Classifier systems and the animat problem View Full Text


Ontology type: schema:ScholarlyArticle      Open Access: True


Article Info

DATE

1987-11

AUTHORS

Stewart W. Wilson

ABSTRACT

This paper characterizes and investigates, from the perspective of machine learning and, particularly, classifier systems, the learning problem faced by animals and autonomous robots (here collectively termed animats). We suggest that, to survive in their environments, animats must in effect learn multiple disjunctive concepts incrementally under payoff (needs-satisfying) feedback. A review of machine learning techniques indicates that most relax at least one of these constraints. In theory, classifier systems satisfy the constraints, but tests have been limited. We show how the standard classifier system model applies to the animat learning problem. Then, in the experimental part of the paper, we specialize the model and test it in a problem environment satisfying the constraints and consisting of a difficult, disjunctive Boolean function drawn from the machine learning literature. Results include: learning the function in significantly fewer trials than a neural-network method; learning under payoff regimes that include both noisy payoff and partial reward for suboptimal performance; demonstration, in a classifier system, of a theoretically predicted property of genetic algorithms: the superiority of crossovers to point mutations; and automatic control of variation (search) rate based on system entropy. We conclude that the results support the classifier system approach to the animat problem, but suggest work aimed at the emergence of behavioral hierarchies of classifiers to offset slower learning rates in larger problems. More... »

PAGES

199-228

Identifiers

URI

http://scigraph.springernature.com/pub.10.1007/bf00058679

DOI

http://dx.doi.org/10.1007/bf00058679

DIMENSIONS

https://app.dimensions.ai/details/publication/pub.1049788334


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