Incremental Case-Based Plan Recognition Using State Indices

We describe a case-based approach to the keyhole plan-recognition task where the observed agent is a state-space planner whose world states can be monitored. Case-based approach provides means for automatically constructing the plan library from observations, minimizing the number of extraneous plans in the library. We show that the knowledge about the states of the observed agent's world can be effectively used to recognize agent's plans and goals, given no direct knowledge about the planner's internal decision cycle. Cases (plans) containing state knowledge enable the recognizer to cope with novel situations for which no plans exist in the plan library, and to further assist in effective discrimination among competing plan hypothesis.