Probabilistic reasoning in intelligent systems: networks of plausible inference
Probabilistic reasoning in intelligent systems: networks of plausible inference
The uncertain reasoner's companion: a mathematical perspective
The uncertain reasoner's companion: a mathematical perspective
Easy Cases of Probabilistic Satisfiability
Annals of Mathematics and Artificial Intelligence
Convex Optimization
Elements of Information Theory (Wiley Series in Telecommunications and Signal Processing)
Elements of Information Theory (Wiley Series in Telecommunications and Signal Processing)
Argumentation in artificial intelligence
Artificial Intelligence
Resolving Inconsistencies in Probabilistic Knowledge Bases
KI '07 Proceedings of the 30th annual German conference on Advances in Artificial Intelligence
Propositional Clausal Defeasible Logic
JELIA '08 Proceedings of the 11th European conference on Logics in Artificial Intelligence
Reasoning under inconsistency: the forgotten connective
IJCAI'05 Proceedings of the 19th international joint conference on Artificial intelligence
Conditionals in nonmonotonic reasoning and belief revision: considering conditionals as agents
Conditionals in nonmonotonic reasoning and belief revision: considering conditionals as agents
Coherent knowledge processing at maximum entropy by spirit
UAI'96 Proceedings of the Twelfth international conference on Uncertainty in artificial intelligence
Approaches to measuring inconsistent information
Inconsistency Tolerance
On the measure of conflicts: Shapley Inconsistency Values
Artificial Intelligence
Measuring and repairing inconsistency in probabilistic knowledge bases
International Journal of Approximate Reasoning
Measuring and repairing inconsistency in knowledge bases with graded truth
Fuzzy Sets and Systems
From inconsistency handling to non-canonical requirements management: A logical perspective
International Journal of Approximate Reasoning
The Consistency of the Medical Expert System CADIAG-2: A Probabilistic Approach
Journal of Information Technology Research
Inconsistency measures for probabilistic logics
Artificial Intelligence
Approaches to measuring inconsistency for stratified knowledge bases
International Journal of Approximate Reasoning
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This paper develops an inconsistency measure on conditional probabilistic knowledge bases. The measure is based on fundamental principles for inconsistency measures and thus provides a solid theoretical framework for the treatment of inconsistencies in probabilistic expert systems. We illustrate its usefulness and immediate application on several examples and present some formal results. Building on this measure we use the Shapley value---a well-known solution for coalition games---to define a sophisticated indicator that is not only able to measure inconsistencies but to reveal the causes of inconsistencies in the knowledge base. Altogether these tools guide the knowledge engineer in his aim to restore consistency and therefore enable him to build a consistent and usable knowledge base that can be employed in probabilistic expert systems.