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Similarity Measures Based on Compiled Arguments

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Symbolic and Quantitative Approaches to Reasoning with Uncertainty (ECSQARU 2021)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 12897))

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Abstract

Argumentation is a prominent approach for reasoning with inconsistent information. It is based on the justification of formulas by arguments generated from propositional knowledge bases. It has recently been shown that similarity between arguments should be taken into account when evaluating arguments. Consequently, different similarity measures have been proposed in the literature. Although these measures satisfy desirable properties, they suffer from the side effects of being syntax-dependent. Indeed, they may miss redundant information, leading to undervalued similarity. This paper overcomes this shortcoming by compiling arguments, which amounts to transforming their formulas into clauses, and using the latter for extending existing measures and principles. We show that the new measures deal properly with the critical cases.

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Notes

  1. 1.

    \(^3\) \(||\) stands for the cardinality of a set.

  2. 2.

    The letter \(\mathtt {A}\) in \(\mathtt {A}\)-\(\mathtt { CR}\) stands for “average”.

  3. 3.

    \(\mathtt {U}\) in \(\mathtt {U}\)-\(\mathtt { CR}\) stands for “union”.

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Author information

Authors and Affiliations

  1. CNRS – IRIT, Toulouse, France

    Leila Amgoud

  2. Paul Sabatier University – IRIT, Toulouse, France

    Victor David

Authors
  1. Leila Amgoud
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  2. Victor David
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Correspondence to Victor David .

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Editors and Affiliations

  1. Institute of Information Theory and Automation, Prague, Czech Republic

    Jiřina Vejnarová

  2. Insight Centre for Data Analytics, School of Computer Science and IT University College Cork, Cork, Ireland

    Nic Wilson

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Amgoud, L., David, V. (2021). Similarity Measures Based on Compiled Arguments. In: Vejnarová, J., Wilson, N. (eds) Symbolic and Quantitative Approaches to Reasoning with Uncertainty. ECSQARU 2021. Lecture Notes in Computer Science(), vol 12897. Springer, Cham. https://doi.org/10.1007/978-3-030-86772-0_3

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  • DOI: https://doi.org/10.1007/978-3-030-86772-0_3

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  • Print ISBN: 978-3-030-86771-3

  • Online ISBN: 978-3-030-86772-0

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