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Argumentation Framework Based on Evidence Theory

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Information Processing and Management of Uncertainty in Knowledge-Based Systems (IPMU 2016)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 611))

Abstract

In many fields of automated information processing it becomes crucial to consider imprecise, uncertain or inconsistent pieces of information. Therefore, integrating uncertainty factors in argumentation theory is of paramount importance. Recently, several argumentation based approaches have emerged to model uncertain data with probabilities. In this paper, we propose a new argumentation system called evidential argumentation framework that takes into account imprecision and uncertainty modeled by means of evidence theory. Indeed, evidence theory brings new semantics since arguments represent expert opinions with several weighted alternatives. Then, the evidential argumentation framework is studied in the light of both Smets and Demspter-Shafer interpretations of evidence theory. For each interpretation, we generalize Dung’s standard semantics with illustrative examples. We also investigate several preference criteria for pairwise comparison of extensions in order to select the ones that represent potential solutions to a given decision making problem.

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Notes

  1. 1.

    A certain bba expresses the total certainty. It is defined as follows: \(m(A)=1\) and \(m(B)=0\) for all \(B \ne A\) and \(B\subseteq \varTheta \), where A is a singleton event of \(\varTheta \).

  2. 2.

    Let \(x_1, x_2, x^{'}_1, x^{'}_2\) be four alternatives. Then \((x_1, x_2) \ge _{pareto} (x^{'}_1, x^{'}_2)\) iff \(\forall i \in [1, 2]\), \(x_i \ge x^{'}_i\) and \(\exists ~ j\), such that \(x_j > x^{'}_j\).

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Author information

Authors and Affiliations

  1. Sorbonne University, Université de technologie de Compiègne, CNRS, UMR 7338 Biomechanics and Bioengineering, Compiègne, France

    Ahmed Samet & Tien-Tuan Dao

  2. LIAS - ENSMA/University of Poitiers, Poitiers, France

    Badran Raddaoui & Allel Hadjali

Authors
  1. Ahmed Samet
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  2. Badran Raddaoui
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  3. Tien-Tuan Dao
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  4. Allel Hadjali
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Corresponding author

Correspondence to Ahmed Samet .

Editor information

Editors and Affiliations

  1. INESC-ID,Instituto Superior Técnico, Universidade de Lisboa, Lisboa, Portugal

    Joao Paulo Carvalho

  2. LIP 6, Université Pierre et Marie Curie, Paris, France

    Marie-Jeanne Lesot

  3. School of Industrial Engineering, Eindhoven University of Technology, Eindhoven, The Netherlands

    Uzay Kaymak

  4. IDMEC,Instituto Superior Técnico, Universidade de Lisboa, Lisboa, Portugal

    Susana Vieira

  5. LIP6, Université Pierre et Marie Curie, CNRS, Paris, France

    Bernadette Bouchon-Meunier

  6. Iona College, Machine Intelligence Institute, New Rochelle, New York, USA

    Ronald R. Yager

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© 2016 Springer International Publishing Switzerland

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Samet, A., Raddaoui, B., Dao, TT., Hadjali, A. (2016). Argumentation Framework Based on Evidence Theory. In: Carvalho, J., Lesot, MJ., Kaymak, U., Vieira, S., Bouchon-Meunier, B., Yager, R. (eds) Information Processing and Management of Uncertainty in Knowledge-Based Systems. IPMU 2016. Communications in Computer and Information Science, vol 611. Springer, Cham. https://doi.org/10.1007/978-3-319-40581-0_21

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  • DOI: https://doi.org/10.1007/978-3-319-40581-0_21

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-40580-3

  • Online ISBN: 978-3-319-40581-0

  • eBook Packages: Computer ScienceComputer Science (R0)

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