Anytime deduction for probabilistic logic
Artificial Intelligence
Conditional logic and the principle of entropy
Artificial Intelligence
Probabilistic Default Reasoning with Conditional Constraints
Annals of Mathematics and Artificial Intelligence
Knowledge Representation and Reasoning
Knowledge Representation and Reasoning
Convex Optimization
Combining probabilistic logic programming with the power of maximum entropy
Artificial Intelligence - Special issue on nonmonotonic reasoning
The Uncertain Reasoner's Companion (Cambridge Tracts in Theoretical Computer Science)
The Uncertain Reasoner's Companion (Cambridge Tracts in Theoretical Computer Science)
Analysing inconsistent first-order knowledgebases
Artificial Intelligence
Resolving Inconsistencies in Probabilistic Knowledge Bases
KI '07 Proceedings of the 30th annual German conference on Advances in Artificial Intelligence
Argumentation in Artificial Intelligence
Argumentation in Artificial Intelligence
Conditionals in nonmonotonic reasoning and belief revision: considering conditionals as agents
Conditionals in nonmonotonic reasoning and belief revision: considering conditionals as agents
Measuring inconsistency in probabilistic knowledge bases
UAI '09 Proceedings of the Twenty-Fifth Conference on Uncertainty in Artificial Intelligence
On the measure of conflicts: Shapley Inconsistency Values
Artificial Intelligence
Measuring and repairing inconsistency in probabilistic knowledge bases
International Journal of Approximate Reasoning
Coherent knowledge processing at maximum entropy by spirit
UAI'96 Proceedings of the Twelfth international conference on Uncertainty in artificial intelligence
Probabilistic satisfiability: logic-based algorithms and phase transition
IJCAI'11 Proceedings of the Twenty-Second international joint conference on Artificial Intelligence - Volume Volume One
Measuring the good and the bad in inconsistent information
IJCAI'11 Proceedings of the Twenty-Second international joint conference on Artificial Intelligence - Volume Volume Three
Distance-Based measures of inconsistency
ECSQARU'13 Proceedings of the 12th European conference on Symbolic and Quantitative Approaches to Reasoning with Uncertainty
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Inconsistencies in knowledge bases are of major concern in knowledge representation and reasoning. In formalisms that employ model-based reasoning mechanisms inconsistencies render a knowledge base useless due to the non-existence of a model. In order to restore consistency an analysis and understanding of inconsistencies are mandatory. Recently, the field of inconsistency measurement has gained some attention for knowledge representation formalisms based on classical logic. An inconsistency measure is a tool that helps the knowledge engineer in obtaining insights into inconsistencies by assessing their severity. In this paper, we investigate inconsistency measurement in probabilistic conditional logic, a logic that incorporates uncertainty and focuses on the role of conditionals, i.e. if-then rules. We do so by extending inconsistency measures for classical logic to the probabilistic setting. Further, we propose novel inconsistency measures that are specifically tailored for the probabilistic case. These novel measures use distance measures to assess the distance of a knowledge base to a consistent one and therefore takes the crucial role of probabilities into account. We analyze the properties of the discussed measures and compare them using a series of rationality postulates.