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Learning Argument Acceptability from Abstract Argumentation Frameworks

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New Frontiers in Artificial Intelligence (JSAI-isAI 2015)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 10091))

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Abstract

This paper introduces argument-based decision-tree for learning acceptability of arguments. We specifically examine an attack relation existing between arguments, without referring to any contents, either sentences or words, existing in individual arguments. This idea is formalized using decision trees in which their attributes are instantiated by complete, preferred, stable and grounded extensions, respectively, defined by acceptability semantics. This study extracted 38 arguments and 4 utterers from an argument about euthanasia that actually took place on a social media site. Also, 21 training data were collected by asking them to express their attitudes either for or against the individual 38 arguments. By stratifying audiences in accordance with consistency with utterers, leave-two-out cross validation yielded results with a 0.73 AUC value, on average. This fact demonstrates that our argument-based decision-tree learning is expected to be fairly useful for agents who have a definite position on an issue of argument.

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Acknowledgments

This work was supported by JSPS KAKENHI Grant Number 15KT0041. We would like to thank the manager of SYNCLON for the active participation in this work and valuable comments and suggestions.

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Authors and Affiliations

  1. The University of Tokyo, Hongo 7-3-1, Bunkyo-ku, Tokyo, 113-8656, Japan

    Hiroyuki Kido

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  1. Hiroyuki Kido
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Correspondence to Hiroyuki Kido .

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Editors and Affiliations

  1. Chiba University Graduate School of Engineering, Chiba, Japan

    Mihoko Otake

  2. University of Tsukuba, Tokyo, Japan

    Setsuya Kurahashi

  3. Fujitsu Laboratories, Kanagawa, Japan

    Yuiko Ota

  4. National Institute of Informatics, Tokyo, Japan

    Ken Satoh

  5. Ochanomizu University, Tokyo, Japan

    Daisuke Bekki

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Kido, H. (2017). Learning Argument Acceptability from Abstract Argumentation Frameworks. In: Otake, M., Kurahashi, S., Ota, Y., Satoh, K., Bekki, D. (eds) New Frontiers in Artificial Intelligence. JSAI-isAI 2015. Lecture Notes in Computer Science(), vol 10091. Springer, Cham. https://doi.org/10.1007/978-3-319-50953-2_24

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  • DOI: https://doi.org/10.1007/978-3-319-50953-2_24

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-50952-5

  • Online ISBN: 978-3-319-50953-2

  • eBook Packages: Computer ScienceComputer Science (R0)

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