Describing plan recognition as non-monotonic reasoning and belief revision.

摘要

We provide a characterization of plan recognition in terms of a general framework of belief revision and non-monotonic reasoning. We adopt a generalization of classical belief revision to describe a competence model of plan recognition which supports dynamic change to all aspects of a plan recognition knowledge base, including background knowledge, action descriptions and their relationship to named plans, and accumulating sets of observations on agent actions.; Our plan recognition model exploits the underlying belief revision model to assimilate observations, and answer queries about an agent's intended plans and actions. Supporting belief states are determined by observed actions and non-monotonic assumptions consistent with background knowledge and action descriptions.; We use a situation calculus notation to describe plans and actions, together with a small repertoire of meta predicates which are used to specify observations to the belief revision system, and to query the reasoning system regarding the current status of plans and predictable actions.; Our intent is to demonstrate the connections between a general plan recognition model and important concepts of belief revision and non-monotonic reasoning, to help establish a basis for improving the specification and development of specialized plan recognition systems.