Multiagent Cooperative Learning Strategies for Pursuit-Evasion Games. (4th October 2015)

• Record Type: Journal Article
• Authors: Kuo, Jong Yih; Yu, Hsiang-Fu; Liu, Kevin Fong-Rey; Lee, Fang-Wen
• Other Names: Balochian Saeed Academic Editor.
• Is Part Of: Mathematical problems in engineering. Volume 2015(2015)
• Publication Date: 2015-10-04
• Subjects: Engineering mathematics -- Periodicals; 510.2462
• DOI: 10.1155/2015/964871
• Languages: English
• ISSNs: 1024-123X

Abstract

Abstract : This study examines the pursuit-evasion problem for coordinating multiple robotic pursuers to locate and track a nonadversarial mobile evader in a dynamic environment. Two kinds of pursuit strategies are proposed, one for agents that cooperate with each other and the other for agents that operate independently. This work further employs the probabilistic theory to analyze the uncertain state information about the pursuers and the evaders and uses case-based reasoning to equip agents with memories and learning abilities. According to the concepts of assimilation and accommodation, both positive-angle and bevel-angle strategies are developed to assist agents in adapting to their environment effectively. The case study analysis uses the Recursive Porous Agent Simulation Toolkit (REPAST) to implement a multiagent system and demonstrates superior performance of the proposed approaches to the pursuit-evasion game.