Artificial Intelligence
Distributed revision of composite beliefs
Artificial Intelligence
Nonmonotonic logic and temporal projection
Artificial Intelligence
Foundations of logic programming; (2nd extended ed.)
Foundations of logic programming; (2nd extended ed.)
Embracing causality in fault reasoning
Artificial Intelligence
Probabilistic reasoning in intelligent systems: networks of plausible inference
Probabilistic reasoning in intelligent systems: networks of plausible inference
Explanation and prediction: an architecture for default and abductive reasoning
Computational Intelligence
Assumptions, beliefs and probabilities
Artificial Intelligence
Abductive inference models for diagnostic problem-solving
Abductive inference models for diagnostic problem-solving
What is the most likely diagnosis?
UAI '90 Proceedings of the Sixth Annual Conference on Uncertainty in Artificial Intelligence
A new algorithm for finding MAP assignments to belief networks
UAI '90 Proceedings of the Sixth Annual Conference on Uncertainty in Artificial Intelligence
An analysis of ATMS-based techniques for computing Dempster-Shafer belief functions
IJCAI'89 Proceedings of the 11th international joint conference on Artificial intelligence - Volume 2
Diagnosis with behavioral modes
IJCAI'89 Proceedings of the 11th international joint conference on Artificial intelligence - Volume 2
Representing diagnostic knowledge for probabilistic Horn abduction
IJCAI'91 Proceedings of the 12th international joint conference on Artificial intelligence - Volume 2
Probabilistic semantics for cost based abduction
AAAI'90 Proceedings of the eighth National conference on Artificial intelligence - Volume 1
Context Hypothesization Using Probabilistic Knowledge
Fundamenta Informaticae
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This paper presents a simple framework for Horn clause abduction, with probabilities associated with hypotheses. It is shown how this representation can represent any probabilistic knowledge representable in a Bayesian belief network. The main contributions are in finding a relationship between logical and probabilistic notions of evidential reasoning. This can be used as a basis for a new way to implement Bayesian Networks that allows for approximations to the value of the posterior probabilities, and also points to a way that Bayesian networks can be extended beyond a propositional language.