DocumentCode
2414521
Title
Learning to predict with the Delayed Action Classifier System
Author
Carse, Brian ; Pipe, Anthony G.
Author_Institution
Intelligent Autonomous Syst. Lab., West of England Univ., Bristol, UK
fYear
2003
fDate
8-8 Oct. 2003
Firstpage
93
Lastpage
98
Abstract
This paper describes an extended version of the Delayed Action Classifier System (DACS), a rule-based system which employs the genetic algorithm to discover temporal rules for learning in environments with temporal structure. The extended version of DACS (called DACS2) is described and experimentally evaluated. A mathematical analysis is provided which offers a theoretical justification of the advantages of DACS compared to non-temporal classifier systems. Areas for further development are briefly discussed and possible applications of the system in the field of intelligent control are suggested.
Keywords
genetic algorithms; intelligent control; learning (artificial intelligence); mathematical analysis; temporal reasoning; DACS2; delayed action classifier system; genetic algorithm; intelligent control; learning; mathematical analysis; nontemporal classifier systems; rule based system; temporal rules; temporal structure;
fLanguage
English
Publisher
ieee
Conference_Titel
Intelligent Control. 2003 IEEE International Symposium on
Conference_Location
Houston, TX, USA
ISSN
2158-9860
Print_ISBN
0-7803-7891-1
Type
conf
DOI
10.1109/ISIC.2003.1253920
Filename
1253920
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