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Deontic Logic for Human Reasoning

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Advances in Knowledge Representation, Logic Programming, and Abstract Argumentation

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 9060))

Abstract

Deontic logic is shown to be applicable for modelling human reasoning. For this the Wason selection task and the suppression task are discussed in detail. Different versions of modelling norms with deontic logic are introduced and in the case of the Wason selection task it is demonstrated how differences in the performance of humans in the abstract and in the social contract case can be explained. Furthermore, it is shown that an automated theorem prover can be used as a reasoning tool for deontic logic.

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Author information

Authors and Affiliations

  1. Universität Koblenz-Landau, Germany

    Ulrich Furbach & Claudia Schon

Authors
  1. Ulrich Furbach
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  2. Claudia Schon
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Editor information

Editors and Affiliations

  1. Institut für Informationssysteme, Technische Universität Wien, 184/3, Abteilung für wissensbasierte Systeme, Favoritenstraße 9-11, 1040, Wien, Austria

    Thomas Eiter

  2. Institut für Informatik, Abteilung Intelligente Systeme, Universität Leipzig, PF 100920, 04009, Leipzig, Germany

    Hannes Strass

  3. Department of Computer Science, University of Kentucky, 309 Davis Marksbury Building, 329 Rose Street, 40506-0633, Lexington, KY, USA

    Mirosław Truszczyński

  4. Institut für Informationssysteme, Technische Universität Wien, 184/2, Abteilung für Datenbanken und Artificial Intelligence, Favoritenstraße 9-11, 1040, Wien, Austria

    Stefan Woltran

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Furbach, U., Schon, C. (2015). Deontic Logic for Human Reasoning. In: Eiter, T., Strass, H., Truszczyński, M., Woltran, S. (eds) Advances in Knowledge Representation, Logic Programming, and Abstract Argumentation. Lecture Notes in Computer Science(), vol 9060. Springer, Cham. https://doi.org/10.1007/978-3-319-14726-0_5

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  • DOI: https://doi.org/10.1007/978-3-319-14726-0_5

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-14725-3

  • Online ISBN: 978-3-319-14726-0

  • eBook Packages: Computer ScienceComputer Science (R0)

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