Advertisement

Enumerating Preferred Extensions: A Case Study of Human Reasoning

  • Alice Toniolo
  • Timothy J. Norman
  • Nir Oren
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10757)

Abstract

This paper seeks to better understand the links between human reasoning and preferred extensions as found within formal argumentation, especially in the context of uncertainty. The degree of believability of a conclusion may be associated with the number of preferred extensions in which the conclusion is credulously accepted. We are interested in whether people agree with this evaluation. A set of experiments with human participants is presented to investigate the validity of such an association. Our results show that people tend to agree with the outcome of a version of Thimm’s probabilistic semantics in purely qualitative domains as well as in domains in which conclusions express event likelihood. Furthermore, we are able to characterise this behaviour: the heuristics employed by people in understanding preferred extensions are similar to those employed in understanding probabilities.

Keywords

Argumentation Probabilistic semantics User evaluation 

Notes

Acknowledgements

This work was partially funded by a grant to the University of Aberdeen made by the UK Economic and Social Research Council; Grant reference ES/MOO1628/1.

References

  1. 1.
    Bailin, S., Battersby, M.: Conductive argumentation, degrees of confidence, and the communication of uncertainty. In: van Eemeren, F.H., Garssen, B. (eds.) Reflections on Theoretical Issues in Argumentation Theory. AL, vol. 28, pp. 71–82. Springer, Cham (2015).  https://doi.org/10.1007/978-3-319-21103-9_5 Google Scholar
  2. 2.
    Bench-Capon, T.J.M.: Persuasion in practical argument using value-based argumentation frameworks. J. Logic Comput. 13(3), 429–448 (2003)MathSciNetCrossRefzbMATHGoogle Scholar
  3. 3.
    Bonzon, E., Delobelle, J., Konieczny, S., Maudet, N.: A comparative study of ranking-based semantics for abstract argumentation. In: Proceedings of the 30th AAAI Conference on Artificial Intelligence, pp. 914–920 (2016)Google Scholar
  4. 4.
    Cayrol, C., Lagasquie-Schiex, M.C.: Graduality in argumentation. J. Artif. Intell. Res. 23(1), 245–297 (2005)MathSciNetzbMATHGoogle Scholar
  5. 5.
    Cerutti, F., Tintarev, N., Oren, N.: Formal arguments, preferences, and natural language interfaces to humans: an empirical evaluation. In: Proceedings of the 21st European Conference on Artificial Intelligence, pp. 207–212 (2014)Google Scholar
  6. 6.
    Croitoru, M., Vesic, S.: What can argumentation do for inconsistent ontology query answering? In: Liu, W., Subrahmanian, V.S., Wijsen, J. (eds.) SUM 2013. LNCS (LNAI), vol. 8078, pp. 15–29. Springer, Heidelberg (2013).  https://doi.org/10.1007/978-3-642-40381-1_2 CrossRefGoogle Scholar
  7. 7.
    Dunne, P.E., Hunter, A., McBurney, P., Parsons, S., Wooldridge, M.: Weighted argument systems: basic definitions, algorithms, and complexity results. Artif. Intell. 175(2), 457–486 (2011)MathSciNetCrossRefzbMATHGoogle Scholar
  8. 8.
    Haenni, R.: Probabilistic argumentation. J. Appl. Logic 7(2), 155–176 (2009)MathSciNetCrossRefzbMATHGoogle Scholar
  9. 9.
    Heatherly, R., Kantarcioglu, M., Thuraisingham, B.: Preventing private information inference attacks on social networks. IEEE Trans. Knowl. Data Eng. 25(8), 1849–1862 (2013)CrossRefGoogle Scholar
  10. 10.
    Hunter, A., Thimm, M.: Probabilistic argument graphs for argumentation lotteries. In: Computational Models of Argument, Frontiers in Artificial Intelligence and Applications, vol. 266, pp. 313–324. IOS Press (2014)Google Scholar
  11. 11.
    Li, H., Oren, N., Norman, T.J.: Probabilistic argumentation frameworks. In: Modgil, S., Oren, N., Toni, F. (eds.) TAFA 2011. LNCS (LNAI), vol. 7132, pp. 1–16. Springer, Heidelberg (2012).  https://doi.org/10.1007/978-3-642-29184-5_1 CrossRefGoogle Scholar
  12. 12.
    Mayer, J.M., Schuler, R.P., Jones, Q.: Towards an understanding of social inference opportunities in social computing. In: Proceedings of the 17th ACM International Conference on Supporting Group Work, pp. 239–248 (2012)Google Scholar
  13. 13.
    Modgil, S., Prakken, H.: The ASPIC+ framework for structured argumentation: a tutorial. Argum. Comput. 5(1), 31–62 (2014)CrossRefGoogle Scholar
  14. 14.
    Prakken, H.: An abstract framework for argumentation with structured arguments. Argum. Comput. 1(2), 93–124 (2010)CrossRefGoogle Scholar
  15. 15.
    Tang, Y., Cai, K., McBurney, P., Sklar, E., Parsons, S.: Using argumentation to reason about trust and belief. J. Logic Comput. 22(5), 979 (2012)MathSciNetCrossRefzbMATHGoogle Scholar
  16. 16.
    Thimm, M.: A probabilistic semantics for abstract argumentation. In: Proceedings of the Twentieth European Conference on Artificial Intelligence, pp. 750–755 (2012)Google Scholar
  17. 17.
    Walton, D., Reed, C., Macagno, F.: Argumentation Schemes. Cambridge University Press, Cambridge (2008)CrossRefzbMATHGoogle Scholar
  18. 18.
    Zenker, F.: Bayesian Argumentation: The Practical Side of Probability. Synthese Library, vol. 362. Springer, Heidelberg (2013).  https://doi.org/10.1007/978-94-007-5357-0. pp. 1–11CrossRefGoogle Scholar

Copyright information

© Springer International Publishing AG, part of Springer Nature 2018

Authors and Affiliations

  1. 1.School of Computer ScienceUniversity of St AndrewsSt AndrewsScotland, UK
  2. 2.Department of Electronics and Computer ScienceUniversity of SouthamptonSouthamptonUK
  3. 3.Department of Computing ScienceUniversity of AberdeenAberdeenScotland, UK

Personalised recommendations