Probabilistic reasoning in intelligent systems: networks of plausible inference
Probabilistic reasoning in intelligent systems: networks of plausible inference
Plan Recognition in Stories and in Life
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Dynamic construction of belief networks
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Qualtitative propagation and scenario-based scheme for exploiting probabilistic reasoning
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A Probabilistic Analysis of Marker-Passing Techniques for Plan Recognition
A Probabilistic Analysis of Marker-Passing Techniques for Plan Recognition
A Probabilistic Approach to Language Understanding
A Probabilistic Approach to Language Understanding
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A probabilistic network of predicates
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Plan recognition by program execution in continuous temporal domains
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Discriminative learning of first-order weighted abduction from partial discourse explanations
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AI Communications
Intelligent Decision Technologies
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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.