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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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
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