Learning fuzzy classifier systems for multi-agent coordination

Abstract We present ELF, a learning fuzzy classifier system (LFCS), and its application to the field of Learning Autonomous Agents. In particular, we will show how this kind of Reinforcement Learning systems can be successfully applied to learn both behaviors and their coordination for Autonomous Agents. We will discuss the importance of knowledge representation approach based on fuzzy sets to reduce the search space without losing the required precision. Moreover, we will show how we have applied ELF to learn the distributed coordination among agents which can exchange information with each other. The experimental validation has been done on software agents interacting in a real-time task.

[1]  Philip R. Thrift,et al.  Fuzzy Logic Synthesis with Genetic Algorithms , 1991, ICGA.

[2]  Stephen F. Smith,et al.  A learning system based on genetic adaptive algorithms , 1980 .

[3]  Alexandre Parodi,et al.  A New Approach to Fuzzy Classifier Systems , 1993, ICGA.

[4]  D.E. Goldberg,et al.  Classifier Systems and Genetic Algorithms , 1989, Artif. Intell..

[5]  Rodney A. Brooks,et al.  A Robust Layered Control Syste For A Mobile Robot , 2022 .

[6]  Andrea Bonarini,et al.  Anytime Learning and Adaptation of Structured Fuzzy Behaviors , 1997, Adapt. Behav..

[7]  Charles L. Karr,et al.  Improved Fuzzy Process Control of Spacecraft Autonomous Rendezvous Using a Genetic Algorithm , 1990, Other Conferences.

[8]  A. Koller,et al.  Speech Acts: An Essay in the Philosophy of Language , 1969 .

[9]  Andrea Bonarini,et al.  Learning to compose fuzzy behaviors for autonomous agents , 1997, Int. J. Approx. Reason..

[10]  Andrea Bonarini,et al.  An approach to the design of reinforcement functions in real world, agent-based applications , 2001, IEEE Trans. Syst. Man Cybern. Part B.

[11]  John H. Holland,et al.  COGNITIVE SYSTEMS BASED ON ADAPTIVE ALGORITHMS1 , 1978 .

[12]  Maja J. Mataric,et al.  Reinforcement Learning in the Multi-Robot Domain , 1997, Auton. Robots.

[13]  Manuel Valenzuela-Rendón,et al.  The Fuzzy Classifier System: A Classifier System for Continuously Varying Variables , 1991, ICGA.

[14]  Donald A. Waterman,et al.  Pattern-Directed Inference Systems , 1981, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[15]  Andrew W. Moore,et al.  Reinforcement Learning: A Survey , 1996, J. Artif. Intell. Res..

[16]  Alistair Munro,et al.  Evolving fuzzy rule based controllers using genetic algorithms , 1996, Fuzzy Sets Syst..

[17]  Maja J. Mataric,et al.  Using Communication to Reduce Locality in Multi-Robot Learning , 1997, AAAI/IAAI.

[18]  Andrea Bonarini,et al.  Evolutionary Learning of Fuzzy rules: competition and cooperation , 1996 .

[19]  Lashon B. Booker,et al.  Proceedings of the fourth international conference on Genetic algorithms , 1991 .

[20]  Stewart W. Wilson.  Classifier Fitness Based on Accuracy , 1995, Evolutionary Computation.

[21]  Andrea Bonarini.  Reinforcement distribution for fuzzy classifiers: a methodology to extend crisp algorithms , 1998, 1998 IEEE International Conference on Evolutionary Computation Proceedings. IEEE World Congress on Computational Intelligence (Cat. No.98TH8360).

[22]  J. David Schaffer,et al.  Proceedings of the third international conference on Genetic algorithms , 1989 .

[23]  Hiroaki Kitano,et al.  RoboCup: Today and Tomorrow - What we have learned , 1999, Artif. Intell..