Learning in BDI multi-agent systems

  • Authors:

  • Alejandro Guerra-Hernández;Amal El Fallah-Seghrouchni;Henry Soldano

  • Affiliations:

  • Laboratoire d'Informatique de Paris Nord, UMR 7030 – CNRS, Université Paris 13, Villetaneuese, France;Laboratoire d'Informatique de Paris 6, UMR 7606 – CNRS, Université Paris 6, Paris, France;Laboratoire d'Informatique de Paris Nord, UMR 7030 – CNRS, Université Paris 13, Villetaneuese, France

  • Venue:
  • CLIMA IV'04 Proceedings of the 4th international conference on Computational Logic in Multi-Agent Systems
  • Year:
  • 2004

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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.