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<identifier>oai:HAL:hal-00520608v1</identifier>
<datestamp>2017-12-21</datestamp>
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<publisher>HAL CCSD</publisher>
<title lang=en>Towards Self-Adjustment of Adapted Pittsburgh Classifier System Cognitive Capacity on Multi-Step Problems</title>
<creator>Peroumalnaïk, Mathias</creator>
<creator>Gilles, Enée</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>2008 Eighth International Conference on Hybrid Intelligent Systems</source>
<source>2008 Eighth International Conference on Hybrid Intelligent Systems</source>
<coverage>Barcelone, Spain</coverage>
<identifier>hal-00520608</identifier>
<identifier>https://hal.archives-ouvertes.fr/hal-00520608</identifier>
<source>https://hal.archives-ouvertes.fr/hal-00520608</source>
<source>2008 Eighth International Conference on Hybrid Intelligent Systems, Sep 2008, Barcelone, Spain. pp.885-892, 2008, 〈10.1109/HIS.2008.105〉</source>
<identifier>DOI : 10.1109/HIS.2008.105</identifier>
<relation>info:eu-repo/semantics/altIdentifier/doi/10.1109/HIS.2008.105</relation>
<language>en</language>
<subject lang=en>Adapted Pittsburgh Classifier System</subject>
<subject lang=en>covering</subject>
<subject lang=en>cognitive capacity</subject>
<subject lang=en>non-markovian multistep environments</subject>
<subject>[INFO.INFO-LG] Computer Science [cs]/Machine Learning [cs.LG]</subject>
<type>info:eu-repo/semantics/conferenceObject</type>
<type>Conference papers</type>
<description lang=en>This paper focuses on the study of the influence of a newly implemented mechanism on a Pittsburgh-like classifier system. The Adapted Pittsburgh Classifier System is a learning classifier system that uses genetic algorithms to evolve its ruleset. The new mechanism discussed is inspired from Wilson work on the eXtended Classifier System (XCS): it allows the concerned LCS to adapt its rule set when facing a new signal by modifying an existing rule. This mechanism is called covering mechanism due to the fact that the rule is covered up by a new rule which is sensitive to this new signal. Effects of this covering mechanism are first measured on the performances of APCS on well known multi-step environments: maze-type environments (Woods 101 and E2). In addition, further measures presented in this paper indicate that we could possibly rely on the behavior of this covering mechanism to automate the research of a correct cognitive capacity needed by the APCS to solve a given multistep problem.</description>
<date>2008-09-10</date>
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