COOPERATIVE COEVOLUTION OF MULTI-AGENT SYSTEMS
COOPERATIVE COEVOLUTION OF MULTI-AGENT SYSTEMS
TARK '94 Proceedings of the 5th conference on Theoretical aspects of reasoning about knowledge
A layered approach to learning coordination knowledge in multiagent environments
Applied Intelligence
Robot team coordination for target tracking using fuzzy logic controller in game theoretic framework
Robotics and Autonomous Systems
Coordinated pursuer control using particle filters for autonomous search-and-capture
Robotics and Autonomous Systems
Autonomous Agents and Multi-Agent Systems
WI-IAT '10 Proceedings of the 2010 IEEE/WIC/ACM International Conference on Web Intelligence and Intelligent Agent Technology - Volume 03
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This paper proposes an opportunistic framework to modeling and control of multi-robots moving-target pursuit in dynamic environment with partial observability. The partial observability is achieved via the introduction of third party agent (referred to as mediator) that transforms the target's as well as group members' positioning information into the robotic agents' common knowledge, thereby eliminating the necessity of direct inter-robots communication. The robotic agents involved are modeled as fully autonomous entities, capable of determining their corresponding action profiles, using a strategy inference engine. The robot's inference engine consists of two sub-rating components viz. fixed or predefined sub-rating, and the variable or the opportunistic subrating. The action profiles at individual level are further analyzed by the mediator that finalizes the agent's action assignment at every execution cycle. It has been proven that addition of the third party mediator guarantees the optimality of group level performance.