The agent technology arises as a solution that provides flexibility and robustness to address dynamic and complex domains. Such flexibility can be achieved by the adoption of existing agent-based approaches, such as the BDI architecture, which provides agents with the mental attitudes of beliefs, desires and intentions. This architecture is highly customisable, leaving gaps to be fulfilled in particular applications. One of these gaps is the plan selection algorithm that is responsible for selecting a plan to be executed by an agent to achieve a goal, having an important influence on the overall agent performance. Thus, in this paper, we propose a plan selection approach, which is able to learn plans that provide possibly best outcomes, based on a current context and agent's preferences. Our approach is composed of a meta-model, which must be instantiated to specify plan metadata, and a technique that uses such metadata to learn and predict plan outcomes. We evaluated our approach experimentally, and results indicate it is effective.
['João Guilherme Faccin', 'Ingrid Nunes']
BDI-Agent Plan Selection Based on Prediction of Plan Outcomes