A theory of diagnosis from first principles
Artificial Intelligence
What does a conditional knowledge base entail?
Artificial Intelligence
On the complexity of propositional knowledge base revision, updates, and counterfactuals
Artificial Intelligence
Handbook of logic in artificial intelligence and logic programming (vol. 3)
The uncertain reasoner's companion: a mathematical perspective
The uncertain reasoner's companion: a mathematical perspective
On the logic of iterated belief revision
Artificial Intelligence
Two Information Measures for Inconsistent Sets
Journal of Logic, Language and Information
Measuring inconsistency in knowledge via quasi-classical models
Eighteenth national conference on Artificial intelligence
Artificial Intelligence - Special issue on nonmonotonic 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
A negotiation-style framework for non-prioritised revision
TARK '01 Proceedings of the 8th conference on Theoretical aspects of rationality and knowledge
Merging Requirements Views with Incompleteness and Inconsistency
ASWEC '05 Proceedings of the 2005 Australian conference on Software Engineering
Logical comparison of inconsistent perspectives using scoring functions
Knowledge and Information Systems
How to act on inconsistent news: ignore, resolve, or reject
Data & Knowledge Engineering
Measuring inconsistency in knowledgebases
Journal of Intelligent Information Systems
Social contraction and belief negotiation
Information Fusion
Iterated belief revision, revised
Artificial Intelligence
Logical foundations of negotiation: outcome, concession and adaptation
AAAI'04 Proceedings of the 19th national conference on Artifical intelligence
Negotiation as mutual belief revision
AAAI'04 Proceedings of the 19th national conference on Artifical intelligence
Admissible and restrained revision
Journal of Artificial Intelligence Research
Preferred subtheories: an extended logical framework for default reasoning
IJCAI'89 Proceedings of the 11th international joint conference on Artificial intelligence - Volume 2
Boosting a complete technique to find MSS and MUS thanks to a local search oracle
IJCAI'07 Proceedings of the 20th international joint conference on Artifical intelligence
Quantifying information and contradiction in propositional logic through test actions
IJCAI'03 Proceedings of the 18th international joint conference on Artificial intelligence
Reasoning under inconsistency: the forgotten connective
IJCAI'05 Proceedings of the 19th international joint conference on Artificial intelligence
Iterated theory base change: a computational model
IJCAI'95 Proceedings of the 14th international joint conference on Artificial intelligence - Volume 2
Measuring inconsistency in probabilistic knowledge bases
UAI '09 Proceedings of the Twenty-Fifth Conference on Uncertainty in Artificial Intelligence
Argumentative inference in uncertain and inconsistent knowledge bases
UAI'93 Proceedings of the Ninth international conference on Uncertainty in artificial intelligence
Approaches to measuring inconsistent information
Inconsistency Tolerance
Measuring inconsistency in requirements specifications
ECSQARU'05 Proceedings of the 8th European conference on Symbolic and Quantitative Approaches to Reasoning with Uncertainty
Conciliation and consensus in iterated belief merging
ECSQARU'05 Proceedings of the 8th European conference on Symbolic and Quantitative Approaches to Reasoning with Uncertainty
A Syntax-based approach to measuring the degree of inconsistency for belief bases
International Journal of Approximate Reasoning
The 10th International Conference on Autonomous Agents and Multiagent Systems - Volume 2
Beyond maxi-consistent argumentation operators
JELIA'12 Proceedings of the 13th European conference on Logics in Artificial Intelligence
A Blame-Based Approach to Generating Proposals for Handling Inconsistency in Software Requirements
International Journal of Knowledge and Systems Science
Inconsistency measures for probabilistic logics
Artificial Intelligence
Distance-Based measures of inconsistency
ECSQARU'13 Proceedings of the 12th European conference on Symbolic and Quantitative Approaches to Reasoning with Uncertainty
Measuring inconsistency through minimal proofs
ECSQARU'13 Proceedings of the 12th European conference on Symbolic and Quantitative Approaches to Reasoning with Uncertainty
A reasoning platform based on the MI shapley inconsistency value
ECSQARU'13 Proceedings of the 12th European conference on Symbolic and Quantitative Approaches to Reasoning with Uncertainty
Approaches to measuring inconsistency for stratified knowledge bases
International Journal of Approximate Reasoning
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There are relatively few proposals for inconsistency measures for propositional belief bases. However inconsistency measures are potentially as important as information measures for artificial intelligence, and more generally for computer science. In particular, they can be useful to define various operators for belief revision, belief merging, and negotiation. The measures that have been proposed so far can be split into two classes. The first class of measures takes into account the number of formulae required to produce an inconsistency: the more formulae required to produce an inconsistency, the less inconsistent the base. The second class takes into account the proportion of the language that is affected by the inconsistency: the more propositional variables affected, the more inconsistent the base. Both approaches are sensible, but there is no proposal for combining them. We address this need in this paper: our proposal takes into account both the number of variables affected by the inconsistency and the distribution of the inconsistency among the formulae of the base. Our idea is to use existing inconsistency measures in order to define a game in coalitional form, and then to use the Shapley value to obtain an inconsistency measure that indicates the responsibility/contribution of each formula to the overall inconsistency in the base. This allows us to provide a more reliable image of the belief base and of the inconsistency in it.