A formal theory of plan recognition
A formal theory of plan recognition
Abductive inference models for diagnostic problem-solving
Abductive inference models for diagnostic problem-solving
Decision analysis and expert systems
AI Magazine
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
UAI '89 Proceedings of the Fifth Annual Conference on Uncertainty in Artificial Intelligence
Integrating probabilistic, taxonomic and causal knowledge in abductive diagnosis
UAI '90 Proceedings of the Sixth Annual Conference on Uncertainty in Artificial Intelligence
Dynamic construction of belief networks
UAI '90 Proceedings of the Sixth Annual Conference on Uncertainty in Artificial Intelligence
Representing and reasoning with probabilistic knowledge
Representing and reasoning with probabilistic knowledge
Obvious abduction
A message passing algorithm for plan recognition
IJCAI'91 Proceedings of the 12th international joint conference on Artificial intelligence - Volume 1
On the role of coherence in abductive explanation
AAAI'90 Proceedings of the eighth National conference on Artificial intelligence - Volume 1
A probabilistic model of plan recognition
AAAI'91 Proceedings of the ninth National conference on Artificial intelligence - Volume 1
Explanation, irrelevance and statistical independence
AAAI'91 Proceedings of the ninth National conference on Artificial intelligence - Volume 1
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Bayesian networks are directed acyclic graphs representing independence relationships among a set of random variables. A random variable can be regarded as a set of exhaustive and mutually exclusive propositions. We argue that there are several drawbacks resulting from the propositional nature and acyclic structure of Bayesian networks. To remedy these shortcomings, we propose a probabilistic network where nodes represent unary predicates and which may contain directed cycles. The proposed representation allows us to represent domain knowledge in a single static network even though we cannot determine the instantiations of the predicates before hand. The ability to deal with cycles also enables us to handle cyclic causal tendencies and to recognize recursive plans.