A Probabilistic Model of Plan Recognition

Eugene Charniak, Robert Goldman

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 best 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.

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