Quantifying the Progress of Goals in Intelligent Agents

Deliberation over goals is a fundamental feature of intelligent agent systems. In this article we provide pragmatic but principled mechanisms for quantifying the level of completeness of goals in a Belief-Desire-Intention (BDI) agent. Our approach leverages previous work on resource and effects summarization which we extend by accommodating both dynamic resource summaries and goal effects, while also allowing a non-binary quantification of goal completeness. We treat both goals of accomplishment (achievement goals) and goals of monitoring (maintenance goals). We reconcile such practical computation of progress estimates of goals of both types with an earlier theoretical perspective on rnBDI goal completeness, and thus extend the theoretical framework to include maintenance goals. Our computational mechanisms have been implemented in the abstract agent language CAN. We also provide a case study in an autonomous rover domain.