Cooperative RoboCup agents using genetic case-based reasoning

RoboCup soccer game is a competition game. Robots need good strategy and planning ability on different conditions to win the game. If robots act by prior rules, they cannot gain ground. In this paper, we proposed a hybrid approach to implement our RoboCup agent. It can provide good strategy for robots planning base on all kinds of conditions and saving experience for reusing. Robots will grow up by our hybrid approach without prior defining knowledge and complex math basis. They just learn by saving experience. The robots don't only grow up but also avoid to making the same mistakes. And we show the effectiveness of the proposed method through implement and comparing with another learning approach.

[1]  Agnar Aamodt,et al.  Case-Based Reasoning: Foundational Issues, Methodological Variations, and System Approaches , 1994, AI Commun..

[2]  Lai Chaoan The Expert System of Product Design Based on CBR and GA , 2007, 2007 International Conference on Computational Intelligence and Security Workshops (CISW 2007).

[3]  Kang Yen,et al.  More efficient genetic algorithm for solving optimization problems , 1995, 1995 IEEE International Conference on Systems, Man and Cybernetics. Intelligent Systems for the 21st Century.

[4]  Carlos D. Castillo,et al.  Chimps: an evolutionary reinforcement learning approach for soccer agents , 2003, SMC'03 Conference Proceedings. 2003 IEEE International Conference on Systems, Man and Cybernetics. Conference Theme - System Security and Assurance (Cat. No.03CH37483).

[5]  Shuhei Kinoshita,et al.  Team 11 monkeys Description 11 monkeys , 1999 .

[6]  Nguyen Hoang Phuong,et al.  Approach to combining case based reasoning with rule based reasoning for lung disease diagnosis , 2001, Proceedings Joint 9th IFSA World Congress and 20th NAFIPS International Conference (Cat. No. 01TH8569).

[7]  Hisao Ishibuchi,et al.  An evolutionary approach for strategy learning in RoboCup soccer , 2004, 2004 IEEE International Conference on Systems, Man and Cybernetics (IEEE Cat. No.04CH37583).

[8]  Jong-Yih Kuo,et al.  Goal Evolution based on Adaptive Q-learning for Intelligent Agent , 2006, 2006 IEEE International Conference on Systems, Man and Cybernetics.