untitled
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<identifier>oai:HAL:hal-00520607v1</identifier>
<datestamp>2017-12-21</datestamp>
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<publisher>HAL CCSD</publisher>
<title lang=en>Adapted Pittsburgh classifier system: building accurate strategies in non markovian environments</title>
<creator>Gilles, Enée</creator>
<creator>Peroumalnaïk, Mathias</creator>
<contributor>Groupe de Recherche en Informatique et Mathématiques Appliquées Antilles-Guyane (GRIMAAG) ; Université des Antilles et de la Guyane (UAG)</contributor>
<description>International audience</description>
<source>Proceedings of the 2008 GECCO conference companion on Genetic and evolutionary computation</source>
<source>Genetic and Evolutionary Computation Conference</source>
<coverage>Atlanta, GA, United States</coverage>
<contributor>ACM New York, NY, USA</contributor>
<publisher>ACM New York, NY, USA</publisher>
<identifier>hal-00520607</identifier>
<identifier>https://hal.archives-ouvertes.fr/hal-00520607</identifier>
<source>https://hal.archives-ouvertes.fr/hal-00520607</source>
<source>ACM New York, NY, USA. Genetic and Evolutionary Computation Conference, Jul 2008, Atlanta, GA, United States. ACM New York, NY, USA, pp.2001-2008, 2008, 〈10.1145/1388969.1389013〉</source>
<identifier>DOI : 10.1145/1388969.1389013</identifier>
<relation>info:eu-repo/semantics/altIdentifier/doi/10.1145/1388969.1389013</relation>
<language>en</language>
<subject lang=en>APCS</subject>
<subject lang=en>XCS</subject>
<subject lang=en>classifier system</subject>
<subject lang=en>non markovian multistep environment</subject>
<subject lang=en>strategy</subject>
<subject>I.2.6 Concept Learning, Knowledge Acquisition</subject>
<subject>[INFO.INFO-LG] Computer Science [cs]/Machine Learning [cs.LG]</subject>
<subject>[INFO.INFO-AI] Computer Science [cs]/Artificial Intelligence [cs.AI]</subject>
<type>info:eu-repo/semantics/conferenceObject</type>
<type>Conference papers</type>
<description lang=en>This paper focuses on the study of the behavior of a genetic algorithm based classifier system, the Adapted Pittsburgh Classifier System (A.P.C.S), on maze type environments containing aliasing squares. This type of environment is often used in reinforcement learning litterature to assess the performances of learning methods when facing problems containing non markov situations. Through this study, we discuss on the performances of the APCS on maze type environments and also of the efficiency of an improvement of the APCS learning method inspired from the XCS : the covering mechanism. We manage to show that, without any memory mechanism, the APCS is able to build and to keep accurate strategies to produce regular sub-optimal solutions to these maze problem. This statement is shown through a comparison of the results obtained by the XCS, XCSM and XCSMH on distinct maze problems with these obtained by the APCS.</description>
<date>2008-07-12</date>
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