Artificial Intelligence
A theory of diagnosis from first principles
Artificial Intelligence
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
A logical framework for depiction and image interpretation
Artificial Intelligence
Conditional Logics for Default Reasoning and Belief Revision
Conditional Logics for Default Reasoning and Belief Revision
Preferred subtheories: an extended logical framework for default reasoning
IJCAI'89 Proceedings of the 11th international joint conference on Artificial intelligence - Volume 2
A knowledge-level account of abduction
IJCAI'89 Proceedings of the 11th international joint conference on Artificial intelligence - Volume 2
Inaccessible worlds and irrelevance preliminary report
IJCAI'91 Proceedings of the 12th international joint conference on Artificial intelligence - Volume 1
Representing diagnostic knowledge for probabilistic Horn abduction
IJCAI'91 Proceedings of the 12th international joint conference on Artificial intelligence - Volume 2
Review: on the evolving relation between belief revision and argumentation
The Knowledge Engineering Review
The probability of a possibility: adding uncertainty to default rules
UAI'93 Proceedings of the Ninth international conference on Uncertainty in artificial intelligence
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We propose a natural model of abduction based on the revision of the epistemic state of an agent. We require that explanations be sufficient to induce belief in an observation in a manner that adequately accounts for factual and hypothetical observations. Our model will generate explanations that nonmonotonically predict an observation, thus generalizing most current accounts, which require some deductive relationship between explanation and observation. It also provides a natural preference ordering on explanations, defined in terms of normality or plausibility. We reconstruct the Theorist system in our framework, and show how it can be extended to accommodate our predictive explanations and semantic preferences on explanations.