NDLI: The anticipatory classifier system and genetic generalization

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The anticipatory classifier system and genetic generalization

Content Provider	Semantic Scholar
Author	Butz, Alexandra Goldberg, Brian D. Stolzmann, C. Wolfgang
Copyright Year	2004
Abstract	The anticipatory classifier system (ACS)combines the learning classifier system frameworkwith the cognitive learning theory ofanticipatory behavioral control. The result is an evolutionary system thatbuilds a complete and generalized predictiveenvironmental model. Reinforcement learningtechniques are applied to form a behavioralpolicy represented in the model. After providingsome background as well as outlining the objectives of the system, we explainin detail all involved current processes. Furthermore, we analyze thedeficiency of over-specialization in the anticipatory learning process (ALP),the main learning mechanism in the ACS. Consequently, we introduce a geneticalgorithm (GA) to the ACS that is meant for generalization of over-specializedclassifiers. We show that it is possible to form a symbiosis between a directedspecialization and a genetic generalization mechanism achieving a learningmechanism that evolves a complete, accurate, and compact description of theperceived environment. Results in three different environmental settingsconfirm the usefulness of the genetic algorithm in the ACS. Finally, we discuss future research directions.
Starting Page	427
Ending Page	467
Page Count	41
File Format	PDF HTM / HTML
DOI	10.1023/A:1021330114221
Volume Number	1
Alternate Webpage(s)	https://doi.org/10.1023/A%3A1021330114221
Journal	Natural Computing
Language	English
Access Restriction	Open
Content Type	Text
Resource Type	Article

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