Learning within the BDI framework: an empirical analysis

Authors:
Toan Phung;Michael Winikoff;Lin Padgham
Affiliations:
School of Computer Science and IT, RMIT University, Melbourne, Australia;School of Computer Science and IT, RMIT University, Melbourne, Australia;School of Computer Science and IT, RMIT University, Melbourne, Australia
Venue:
KES'05 Proceedings of the 9th international conference on Knowledge-Based Intelligent Information and Engineering Systems - Volume Part III
Year:
2005

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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.