Learning anticipatory behaviour using a delayed action classifier system

Abstract

To manifest anticipatory behaviour that goes beyond simple stimulus-response, classifier systems must evolve internal reasoning processes based on couplings via internal messages. A major challenge that has been encountered in engendering internal reasoning processes in classifier systems has been the discovery and maintenance of long classifier chains. This paper proposes a modified version of the traditional classifier system, called the delayed action classifier system (DACS), devised specifically for learning of anticipatory or predictive behaviour. DACS operates by delaying the action (i.e. posting of messages) of appropriately tagged, matched classifiers by a number of execution cycles which is encoded on the classifier. Since classifier delays are encoded on the classifier genome, a GA is able to explore simultaneously the spaces of actions and delays. Results of experiments comparing DACS to a traditional classifier system in terms of the dynamics of classifier reinforcement and system performance using the bucket brigade are presented and examined. Experiments comparing DACS with a traditional classifier system, which appear encouraging, for a simple prediction problem are described and considered. Areas for further work using the delayed-action classifier notion are suggested and briefly discussed.KeywordsGenetic AlgorithmClassifier SystemTraditional ClassifierMarginal PayoffLetter SequenceThese keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.