arrays (CP-FPGAs), for perpetually reconfigurable evolvable hardware operation. Whether in application-specific or multi-functional mode, the MHSR is useful to groups of agents in intelligent systems for adaptation to uncertain environments in order to perform self-organization capabilities.

Abstract: The invention develops models of functional proteomics. Simulation scenarios of protein pathway vectors and protein-protein interactions are modeled from limited information in protein databases. The system focuses on three integrated subsystems, including (1) a system to model protein-protein interactions using an evolvable Global Proteomic Model (GPM) of functional proteomics to ascertain healthy pathway operations, (2) a system to identify haplotypes customized for specific pathology using dysfunctional protein pathway simulations of the function of combinations of single nucleotide polymorphisms (SNPs) so as to ascertain pathology mutation sources and (3) a pharmacoproteomic modeling system to develop, test and refine proposed drug solutions based on the molecular structure and topology of mutant protein(s) in order to manage individual pathologies. The system focuses on simulating the degenerative genetic disease categories of cancer, neurodegenerative diseases, immunodegenerative diseases and aging.

Abstract: A system of self-organizing mobile robotic agents (MRAs) in a multi-robotic system (MRS) is disclosed. MRAs cooperate, learn and interact with the environment. The system uses various AI technologies including genetic algorithms, genetic programming and evolving artificial neural networks to develop emergent dynamic behaviors. The collective behaviors of autonomous intelligent robotic agents are applied to numerous applications. The system uses hybrid control architectures. The system also develops dynamic coalitions of groups of autonomous MRAs for formation and reformation in order to perform complex task.

Abstract: A system for managing a weapon system is provided. The system includes a number of mobile robotic vehicles (MRVs), a number of squads, each squad having a lead MRV and member MRVs, a number of central control systems, a number of reactive control systems, a central planning control configured to control the plurality of central control systems, a behavior-based reactive control configured to control the plurality of reactive control systems, an intermediated control layer configured to control the central planning control and the behavior-based reactive control, a number of hybrid control models configured to communicate with the intermediate control layer, the hybrid control models including a planning driven model and an adaptation model, a number of synthetic control models configured to communicate with the hybrid control models, and a number of synthetic hybrid control models configured based on combinations of the hybrid control models and the synthetic control models.

Abstract: A system, methods and apparatus are described involving the self-organizing dynamics of networks of distributed computers. The system is comprised of complex networks of databases. The system presents a novel database architecture called the distributed transformational spatio-temporal object relational (T-STOR) database management system (dbms). Data is continuously input, analyzed, organized, reorganized and used for specific commercial and industrial applications. The system uses intelligent mobile software agents in a multi-agent system in order to learn, anticipate, and adapt and to perform numerous functions, including search, analysis, collaboration, negotiation, decision making and structural transformation. The system links together numerous complex systems involving distributed networks to present a novel model for dynamic adaptive computing systems, which includes plasticity of collective behavior and self-organizing behavior in intelligent system structures.

Abstract: A system, methods and apparatus are described involving modular customized e-learning components. After an individual requests an e-learning process, databases are customized for him or her, thereby implementing just-in-time learning. The learner generates questions by using problem finding approaches and may use a personal learning agent. Collaboration between individuals is performed. In addition, a method is presented for conducting distributed classes both with and without (a teacher's) mediation. Part of the system involves a method featuring a dialectical learning process for problem solving. There is an automatic method to assess individual abilities.

Abstract: A system of self-organizing mobile robotic agents (MRAs) in a multi-robotic system (MRS) is disclosed. MRAs cooperate, learn and interact with the environment. The system uses various AI technologies including genetic algorithms, genetic programming and evolving artificial neural networks to develop emergent dynamic behaviors. The collective behaviors of autonomous intelligent robotic agents are applied to numerous applications. The system uses hybrid control architectures. The system also develops dynamic coalitions of groups of autonomous MRAs for formation and reformation in order to perform complex tasks.

Abstract: In a swarm weapon system, decision-making processes are described for the organization of mobile robotic vehicles (MRVs). After MRV drone sensor data is provided to a lead MRV, an initial decision is made by the lead MRV as a result of the selection of a winning simulation that provides the best opportunity for success of a mission. Once this decision is made, actions are organized for the MRVs, which provide feedback for the continuation of the process until the mission is completed.

Abstract: A system of self-organizing mobile robotic agents (MRAs) in a multi-robotic system (MRS) is disclosed. MRAs cooperate, learn and interact with the environment. The system uses various AI technologies including genetic algorithms, genetic programming and evolving artificial neural networks to develop emergent dynamic behaviors. The collective behaviors of autonomous intelligent robotic agents are applied to numerous applications. The system uses hybrid control architectures. The system also develops dynamic coalitions of groups of autonomous MRAs for formation and reformation in order to perform complex tasks.

Abstract: Aggregation processes of mobile robotic vehicles (MRVs) in a swarm weapon system are described. The formation of MRVs from the larger swarm into specific squads is organized by a lead MRV that makes decisions on the specific spatial configuration by determining the optimal approach to interact with the environment in rapidly changing battlefield situations. The initial phase of organization into specific configurations is contingent on information about enemy targets.

Abstract: Mobile software agents (MSAs) are software program code that moves from machine to machine in order to perform specific functions. MSAs are used in the swarm weapon system by using methods of transfer via wireless communications between mobile robotic vehicles (MRVs) in a multirobotic system. MSAs provide additional program parameters and analytical functions to existing program code in a real time changing battlefield environment.

Abstract: In a multirobotic system comprised of automated mobile robotic vehicles (MRVs), a network of MRV drones provides sensor information to a lead MRV, which calculates the distance to objects in the environment. By using a method of optic flow to map coordinates in spatial positions, MRVs establish mission priorities and work as a group to accomplish a mission. Once the operational coordination is made for the group to organize its priorities and tasks, the enemy targets are struck in a sequence according to mission parameters.

Abstract: A system is described for a sensor network comprised of an apparatus of a group of mobile robotic vehicles (MRVs). The sensors in the MRVs are linked into a network such that sensor data from one MRV can be used by another MRV. The collective of MRVs can reconfigure in spatial positions and thereby responds to and interacts with its environment. The mobile sensor network is used to provide sensor data to the swarm weapon system.

Abstract: Behavior-based models of robotic coordination are integrated into a synthetic hybrid control architecture of a multi-robotic system (MRS). The MRS uses rules to organize the priorities of squads of mobile robotic vehicles (MRVs) in a broader system that includes a central control model for planning, hierarchy and simulations. The system, and methods and apparatus thereof, is applied to the organization of the swarm weapon system.

Abstract: A system is described for remote sensing surveillance by configuring groups of automated mobile robotic vehicles (MRVs). The collection of MRVs have sensors that feed information to a central network node. As the MRVs interact with the environment, the mobile network adapts in real time by reconfiguring its spatial positions. This system, and the methods and apparatus involved therein, are applied to reconnaissance missions in order to collect information in remote hostile environments.

Abstract: A process is described in which mobile robotic vehicles (MRVs) in a swarm weapon system is used as a communication interface. In a battlefield with restricted communication bandwidth, swarms of MRVs can be used as a wireless repeater by interfacing between ground troops and a satellite for transmission of signals to central command.

Abstract: In a swarm weapon system including mobile robotic vehicles (MRVs), a system, methods and apparatus are described for the use of external resources, including computation and sensor capabilities, to guide groups of MRVs from position to position in real time. Methods are shown whereby external computation resources provide massive supplementary computing capability to a remote computing network to solve complex problems on the fly that preserves limited intra-systemic power and computation resources. Methods are shown for external sensors, such as satellite sensors, to provide supplemental sensor data to a multi robotic system with resource constraints.