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Learning Within the BDI Framework: An Empirical Analysis

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Knowledge-Based Intelligent Information and Engineering Systems (KES 2005)

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.

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© 2005 Springer-Verlag Berlin Heidelberg

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Phung, T., Winikoff, M., Padgham, L. (2005). Learning Within the BDI Framework: An Empirical Analysis. In: Khosla, R., Howlett, R.J., Jain, L.C. (eds) Knowledge-Based Intelligent Information and Engineering Systems. KES 2005. Lecture Notes in Computer Science(), vol 3683. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11553939_41

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  • DOI: https://doi.org/10.1007/11553939_41

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-28896-1

  • Online ISBN: 978-3-540-31990-0

  • eBook Packages: Computer ScienceComputer Science (R0)

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