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. We 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. Intelligent Agents are a new way of developing software applications. They are an amalgam of Artificial Intelligence (AI) and Software Engineering concepts that are highly suited to domains that are inherently complex and dynamic. Agents are software entities that are autonomous, reactive, proactive, situated and social. They are autonomous in that they are able to make decisions on their own volition. They are situated in some environment and are reactive to this environment yet are also capable of proactive behaviour where they actively pursue goals. They are capable of social behaviour where communication can occur between agents. BDI (Belief Desire Intention) agents are one popular type of agent that support complex behaviour in dynamic environments. Agent adaptation can be viewed as the process of changing the way in which an agent achieves its goals. We distinguish between 'reactive' or short-term adaptation, 'long-term' or historical adaptation and 'very long term' or evolutionary adaptation. Short-term adaptation, an ability that current BDI agents already possess, involves reacting to changes in the environment and choosing alternative plans of action which may involve choosing new plans if the current plan fails. 'Long-term' or historical adaptation entails the use of past cases during the reasoning process which enables agents to avoid repeating past mistak es. 'Evolutionary adaptation' could involve the use of genetic programming or similar techniques to mutate plans to lead to altered behaviour. Our work aims to improve BDI agents by introducing a framework that allows BDI agents to alter their behaviour based on past experience, i.e. to learn.