A probabilistic model of plan recognition
Eugene Charniak, Robert P. Goldman · National Conference on Artificial Intelligence · 1991
Plan-recognition requires the construction of possible plans which could explain a set of observed actions, and then selecting one or more of them as providing the belt explanation. In this paper we present a formal model of the latter process based upon probability theory. Our model consists of a knowledge-base of facts about the world expressed in a first-order language, and rules for using that knowledge-base to construct a Bayesian network. The network is then evaluated to find the plans with the highest probability.