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Mathematical Problems in Engineering
Volume 2015 (2015), Article ID 964871, 13 pages
Research Article

Multiagent Cooperative Learning Strategies for Pursuit-Evasion Games

1Department of Computer Science and Information Engineering, National Taipei University of Technology, Taipei, Taiwan
2Department of Computer Science, National Taipei University of Education, Taipei, Taiwan
3Department of Safety, Health and Environmental Engineering, Ming Chi University of Technology, New Taipei City, Taiwan

Received 20 June 2014; Revised 17 September 2014; Accepted 12 October 2014

Academic Editor: Saeed Balochian

Copyright © 2015 Jong Yih Kuo et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


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.