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Inferring Quantitative Preferences: Beyond Logical Deduction

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Scalable Uncertainty Management (SUM 2018)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 11142))

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Abstract

In this paper we consider a hybrid possibilistic-probabilistic alternative approach to Probabilistic Preference Logic Networks (PPLNs). Namely, we first adopt a possibilistic model to represent the beliefs about uncertain strict preference statements, and then, by means of a pignistic probability transformation, we switch to a probabilistic-based credulous inference of new preferences for which no explicit (or transitive) information is provided. Finally, we provide a tractable approximate method to compute these probabilities.

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Acknowledgments

Martinez and Simari have been partially supported by EU H2020 research and innovation programme under the Marie Sklodowska-Curie grant agreement No. 690974 for the project MIREL: MIning and REasoning with Legal texts; and funds provided by Universidad Nacional del Sur (UNS), Agencia Nacional de Promocion Cientifica y Tecnologica, and CONICET, Argentina. Godo acknowledges the EU H2020 project SYSMICS (MSCA-RISE-2015 Project 689176) and the Spanish FEDER/MINECO project TIN2015-71799-C2-1-P.

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Authors and Affiliations

  1. Department of Computer Science and Engineering, Universidad Nacional del Sur (UNS), Institute for Computer Science and Engineering (UNS–CONICET), San Andres 800, 8000, Bahia Blanca, Argentina

    Maria Vanina Martinez & Gerardo I. Simari

  2. Artificial Intelligence Research Institute (IIIA–CSIC), Campus de la University Autonoma de Barcelona, 08193, Bellaterra, Spain

    Lluis Godo

Authors
  1. Maria Vanina Martinez
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  2. Lluis Godo
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  3. Gerardo I. Simari
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Corresponding author

Correspondence to Gerardo I. Simari .

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Editors and Affiliations

  1. University of Milano-Bicocca, Milan, Italy

    Davide Ciucci

  2. University of Milano-Bicocca, Milan, Italy

    Gabriella Pasi

  3. Sapienza University of Rome, Rome, Italy

    Barbara Vantaggi

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Martinez, M.V., Godo, L., Simari, G.I. (2018). Inferring Quantitative Preferences: Beyond Logical Deduction. In: Ciucci, D., Pasi, G., Vantaggi, B. (eds) Scalable Uncertainty Management. SUM 2018. Lecture Notes in Computer Science(), vol 11142. Springer, Cham. https://doi.org/10.1007/978-3-030-00461-3_29

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  • DOI: https://doi.org/10.1007/978-3-030-00461-3_29

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-00460-6

  • Online ISBN: 978-3-030-00461-3

  • eBook Packages: Computer ScienceComputer Science (R0)

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