Chapter

Knowledge-Based Intelligent Information and Engineering Systems

Volume 3683 of the series Lecture Notes in Computer Science pp 282-288

Learning Within the BDI Framework: An Empirical Analysis

  • Toan PhungAffiliated withSchool of Computer Science and IT, RMIT University
  • , Michael WinikoffAffiliated withSchool of Computer Science and IT, RMIT University
  • , Lin PadghamAffiliated withSchool of Computer Science and IT, RMIT University

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Abstract

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.