Artificial Intelligence
A theory of diagnosis from first principles
Artificial Intelligence
Artificial Intelligence
Reasoning about action I: a possible worlds approach
Artificial Intelligence
A logical framework for default reasoning
Artificial Intelligence
Probabilistic reasoning in intelligent systems: networks of plausible inference
Probabilistic reasoning in intelligent systems: networks of plausible inference
Assumptions, beliefs and probabilities
Artificial Intelligence
Nonmonotonic reasoning, preferential models and cumulative logics
Artificial Intelligence
Abductive inference models for diagnostic problem-solving
Abductive inference models for diagnostic problem-solving
Using crude probability estimates to guide diagnosis
Artificial Intelligence
A knowledge level analysis of belief revision
Proceedings of the first international conference on Principles of knowledge representation and reasoning
Defaults and probabilities: extensions and coherence
Proceedings of the first international conference on Principles of knowledge representation and reasoning
The computational complexity of abduction
Artificial Intelligence - Special issue on knowledge representation
A spectrum of logical definitions of model-based diagnosis
Computational Intelligence
Characterizing diagnoses and systems
Artificial Intelligence
Default Reasoning: Causal and Conditional Theories
Default Reasoning: Causal and Conditional Theories
On the semantics of updates in databases
PODS '83 Proceedings of the 2nd ACM SIGACT-SIGMOD symposium on Principles of database systems
Probability of Deductibility and Belief Functions
ECSQARU '93 Proceedings of the European Conference on Symbolic and Quantitative Approaches to Reasoning and Uncertainty
What is the most likely diagnosis?
UAI '90 Proceedings of the Sixth Annual Conference on Uncertainty in Artificial Intelligence
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
Preferred subtheories: an extended logical framework for default reasoning
IJCAI'89 Proceedings of the 11th international joint conference on Artificial intelligence - Volume 2
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
Normality and faults in logic-based diagnosis
IJCAI'89 Proceedings of the 11th international joint conference on Artificial intelligence - Volume 2
Inconsistency management and prioritized syntax-based entailment
IJCAI'93 Proceedings of the 13th international joint conference on Artifical intelligence - Volume 1
Prioritized defaults: implementation by TMS and application to diagnosis
IJCAI'91 Proceedings of the 12th international joint conference on Artificial intelligence - Volume 1
A maximum entropy approach to nonmonotonic reasoning
AAAI'90 Proceedings of the eighth National conference on Artificial intelligence - Volume 1
System-Z+: a formalism for reasoning with variable-strength defaults
AAAI'91 Proceedings of the ninth National conference on Artificial intelligence - Volume 1
Argumentative inference in uncertain and inconsistent knowledge bases
UAI'93 Proceedings of the Ninth international conference on Uncertainty in artificial intelligence
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We view the syntax-based approaches to default reasoning as a model-based diagnosis problem, where each source giving a piece of information is considered as a component. It is formalized in the ATMS framework (each source corresponds to an assumption). We assume then that all sources are independent and "fail" with a very small probability. This leads to a probability assignment on the set of candidates, or equivalently on the set of consistent environments. This probability assignment induces a Dempster-Shafer belief function which measures the probability that a proposition can be deduced from the evidence. This belief function can be used in several different ways to define a nonmonotonic consequence relation. We study and compare these consequence relations. The case of prioritized knowledge bases is briefly considered.