On measuring inconsistency using maximal consistent sets

Meriem Ammoura; Badran Raddaoui; Yakoub Salhi; Brahim Oukacha

doi:10.1007/978-3-319-20807-7_24

On measuring inconsistency using maximal consistent sets

Meriem Ammoura
, Badran Raddaoui
, Yakoub Salhi
, Brahim Oukacha

Université d'Artois
UMMTO

Résultats de recherche: Le chapitre dans un livre, un rapport, une anthologie ou une collection › Contribution à une conférence › Revue par des pairs

Résumé

An important problem in knowledge-based systems is inconsistency handling. This problem has recently been attracting a lot of attention in AI community. In this paper, we tackle the problem of evaluating the amount of conflicts in knowledge bases, and provide a new fine grained inconsistency measure, denoted MCSC, based on maximal consistent sets. In particular, it is suitable in systems where inconsistency results from multiple consistent sources. We show that our measure satisfies several rational postulates proposed in the literature. Moreover, we provide an encoding in integer linear programming for computing MCSC.

langue originale	Anglais
titre	Symbolic and Quantitative Approaches to Reasoning with Uncertainty - 13th European Conference, ECSQARU 2015, Proceedings
rédacteurs en chef	Sébastien Destercke, Thierry Denoeux
Editeur	Springer Verlag
Pages	267-276
Nombre de pages	10
ISBN (imprimé)	9783319208060
Les DOIs	https://doi.org/10.1007/978-3-319-20807-7_24
état	Publié - 1 janv. 2015
Modification externe	Oui
Evénement	13th European Conference on Symbolic and Quantitative Approaches to Reasoning with Uncertainty, ECSQARU 2015 - Compiègne, France Durée: 15 juil. 2015 → 17 juil. 2015

Série de publications

Nom	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume	9161
ISSN (imprimé)	0302-9743
ISSN (Electronique)	1611-3349

Une conférence

Une conférence	13th European Conference on Symbolic and Quantitative Approaches to Reasoning with Uncertainty, ECSQARU 2015
Pays/Territoire	France
La ville	Compiègne
période	15/07/15 → 17/07/15

Accès au document

10.1007/978-3-319-20807-7_24

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Contient cette citation

Ammoura, M., Raddaoui, B., Salhi, Y., & Oukacha, B. (2015). On measuring inconsistency using maximal consistent sets. Dans S. Destercke, & T. Denoeux (eds.), Symbolic and Quantitative Approaches to Reasoning with Uncertainty - 13th European Conference, ECSQARU 2015, Proceedings (p. 267-276). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol 9161). Springer Verlag. https://doi.org/10.1007/978-3-319-20807-7_24

Ammoura, Meriem ; Raddaoui, Badran ; Salhi, Yakoub et al. / On measuring inconsistency using maximal consistent sets. Symbolic and Quantitative Approaches to Reasoning with Uncertainty - 13th European Conference, ECSQARU 2015, Proceedings. Editeur / Sébastien Destercke ; Thierry Denoeux. Springer Verlag, 2015. p. 267-276 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).

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abstract = "An important problem in knowledge-based systems is inconsistency handling. This problem has recently been attracting a lot of attention in AI community. In this paper, we tackle the problem of evaluating the amount of conflicts in knowledge bases, and provide a new fine grained inconsistency measure, denoted MCSC, based on maximal consistent sets. In particular, it is suitable in systems where inconsistency results from multiple consistent sources. We show that our measure satisfies several rational postulates proposed in the literature. Moreover, we provide an encoding in integer linear programming for computing MCSC.",

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Ammoura, M, Raddaoui, B, Salhi, Y & Oukacha, B 2015, On measuring inconsistency using maximal consistent sets. Dans S Destercke & T Denoeux (eds), Symbolic and Quantitative Approaches to Reasoning with Uncertainty - 13th European Conference, ECSQARU 2015, Proceedings. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), VOL. 9161, Springer Verlag, p. 267-276, 13th European Conference on Symbolic and Quantitative Approaches to Reasoning with Uncertainty, ECSQARU 2015, Compiègne, France, 15/07/15. https://doi.org/10.1007/978-3-319-20807-7_24

On measuring inconsistency using maximal consistent sets. / Ammoura, Meriem; Raddaoui, Badran; Salhi, Yakoub et al.
Symbolic and Quantitative Approaches to Reasoning with Uncertainty - 13th European Conference, ECSQARU 2015, Proceedings. Ed. / Sébastien Destercke; Thierry Denoeux. Springer Verlag, 2015. p. 267-276 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol 9161).

Résultats de recherche: Le chapitre dans un livre, un rapport, une anthologie ou une collection › Contribution à une conférence › Revue par des pairs

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AB - An important problem in knowledge-based systems is inconsistency handling. This problem has recently been attracting a lot of attention in AI community. In this paper, we tackle the problem of evaluating the amount of conflicts in knowledge bases, and provide a new fine grained inconsistency measure, denoted MCSC, based on maximal consistent sets. In particular, it is suitable in systems where inconsistency results from multiple consistent sources. We show that our measure satisfies several rational postulates proposed in the literature. Moreover, we provide an encoding in integer linear programming for computing MCSC.

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Ammoura M, Raddaoui B, Salhi Y, Oukacha B. On measuring inconsistency using maximal consistent sets. Dans Destercke S, Denoeux T, rédacteurs en chef, Symbolic and Quantitative Approaches to Reasoning with Uncertainty - 13th European Conference, ECSQARU 2015, Proceedings. Springer Verlag. 2015. p. 267-276. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)). doi: 10.1007/978-3-319-20807-7_24

On measuring inconsistency using maximal consistent sets

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