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Learning Individual and Group Preferences in Abstract Argumentation

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PRICAI 2019: Trends in Artificial Intelligence (PRICAI 2019)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 11670))

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Abstract

In Abstract Argumentation, given the same AA framework rational agents accept the same arguments unless they reason by different AA semantics. Real agents may not do so in such situations, and in this paper we assume that this is because they have different preferences over the confronted arguments. Hence by reconstructing their reasoning processes, we can learn their hidden preferences, which then allow us to predict what else they must accept. Concretely we formalize and develop algorithms for such problems as learning the hidden preference relation of an agent from his expressed opinion, by which we mean a subset of arguments or attacks he accepted; and learning the collective preferences of a group from a dataset of individual opinions. A major challenge we addressed in this endeavor is to represent and reason with “answer sets” of preference relations which are generally exponential or even infinite.

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Notes

  1. 1.

    This AAF is used in all running examples throughout the paper.

  2. 2.

    The unexpressed conclusion of this argument is that Judge Kavanaugh is not qualified to be a Justice. Hence F attacks T.

  3. 3.

    We focus on the grounded semantics but our approach can be extended to others.

  4. 4.

    An algorithmic form of function Follow can be easily worked out but we skip this.

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Acknowledgment

This paper is based upon work supported in part by the Asian Office of Aerospace R&D (AOARD) (Grant No. FA2386-17-1-4046), and the US Office of Naval Research Global (ONRG, Grant No. N62909-19-1-2031).

Author information

Authors and Affiliations

  1. Sirindhorn International Institute of Technology, Khlong Nueng, Thailand

    Nguyen Duy Hung

  2. Japan Advanced Institute of Science and Technology, Nomi, Japan

    Van-Nam Huynh

Authors
  1. Nguyen Duy Hung
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  2. Van-Nam Huynh
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Correspondence to Nguyen Duy Hung .

Editor information

Editors and Affiliations

  1. Department of Computing, Macquarie University, Sydney, NSW, Australia

    Abhaya C. Nayak

  2. RIKEN Center for Integrative Medical Sciences, Yokohama, Japan

    Alok Sharma

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Hung, N.D., Huynh, VN. (2019). Learning Individual and Group Preferences in Abstract Argumentation. In: Nayak, A., Sharma, A. (eds) PRICAI 2019: Trends in Artificial Intelligence. PRICAI 2019. Lecture Notes in Computer Science(), vol 11670. Springer, Cham. https://doi.org/10.1007/978-3-030-29908-8_55

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  • DOI: https://doi.org/10.1007/978-3-030-29908-8_55

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-29907-1

  • Online ISBN: 978-3-030-29908-8

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