Learning in BDI Multi-agent Systems

Abstract : This paper deals with the issue of learning in multi-agent systems (MAS). Particularly, we are interested in BDI (Belief, Desire, Intention) agents. Despite the relevance of the BDI model of rational agency, little work has been done to deal with its two main limitations: i) The lack of learning competences; and ii) The lack of explicit multi-agent functionality. From the multi-agent learning perspective, we propose a BDI agent architecture extended with learning competences for MAS context. Induction of Logical Decision Trees, a first order method, is used to enable agents to learn when their plans are successfully executable. Our implementation enables multiple agents executed as parallel functions in a single Lisp image. In addition, our approach maintains consistency between learning and the theory of practical reasoning.
Type de document :
Communication dans un congrès
CLIMA 2004 - 4th International Workshop on Computational Logic in Multi-Agent Systems, Jan 2004, Fort Lauderdale, FL, United States. Springer-Verlag, CLIMA 2004 - 4th International Workshop on Computational Logic in Multi-Agent Systems, 3259, pp.218-233, Lecture Notes in Computer Science. 〈10.1007/978-3-540-30200-1_12〉
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https://hal.archives-ouvertes.fr/hal-01496302
Contributeur : Lip6 Publications <>
Soumis le : lundi 27 mars 2017 - 13:46:18
Dernière modification le : jeudi 21 mars 2019 - 13:11:45

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Alejandro Guerra-Hernández, Amal El Fallah Seghrouchni, Henry Soldano. Learning in BDI Multi-agent Systems. CLIMA 2004 - 4th International Workshop on Computational Logic in Multi-Agent Systems, Jan 2004, Fort Lauderdale, FL, United States. Springer-Verlag, CLIMA 2004 - 4th International Workshop on Computational Logic in Multi-Agent Systems, 3259, pp.218-233, Lecture Notes in Computer Science. 〈10.1007/978-3-540-30200-1_12〉. 〈hal-01496302〉

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