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A Characterization of Belief Merging Operators in the Regular Horn Fragment of Signed Logic

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Modeling Decisions for Artificial Intelligence (MDAI 2020)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 12256))

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Abstract

In this paper, we present a set of logical postulates for belief merging in the set of all regular signed Horn formulas. Proving a representation result, we show that regular Horn merging in signed logic can be seen as an aggregation problem on rankings of possible interpretations in this many-valued setting.

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Acknowledgements

The author would like to thank S. Rümmele and E. Lee for their contributions to a preliminary draft of this paper, and the referees for their useful comments. This article has received funding from the project 2018-Recercaixa AppPhil.

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Authors and Affiliations

  1. Universitat Autònoma de Barcelona, Barcelona, Spain

    Pilar Dellunde

  2. Barcelona Graduate School of Mathematics, Barcelona, Spain

    Pilar Dellunde

  3. Artificial Intelligence Research Institute IIIA-CSIC, Barcelona, Spain

    Pilar Dellunde

Authors
  1. Pilar Dellunde
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Corresponding author

Correspondence to Pilar Dellunde .

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Editors and Affiliations

  1. Department of Computing Science, Umeå University, Umeå, Sweden

    Vicenç Torra

  2. Department of Management Science, Tamagawa University, Tokyo, Japan

    Yasuo Narukawa

  3. Department of Operations, Innovation and Data Sciences, ESADE, Sant Cugat, Spain

    Jordi Nin

  4. Department of Operations, Innovation and Data Sciences, ESADE, Sant Cugat, Spain

    Núria Agell

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Dellunde, P. (2020). A Characterization of Belief Merging Operators in the Regular Horn Fragment of Signed Logic. In: Torra, V., Narukawa, Y., Nin, J., Agell, N. (eds) Modeling Decisions for Artificial Intelligence. MDAI 2020. Lecture Notes in Computer Science(), vol 12256. Springer, Cham. https://doi.org/10.1007/978-3-030-57524-3_1

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  • DOI: https://doi.org/10.1007/978-3-030-57524-3_1

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-57523-6

  • Online ISBN: 978-3-030-57524-3

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