Abstract: A method of predicting the behavior of software agents in a simulated environment involving modeling a plurality of software agents representing entities to be analyzed, which may be human beings. Using a set of parameters that governs the behavior of the agents, the internal state of at least one of the agents is estimated by its behavior in the simulation, including its movement within the environment. This facilitates a prediction of the likely future behavior of the agent based solely upon its internal state; that is, without recourse to any intentional agent communications. In one embodiment, the simulated environment is based upon a digital pheromone infrastructure. The simulation integrates knowledge of threat regions, a cognitive analysis of the agent's beliefs, desires, and intentions, a model of the agent's emotional disposition and state, and the dynamics of interactions with the environment.

Abstract: A method includes generating a plurality of bid requests for processing a workpiece. Each bid request is associated with one of a plurality of resources capable of processing the workpiece. For each of the bid requests, a commitment window including a kernel specifying a time period required for processing the workpiece is generated. A first committed capacity of the associated resource is determined based on a schedule of engagements compatible with the processing required for the workpiece. A second committed capacity of the associated resource is determined based on a schedule of engagements not compatible with the processing required for the workpiece. A first rate function specifying a processing cost for the associated resource as a function of the first and second committed capacities is provided. The first and second committed capacities and the first rate function are combined to generate a basic cost function associated with the associated resource.

Abstract: A swarming agent architecture provides a distributed, decentralized, agent-based computing environment applicable to ground-based surveillance. The approach, called Sensor Network Integration through Pheromone Fusion, or “SNIPF,” provides an end-to-end demonstration that integrates self-contained sensor/communication nodes with novel swarming algorithms to detect foot and vehicular movement through a monitored area with minimal configuration and maintenance. A plurality of computational nodes distributed within the environment and, depending upon the way in which they are deployed, the various nodes are operative to sense the local environment, receive a message from a neighboring node, and transmit a message to a neighboring node. Given these capabilities, the nodes can collectively determine the presence and/or movement of a target and communicate this information to a user.

Abstract: Swarming agents in networks of preferably physically distributed processing nodes are used for data acquisition, data fusion, and control applications. An architecture for active surveillance systems is presented in which simple mobile agents collectively process real-time data from heterogeneous sources at or near the origin of the data. System requirements are specifically matched to the needs of a surveillance system for the early detection of large-scale bioterrorist attacks on a civilian population, but the same architecture is applicable to a wide range of other domains. The pattern detection and classification processes executed by the proposed system emerge from the coordinated activities of agents of two populations in a shared computational environment. Detector agents draw each other's attention to significant spatio-temporal patterns in the observed data stream. Classifier agents rank the detected patterns according to their respective criterion.

Abstract: A method for generating a cost function includes providing a resource for processing a workpiece. A plurality of cost function parameters is provided. A library of parameterized cost function components is acessed based on the plurality of cost function parameters to generate a cost function for processing the workpiece using the resource. A system includes a resource for processing a workpiece and at least one scheduling agent. The scheduling agent is configured to provide a plurality of cost function parameters and access a library of parameterized cost function components based on the plurality of cost function parameters to generate a cost function for processing the workpiece using the resource.

Abstract: A method includes providing a schedule of engagements for a resource. Each engagement has a working window and an associated engagement density function. The engagement density functions of the scheduled engagements are combined to generate a committed capacity function for the resource. A region of violation in the committed capacity function is identified where the committed capacity of the resource exceeds a capacity threshold. An area of a region of overlap between the working window of a selected one of the engagements and the region of violation is determined. An area reduction amount for the selected engagement is determined based on a portion of the area of the region of overlap. The working window of the selected engagement is changed based on the area reduction amount.

Abstract: The invention is a technique by which a manufacturing process flow determines when to begin processing a batch of lots. The invention includes a method for determining whether to begin processing a batch in one aspect and a manufacturing process flow in a second aspect. The method comprises: ascertaining a respective time at which each of a plurality of additional lots will be received into the batch, the batch including a plurality of present lots; assessing a cost for a machine, each additional lot, and each present lot for each respective time at which each additional lot will be received should the machine begin processing the batch at the respective time; and determining the respective time at which the total cost borne by the machine, each additional lot, and each present lot will be at a minimum.

Abstract: A method for generating a cost function for processing a candidate workpiece using a resource includes identifying processing requirements for the candidate workpiece. A first committed capacity of the resource is determined based on a schedule of engagements associated with other workpieces having processing requirements compatible with the processing requirements of the candidate workpiece. A second committed capacity of the resource is determined based on a schedule of engagements associated with other workpieces having processing requirements not compatible with the processing requirements of the candidate workpiece. The cost function is generated based on the first and second committed capacities. A system includes a resource for processing a candidate workpiece and at least one scheduling agent.

Abstract: A method for scheduling a resource for processing a workpiece includes defining a commitment window having a kernel specifying a time period required for processing the workpiece. A plurality of candidate bids having candidate commitment windows within the commitment window with varying start times, end times and candidate commitment window sizes is generated. A cost for each of the plurality of candidate bids is determined. A flexibility discount is applied to the cost of the candidate bid. Each candidate bid is evaluated in accordance with an objective function. A candidate bid is selected for scheduling the resource based on the objective function evaluation. A system includes a resource for processing a workpiece and at least one scheduling agent.

Abstract: A method for processing workpieces using a resource includes generating a plurality of engagements for processing the workpieces by the resource. Each engagement is associated with one of the workpieces and has associated processing requirements. A first one of the plurality of engagements is designated as a seed engagement. A set of candidate engagements from the plurality of engagements having associated processing requirements compatible with the processing requirements of the seed engagement is identified. A combined engagement is generated including the seed engagement and at least one of the candidate engagements.

Abstract: A swarming agent architecture provides a distributed, decentralized, agent-based computing environment applicable to ground-based surveillance. The approach, called Sensor Network Integration through Pheromone Fusion, or “SNIPF,” provides an end-to-end demonstration that integrates self-contained sensor/communication nodes with novel swarming algorithms to detect foot and vehicular movement through a monitored area with minimal configuration and maintenance. A plurality of computational nodes distributed within the environment and, depending upon the way in which they are deployed, the various nodes are operative to sense the local environment, receive a message from a neighboring node, and transmit a message to a neighboring node. Given these capabilities, the nodes can collectively determine the presence and/or movement of a target and communicate this information to a user.

Abstract: Swarming agents in networks of preferably physically distributed processing nodes are used for data acquisition, data fusion, and control applications. An architecture for active surveillance systems is presented in which simple mobile agents collectively process real-time data from heterogeneous sources at or near the origin of the data. System requirements are specifically matched to the needs of a surveillance system for the early detection of large-scale bioterrorist attacks on a civilian population, but the same architecture is applicable to a wide range of other domains. The pattern detection and classification processes executed by the proposed system emerge from the coordinated activities of agents of two populations in a shared computational environment. Detector agents draw each other's attention to significant spatio-temporal patterns in the observed data stream. Classifier agents rank the detected patterns according to their respective criterion.

Abstract: An apparatus for determining assignments to attributes (e.g., electrical power or overall dimensional size) of components within a system. A computerized constraint network is constructed which uses constraint agents, variable agents, and task agents in order to make assignments to the attributes of the components based upon market-based constraint optimization techniques. The attributes have variables indicative of the assignments to the attributes. Constraint data structures assist the agents in determining permissible assignments for the variables. The constraint data structures use preferential rules for determining the assignments to the variables. The preferential rules indicate which assignments for the variables of the agents produce higher utility and lower cost.

Abstract: An apparatus for determining assignments to attributes (e.g., electrical power or overall dimensional size) of components within a system. A computerized constraint network is constructed which uses constraint agents, variable agents, and task agents in order to make assignments to the attributes of the components based upon market-based constraint optimization techniques. The attributes have variables indicative of the assignments to the attributes. Constraint data structures assist the agents in determining permissible assignments for the variables. The constraint data structures use preferential rules for determining the assignments to the variables. The preferential rules indicate which assignments for the variables of the agents produce higher utility and lower cost.

Abstract: An apparatus for determining assignments to attributes (e.g., electrical power or overall dimensional size) of components within a system. A computerized constraint network is constructed which uses constraint agents, variable agents, and task agents in order to make assignments to the attributes of the components based upon market-based constraint optimization techniques. The attributes have variables indicative of the assignments to the attributes. Constraint data structures assist the agents in determining permissible assignments for the variables. The constraint data structures use preferential rules for determining the assignments to the variables. The preferential rules indicate which assignments for the variables of the agents produce higher utility and lower cost.

Abstract: A computerized task scheduler for negotiating and scheduling resources with respect to various manufacturing tasks via computerized agent technology. A manufacturing-related customer asks a computerized uniform process broker for a bid on a manufacturing task by specifying the task and the time when it is to be completed. The computerized uniform process broker finds a computerized resource broker which will perform the assignment at the lowest cost within the requested time frame. The computerized resource brokers use the requested time frame and their respective current available resources to determine a working window in which they can perform the task and at what cost. The determination of resources by the computerized resource brokers includes calculating their respective percentage of total commitment based upon the interrelationship between the working window and the time it actually takes to complete the task.