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http://dx.doi.org/10.5391/JKIIS.2006.16.2.131

Behavior Learning and Evolution of Individual Robot for Cooperative Behavior of Swarm Robot System  

Sim, Kwee-Bo (중앙대학교 전자전기공학부)
Lee, Dong-Wook (한국생산기술연구원 로봇기술본부)
Publication Information
Journal of the Korean Institute of Intelligent Systems / v.16, no.2, 2006 , pp. 131-137 More about this Journal
Abstract
In swarm robot systems, each robot must behaves by itself according to the its states and environments, and if necessary, must cooperates with other robots in order to carry out a given task. Therefore it is essential that each robot has both learning and evolution ability to adapt the dynamic environments. In this paper, the new learning and evolution method based on reinforcement learning having delayed reward ability and distributed genetic algorithms is proposed for behavior learning and evolution of collective autonomous mobile robots. Reinforcement learning having delayed reward is still useful even though when there is no immediate reward. And by distributed genetic algorithm exchanging the chromosome acquired under different environments by communication each robot can improve its behavior ability. Specially, in order to improve the performance of evolution, selective crossover using the characteristic of reinforcement learning is adopted in this paper. we verify the effectiveness of the proposed method by applying it to cooperative search problem.
Keywords
Behavior learning; Evolution; Local communication; swarm robot; group behavior; cooperative strategy;
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