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Learning Argument Acceptability from Abstract Argumentation Frameworks

  • Hiroyuki KidoEmail author
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10091)

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.

Keywords

Acceptability learning Decision trees Argumentation 

Notes

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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Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  1. 1.The University of TokyoBunkyo-kuJapan

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