Evolved cooperation and emergent communication structures in learning classifier based organic computing systems

In this paper we look at systems consisting of many autonomous components or agents which have only limited amount of resources (e.g. memory) but are able to communicate with each other. The aim of these systems is to solve classification problems (usually to classify binary strings). We incorporate a pittsburgh style learning classifier system into the agents and extend its possible actions by actions for passing the classification requests to other agents. We show that the system is able to overcome the limited resources of its parts by evolving cooperation between them. We take a deeper look at the structure of the generated rule sets and investigate the occurring communication patterns.

[1]  Stewart W. Wilson,et al.  An Incremental Multiplexer Problem and Its Uses in Classifier System Research , 2001, IWLCS.

[2]  Devon Dawson,et al.  Improving Performance in Size-Constrained Extended Classifier Systems , 2003, GECCO.

[3]  Jaume Bacardit Peñarroya.  Pittsburgh genetic-based machine learning in the data mining era: representations, generalization, and run-time , 2004 .

[4]  Stewart W. Wilson,et al.  Learning classifier systems: a survey , 2007, Soft Comput..

[5]  Takao Terano,et al.  Learning Classifier Systems Meet Multiagent Environments , 2000, IWLCS.

[6]  Kenneth A. De Jong,et al.  Learning Concept Classification Rules Using Genetic Algorithms , 1991, IJCAI.

[7]  Larry Bull,et al.  On Evolving Social Systems: Communication, Speciation and Symbiogenesis , 1999, Comput. Math. Organ. Theory.

[8]  John H. Holland,et al.  Adaptation in Natural and Artificial Systems: An Introductory Analysis with Applications to Biology, Control, and Artificial Intelligence , 1992 .

[9]  John J. Grefenstette,et al.  A Coevolutionary Approach to Learning Sequential Decision Rules , 1995, ICGA.

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

[11]  Hartmut Schmeck,et al.  Organic Computing - A New Vision for Distributed Embedded Systems , 2005, ISORC.

[12]  Larry Bull,et al.  Coevolutionary computation: an introduction , 1998 .

[13]  R. Rivest.  Learning Decision Lists , 1987, Machine Learning.

[14]  Stewart W. Wilson.  ZCS: A Zeroth Level Classifier System , 1994, Evolutionary Computation.

[15]  Kenneth A. De Jong,et al.  Cooperative Coevolution: An Architecture for Evolving Coadapted Subcomponents , 2000, Evolutionary Computation.

[16]  Kenneth A. De Jong,et al.  Using genetic algorithms for concept learning , 1993, Machine Learning.

[17]  Sean Luke,et al.  Cooperative Multi-Agent Learning: The State of the Art , 2005, Autonomous Agents and Multi-Agent Systems.