Probabilistic reasoning in intelligent systems: networks of plausible inference
Probabilistic reasoning in intelligent systems: networks of plausible inference
Nonmonotonic reasoning, preferential models and cumulative logics
Artificial Intelligence
Epistemic entrenchment and possibilistic logic
Artificial Intelligence
What does a conditional knowledge base entail?
Artificial Intelligence
Nonmonotonic inference based on expectations
Artificial Intelligence
Handbook of logic in artificial intelligence and logic programming (vol. 3)
Qualitative probabilities for default reasoning, belief revision, and causal modeling
Artificial Intelligence
Nonmonotonic reasoning, conditional objects and possibility theory
Artificial Intelligence
A general non-probabilistic theory of inductive reasoning
UAI '88 Proceedings of the Fourth Annual Conference on Uncertainty in Artificial Intelligence
Extending the Maximum Entropy Approach to Variable Strength Defaults
Annals of Mathematics and Artificial Intelligence
Artificial Intelligence - Special issue on logical formalizations and commonsense reasoning
System Z: a natural ordering of defaults with tractable applications to nonmonotonic reasoning
TARK '90 Proceedings of the 3rd conference on Theoretical aspects of reasoning about knowledge
Weak nonmonotonic probabilistic logics
Artificial Intelligence
Possibilistic uncertainty handling for answer set programming
Annals of Mathematics and Artificial Intelligence
Defeasible specifications in action theories
IJCAI'97 Proceedings of the Fifteenth international joint conference on Artifical intelligence - Volume 2
Causal theories of action: a computational core
IJCAI'03 Proceedings of the 18th international joint conference on Artificial intelligence
A possibilistic inconsistency handling in answer set programming
ECSQARU'05 Proceedings of the 8th European conference on Symbolic and Quantitative Approaches to Reasoning with Uncertainty
Dealing Automatically with Exceptions by Introducing Specificity in ASP
ECSQARU '09 Proceedings of the 10th European Conference on Symbolic and Quantitative Approaches to Reasoning with Uncertainty
Efficient policy-based inconsistency management in relational knowledge bases
SUM'10 Proceedings of the 4th international conference on Scalable uncertainty management
Handling exceptions in logic programming without negation as failure
ECSQARU'11 Proceedings of the 11th European conference on Symbolic and quantitative approaches to reasoning with uncertainty
Dealing with explicit preferences and uncertainty in answer set programming
Annals of Mathematics and Artificial Intelligence
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Default rules express concise pieces of knowledge having implicit exceptions, which is appropriate for reasoning under incomplete information. Specific rules that explicitly refer to exceptions of more general default rules can then be handled in a non-monotonic setting. However, there is no assessment of the certainty with which the conclusion of a default rule holds when it applies. We propose a formalism in which uncertain default rules can be expressed, but still preserving the distinction between the defeasibility and uncertainty semantics by means of a two steps processing. Possibility theory is used for representing both uncertainty and defeasibility. The approach is illustrated in persistence modeling problems.