Modeling human and organizational behavior using a relation-centric multi-agent system design paradigm

Today's modeling and simulation communities are being challenged to create rich, detailed models incorporating human decision-making and organizational behavior. Recent advances in distributed artificial intelligence and complex systems theory have demonstrated that such ill-defined problems can be effectively modeled with agent-based simulation techniques using multiple, autonomoous, adaptive entities. RELATE, a relation-centric design paradigm for multi-agent systems (MAS), is presented to assist developers incorporate MAS solutions into their simulations. RELATe focuses the designer on six key concepts of MAS simulations: relationships, environment, laws, agents, things, and effectors. A library of Java classes is presented which enables the user to rapidly prototype an agent-based simulation. This library utilizes the Java programming language to support cross-platform and web based designs. All Java classes and interfaces are fully documented using HTML Javadoc format. Two reference cases are provided that allow for easy code reuse and modification. Finally, an existing metworked DIS-Java-VRML simulation was modified to demonstrate the ability to utilize the RELATE library to add agents to existing applications. LCDR Kim Roddy focused on the development and refinement of the RELATE design paradigm, while LT Mike Dickson focused on the actual Java implementation. Joint work was conducted on all research and reference caseshttp://www.archive.org/details/modelinghumanorg00roddU.S. Navy (U.S.N.) author

Semantic correlation of behavior for the interoperability of heterogeneous simulations

A desirable goal of military simulation training is to provide large scale or joint exercises to train personnel at higher echelons. To help meet this goal, many of the lower echelon combatants must consist of computer generated forces with some of these echelons composed of units from different simulations. The object of the research described is to correlate the behaviors of entities in different simulations so that they can interoperate with one another to support simulation training. Specific source behaviors can be translated to a form in terms of general behaviors which can then be correlated to any desired specific destination simulation behavior without prior knowledge of the pairing. The correlation, however, does not result in 100% effectiveness because most simulations have different semantics and were designed for different training needs. An ontology of general behaviors and behavior parameters, a database of source behaviors written in terms of these general behaviors with a database of destination behaviors. This comparison is based upon the similarity of sub-behaviors and the behavior parameters. Source behaviors/parameters may be deemed similar based upon their sub-behaviors or sub-parameters and their relationship (more specific or more general) to destination behaviors/parameters. As an additional constraint for correlation, a conversion path from all required destination parameters to a source parameter must be found in order for the behavior to be correlated and thus executed. The length of this conversion path often determines the similarity for behavior parameters, both source and destination. This research has shown, through a set of experiments, that heuristic metrics, in conjunction with a corresponding behavior and parameter ontology, are sufficient for the correlation of heterogeneous simulation behavior. These metrics successfully correlated known pairings provided by experts and provided reasonable correlations for behaviors that have no corresponding destination behavior. For different simulations, these metrics serve as a foundation for more complex methods of behavior correlation

Applications of agent architectures to decision support in distributed simulation and training systems

This work develops the approach and presents the results of a new model for applying intelligent agents to complex distributed interactive simulation for command and control. In the framework of tactical command, control communications, computers and intelligence (C4I), software agents provide a novel approach for efficient decision support and distributed interactive mission training. An agent-based architecture for decision support is designed, implemented and is applied in a distributed interactive simulation to significantly enhance the command and control training during simulated exercises. The architecture is based on monitoring, evaluation, and advice agents, which cooperate to provide alternatives to the dec ision-maker in a time and resource constrained environment. The architecture is implemented and tested within the context of an AWACS Weapons Director trainer tool. The foundation of the work required a wide range of preliminary research topics to be covered, including real-time systems, resource allocation, agent-based computing, decision support systems, and distributed interactive simulations. The major contribution of our work is the construction of a multi-agent architecture and its application to an operational decision support system for command and control interactive simulation. The architectural design for the multi-agent system was drafted in the first stage of the work. In the next stage rules of engagement, objective and cost functions were determined in the AWACS (Airforce command and control) decision support domain. Finally, the multi-agent architecture was implemented and evaluated inside a distributed interactive simulation test-bed for AWACS Vv\u27Ds. The evaluation process combined individual and team use of the decision support system to improve the performance results of WD trainees. The decision support system is designed and implemented a distributed architecture for performance-oriented management of software agents. The approach provides new agent interaction protocols and utilizes agent performance monitoring and remote synchronization mechanisms. This multi-agent architecture enables direct and indirect agent communication as well as dynamic hierarchical agent coordination. Inter-agent communications use predefined interfaces, protocols, and open channels with specified ontology and semantics. Services can be requested and responses with results received over such communication modes. Both traditional (functional) parameters and nonfunctional (e.g. QoS, deadline, etc.) requirements and captured in service requests

Multi-Objective Constraint Satisfaction for Mobile Robot Area Defense

In developing multi-robot cooperative systems, there are often competing objectives that need to be met. For example in automating area defense systems, multiple robots must work together to explore the entire area, and maintain consistent communications to alert the other agents and ensure trust in the system. This research presents an algorithm that tasks robots to meet the two specific goals of exploration and communication maintenance in an uncoordinated environment reducing the need for a user to pre-balance the objectives. This multi-objective problem is defined as a constraint satisfaction problem solved using the Non-dominated Sorting Genetic Algorithm II (NSGA-II). Both goals of exploration and communication maintenance are described as fitness functions in the algorithm that would satisfy their corresponding constraints. The exploration fitness was described in three ways to diversify the way exploration was measured, whereas the communication maintenance fitness was calculated as the number of independent clusters of agents. Applying the algorithm to the area defense problem, results show exploration and communication without coordination are two diametrically opposed goals, in which one may be favored, but only at the expense of the other. This work also presents suggestions for anyone looking to take further steps in developing a physically grounded solution to this area defense problem

Multi Agent Systems

Research on multi-agent systems is enlarging our future technical capabilities as humans and as an intelligent society. During recent years many effective applications have been implemented and are part of our daily life. These applications have agent-based models and methods as an important ingredient. Markets, finance world, robotics, medical technology, social negotiation, video games, big-data science, etc. are some of the branches where the knowledge gained through multi-agent simulations is necessary and where new software engineering tools are continuously created and tested in order to reach an effective technology transfer to impact our lives. This book brings together researchers working in several fields that cover the techniques, the challenges and the applications of multi-agent systems in a wide variety of aspects related to learning algorithms for different devices such as vehicles, robots and drones, computational optimization to reach a more efficient energy distribution in power grids and the use of social networks and decision strategies applied to the smart learning and education environments in emergent countries. We hope that this book can be useful and become a guide or reference to an audience interested in the developments and applications of multi-agent systems

Cooperative Behavior In ModSAF

Report on research into mechanisms for cooperative behaviors in computer generated forces

Multi-Agent Systems

A multi-agent system (MAS) is a system composed of multiple interacting intelligent agents. Multi-agent systems can be used to solve problems which are difficult or impossible for an individual agent or monolithic system to solve. Agent systems are open and extensible systems that allow for the deployment of autonomous and proactive software components. Multi-agent systems have been brought up and used in several application domains

Intelligent Agents and Their Potential for Future Design and Synthesis Environment

This document contains the proceedings of the Workshop on Intelligent Agents and Their Potential for Future Design and Synthesis Environment, held at NASA Langley Research Center, Hampton, VA, September 16-17, 1998. The workshop was jointly sponsored by the University of Virginia's Center for Advanced Computational Technology and NASA. Workshop attendees came from NASA, industry and universities. The objectives of the workshop were to assess the status of intelligent agents technology and to identify the potential of software agents for use in future design and synthesis environment. The presentations covered the current status of agent technology and several applications of intelligent software agents. Certain materials and products are identified in this publication in order to specify adequately the materials and products that were investigated in the research effort. In no case does such identification imply recommendation or endorsement of products by NASA, nor does it imply that the materials and products are the only ones or the best ones available for this purpose. In many cases equivalent materials and products are available and would probably produce equivalent results