Learning Within the BDI Framework: An Empirical Analysis

  • Toan Phung
  • Michael Winikoff
  • Lin Padgham
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3683)


One of the limitations of the BDI (Belief-Desire-Intention) model is the lack of any explicit mechanisms within the architecture to be able to learn. In particular, BDI agents do not possess the ability to adapt based on past experience. This is important in dynamic environments since they can change, causing methods for achieving goals that worked well previously to become inefficient or ineffective. We present a model in which learning can be utilised by a BDI agent and verify this model experimentally using two learning algorithms.


Search Space Analogous Reasoning Inductive Logic Programming Knowledge Extractor High Order Function 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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Copyright information

© Springer-Verlag Berlin Heidelberg 2005

Authors and Affiliations

  • Toan Phung
    • 1
  • Michael Winikoff
    • 1
  • Lin Padgham
    • 1
  1. 1.School of Computer Science and ITRMIT UniversityMelbourneAustralia

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