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Can AI Models Capture Natural Language Argumentation?

Author

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  • Leila Amgoud

    (Institut de Recherche en Informatique de Toulouse, France)

  • Henri Prade

    (Institut de Recherche en Informatique de Toulouse, France)

Abstract

Formal AI models of argumentation define arguments as reasons that support claims (which may be beliefs, decisions, actions, etc.). Such arguments may be attacked by other arguments. The main issue is then to identify the accepted ones. Several semantics were thus proposed for evaluating the arguments. Works in linguistics focus mainly on understanding the notion of argument, identifying its types, and describing different forms of counter-argumentation. This paper advocates that such typologies are instrumental for capturing real argumentations. It shows that some of the forms cannot be handled properly by AI models. Finally, it shows that the use of square of oppositions (a very old logical device) illuminates the interrelations between the different forms of argumentation.

Suggested Citation

  • Leila Amgoud & Henri Prade, 2012. "Can AI Models Capture Natural Language Argumentation?," International Journal of Cognitive Informatics and Natural Intelligence (IJCINI), IGI Global, vol. 6(3), pages 19-32, July.
    • Handle: RePEc:igg:jcini0:v:6:y:2012:i:3:p:19-32
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    File URL: http://services.igi-global.com/resolvedoi/resolve.aspx?doi=10.4018/jcini.2012070102
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    Cited by:

    1. Edgardo Bucciarelli & Andrea Oliva, 2020. "Arrow’s impossibility theorem as a special case of Nash equilibrium: a cognitive approach to the theory of collective decision-making," Mind & Society: Cognitive Studies in Economics and Social Sciences, Springer;Fondazione Rosselli, vol. 19(1), pages 15-41, June.

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