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Credit assignment and discovery in classifier systems
Author(s) -
Liepins G. E.,
Hilliard M. R.,
Palmer Mark,
Rangarajan Gita
Publication year - 1991
Publication title -
international journal of intelligent systems
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.291
H-Index - 87
eISSN - 1098-111X
pISSN - 0884-8173
DOI - 10.1002/int.4550060104
Subject(s) - classifier (uml) , computer science , artificial intelligence , machine learning , population , data mining , sociology , demography
Classifier systems are “discovery” production rule systems that utilize the genetic algorithm for discovery and allocate credit through the bucket brigade. For any given problem, the success of a classifier system depends on the choice of representation, the system's ability to attain reward or punishment states (evaluation states), accurate estimation of the relative merit of individual classifiers, and the genetic algorithm's ability to use information about the current population of rules to generate better rules. This article addresses the adequacy of the bucket brigade and backward averaging for credit assignment and reviews a preliminary study of two variants in conjunction with rules that are fully enumerated as well as with discovery. Potential difficulties with each of these methods are highlighted in several theoretical examples, including one from the literature. Preliminary results and tentative similarities between these hybrids and Sutton's Adaptive Heuristic Critic (AHC) are suggested.