Reflexive anticipatory reasoning by BDI agents

This paper investigates how predictions about the future behaviour of an agent can be exploited to improve its decision-making in the present. Future states are foreseen by a simulation technique, which is based on models of both the environment and the agent. Although the environment model is usually taken into account for prediction in artificial intelligence (e.g., in automated planning), the agent model receives less attention. We leverage the agent model to speed up the simulation and as a source of alternative decisions. Our proposal bases the agent model on the practical knowledge the developer has given to the agent, especially in the case of BDI agents. This knowledge is thus exploited in the proposed future-concerned reasoning mechanisms. We present a prototype implementation of our approach as well as the results from its evaluation on static and dynamic environments. This allows us to better understand the relation between the improvement in agent decisions and the quality of the knowledge provided by the developer.