Learning Within the BDI Framework: An Empirical Analysis | Zendy

B.T. Phung | Zendy; Michael Winikoff | Zendy; Lin Padgham | Zendy

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Learning Within the BDI Framework: An Empirical Analysis

Author(s) -

B.T. Phung,

Michael Winikoff,

Lin Padgham

Publication year - 2005

Publication title -

lecture notes in computer science

Language(s) - English

Resource type - Book series

SCImago Journal Rank - 0.249

H-Index - 400

eISSN - 1611-3349

pISSN - 0302-9743

ISBN - 3-540-28896-1

DOI - 10.1007/11553939_41

Subject(s) - computer science , artificial intelligence , architecture , human–computer interaction , machine learning , art , visual arts

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.

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