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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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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