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Inconsistency Measurement

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Scalable Uncertainty Management (SUM 2019)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 11940))

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Abstract

The field of Inconsistency Measurement is concerned with the development of principles and approaches to quantitatively assess the severity of inconsistency in knowledge bases. In this survey, we give a broad overview on this field by outlining its basic motivation and discussing some of these core principles and approaches. We focus on the work that has been done for classical propositional logic but also give some pointers to applications on other logical formalisms.

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Notes

  1. 1.

    We use the term knowledge to refer to subjective knowledge or beliefs, i. e., pieces of information that may not necessary be true in the real world but are only assumed to be true for the agent(s) under consideration.

  2. 2.

    At least in monotonic logics; for a discussion about inconsistency measurement in non-monotonic logics see [9, 43] and Sect. 5.3.

  3. 3.

    Implementations of most of these measures can also be found in the Tweety Libraries for Artificial Intelligence [40] and an online interface is available at http://tweetyproject.org/w/incmes.

  4. 4.

    And in this author’s opinion also a bit mislabelled.

  5. 5.

    see http://tweetyproject.org/w/incmes.

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Acknowledgements

The research reported here was partially supported by the Deutsche Forschungsgemeinschaft (grant DE 1983/9-1).

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Authors and Affiliations

  1. University of Koblenz-Landau, Koblenz, Germany

    Matthias Thimm

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  1. Matthias Thimm
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Editors and Affiliations

  1. Institut Supérieur de Gestion de Tunis, Bouchoucha, Tunisia

    Nahla Ben Amor

  2. University of Technology of Compiègne, Compiègne, France

    Benjamin Quost

  3. University of Luxembourg, Esch-Sur-Alzette, Luxembourg

    Martin Theobald

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Thimm, M. (2019). Inconsistency Measurement. In: Ben Amor, N., Quost, B., Theobald, M. (eds) Scalable Uncertainty Management. SUM 2019. Lecture Notes in Computer Science(), vol 11940. Springer, Cham. https://doi.org/10.1007/978-3-030-35514-2_2

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  • DOI: https://doi.org/10.1007/978-3-030-35514-2_2

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