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 as they can change, causing previously successful methods for achieving goals to become inefficient or ineffective. This theis present a model in which learning, analogous reasoning, data pruning and learner accuracy evaluation can be utilised by a BDI agent and verify this model experimentally using Inductive and Statistical learning. |
