Let the System Learn a Game: How Can FCA Optimize a Cognitive Memory Structure

Abstract : The goal of this article is to study the contribution of FCA (Formal Concepts Analysis) to (1) optimize (2) organize (3) discover new concepts or a better operation of the semantic memory of an Artificial Intelligence (AI) system based on a cognitive approach. The system has been applied to game modeling (here the Reversi board game), since games are a very good experimental field for performance evaluation. After describing the COGITO project, which tries to assess the pros and cons of cognitive modeling over pure operational but non explicative paradigms in games modeling, the paper stresses out the benefits of FCA in providing a better abstraction, and a more reliable way to handle conflictual knowledge.
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Communication dans un congrès
ECAI'2012: European Conference on Artificial Intelligence, Aug 2012, Montpellier, France. pp.001-009, 2012, 〈http://www2.lirmm.fr/ecai2012/index.php?option=com_content&view=article&id=80&Itemid=84〉
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Contributeur : Violaine Prince-Barbier <>
Soumis le : mercredi 12 décembre 2012 - 11:45:17
Dernière modification le : jeudi 11 janvier 2018 - 02:06:42
Document(s) archivé(s) le : mercredi 13 mars 2013 - 03:52:44

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William Dyce, Thibaud Marmin, Namrata Patel, Clément Sipieter, Guillaume Tisserant, et al.. Let the System Learn a Game: How Can FCA Optimize a Cognitive Memory Structure. ECAI'2012: European Conference on Artificial Intelligence, Aug 2012, Montpellier, France. pp.001-009, 2012, 〈http://www2.lirmm.fr/ecai2012/index.php?option=com_content&view=article&id=80&Itemid=84〉. 〈lirmm-00764071〉

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