caa a22 4500 467954666 CHVBK 20180323165703.0 cr unu---uuuuu 170328e19871101xx s 000 0 eng 10.1023/A:1022655214215 doi (NATIONALLICENCE)springer-10.1023/A:1022655214215 (NATIONALLICENCE)springer-10.1007/BF00058679 Wilson Stewart The Rowland Institute for Science, 100 Cambridge Parkway, 02142, Cambridge, Massachusetts, USA aut Classifier Systems and the Animat Problem [Elektronische Daten] [Stewart Wilson] 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. Kluwer Academic Publishers, 1987 Classifier systems nationallicence incremental learning nationallicence disjunctive concepts nationallicence payoff nationallicence animal learning nationallicence genetic algorithm nationallicence Machine Learning Kluwer Academic Publishers-Plenum Publishers 2/3(1987-11-01), 199-228 0885-6125 2:3<199 1987 2 10994 https://doi.org/10.1023/A:1022655214215 text/html Onlinezugriff via DOI 1 research-article jats NATIONALLICENCE 856 40 https://doi.org/10.1023/A:1022655214215 text/html Onlinezugriff via DOI NATIONALLICENCE 100 1- Wilson Stewart The Rowland Institute for Science, 100 Cambridge Parkway, 02142, Cambridge, Massachusetts, USA aut NATIONALLICENCE 773 0- Machine Learning Kluwer Academic Publishers-Plenum Publishers 2/3(1987-11-01), 199-228 0885-6125 2:3<199 1987 2 10994 NATIONALLICENCE 856 40 https://doi.org/10.1007/BF00058679 text/html Onlinezugriff via DOI NATIONALLICENCE 100 1- Wilson Stewart The Rowland Institute for Science, 100 Cambridge Parkway, 02142, Cambridge, Massachusetts, U.S.A aut NATIONALLICENCE 773 0- Machine Learning Kluwer Academic Publishers 2/3(1987-11-01), 199-228 0885-6125 2:3<199 1987 2 10994 Metadata rights reserved Springer special CC-BY-NC licence nationallicence BK010053 XK010053 XK010000 NATIONALLICENCE NATIONALLICENCE NL-springer NATIONALLICENCE NATIONALLICENCE NL-springer