Learning fuzzy logic controller for reactive robot behaviours

IEEE/ASME International Conference on Advanced Intelligent Mechatronics, AIM

Volumn 1, Issue , 2003, Pages 46-51

  Gu, Dongbing ^a   Hu, Huosheng ^a   Spacek, L ^a  

^a UNIVERSITY OF ESSEX   (United Kingdom)

Author keywords

Computer science; Delay; Fuzzy logic; Fuzzy sets; Fuzzy systems; Genetic algorithms; Learning; Logic design; Orbital robotics; Robot control

Indexed keywords

COMPUTER CONTROL SYSTEMS; COMPUTER SCIENCE; ECONOMIC AND SOCIAL EFFECTS; FUZZY INFERENCE; FUZZY SETS; FUZZY SYSTEMS; GENETIC ALGORITHMS; INTELLIGENT MECHATRONICS; LEARNING ALGORITHMS; LOGIC DESIGN; MACHINE DESIGN; REINFORCEMENT LEARNING; ROBOTICS; ROBOTS;

DELAY; DELAYED REWARDS; FUZZY LOGIC CONTROLLERS; LEARNING; LEARNING APPROACH; ORBITAL ROBOTICS; REACTIVE BEHAVIOUR; ROBOT CONTROLS;

FUZZY LOGIC;

EID: 84943640656     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/AIM.2003.1225070     Document Type: Conference Paper

References (20)

1. Bonarini, A., Comparing Reinforcement Learning Algorithms Applied to Crisp and Fuzzy Learning Classifier Systems, Proc. of Int. Conf. on Learning Classifier Systems, Morgan Kaufmann, San Francisco, CA, 1999, pp 228-235.
2. Bonarini, A., Evolutionary Learning of Fuzzy Rules: Competition and Co-operation. In W. Pedrycz, editor, Fuzzy Modelling: Paradigms and Practice, Kluwer Academic Press, 1996, pp265-284.
3. Booker, L. B., Goldberg, D. E., Holland, J. H., Classifier System that Learn Their Internal Models, Machine Learning, Vol. 3, 1988, pp161-192.
4. De Jong, K. A., Learning With Genetic Algorithm: An Overview, Machine Learning, VOL. 3, 1988, pp121-138.
5. Glorennec, P. Y. and Jouffe, L., Fuzzy Q-Leering, Proc. of the Sixth IEEE Int. Conf. on Fuzzy Systems, Barcelona, Spain, 1997, pp659-662.
6. Gu, D. and Hu, H., Learning and Evolving of Sony Legged Robots, Proc. Int. Workshop - Recent Advances in Mobile Robots, Leicester, UK, June 2000.
7. Herrera, F. and Magdalena, L., Genetic Fuzzy Systems: A Tutorial
8. Hu H. and Gu D., Reactive Behaviours and Agent Architecture for Sony Legged Robots to Play Football, Int. Journal of Industrial Robot, Vol. 28, No. 1, Jan. 2001
9. Jouffe, L., Fuzzy Inference System Learning by Reinforcement Methods, IEEE Trans. on SMC-PART C, Vol. 28, No. 3, August 1998, pp338-355.
10. Juang, C. F. Lin, J. Y., and Lin, C. T., Genetic Reinforcement Learning through Symbiotic Evolution for Fuzzy Controller Design, IEEE Trans. On SMC-PART B, Vol. 30, No. 2, April 2000, pp290-301.
11. th Int. Conf. on Genetic Algorithms, 1991, pp450-456.
12. Lee, S. and Cho, S., Emergent Behaviours of a Fuzzy Sensory-Motor Controller Evolved by Genetic Algorithm, IEEE Trans. on SMC-B, Vol. 31, No., 6, 2001, pp919-929.
13. Michalewicz, Z., Genetic Algorithms + Data Structures = Evolution Programs, Springer, 1995.
14. Moriaty, D. E., Schultz, A. C., and Grefenstette, J, J, Evolutionary Algorithms for Reinforcement Learning, Journal of AI Research, 11(1999), pp241-276.
15. Rummery, G. A., On-Line Q-Learning Using Connectionist Systems, Tech. Rep., CUED/F-INFENG/TR 166, Cambridge University, 1994.
16. Sutton, R. S. and Barto, A.G., Introduction to Reinforcement Learning, MIT Press, 1998.
17. Saffiotti, A., Ruspini, E., and Konolige, K., Using Fuzzy Logic for Mobile Robot Control, Practical Applications of Fuzzy Technologies, H. J. Zimmermann, Editor, Kluwer Academic Publisher, 1999, pp. 185-206.
18. Thrun, S. B. Efficient Exploration In Reinforcement Learning, Tech. Rep. CMU-CS-92-102, 1992.
19. h Int. Conf. on Genetic Algorithms, Morgan Kaufmann, San Mateo, CA, 1991, pp345-353.
20. Watkins, C. J. and Dayan, P., Technical note: Q-learning, Machine Learning, 8(3/4), 1992, 323-339.