Advertisement

Open-Mindedness of Gradual Argumentation Semantics

  • Nico PotykaEmail author
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11940)

Abstract

Gradual argumentation frameworks allow modeling arguments and their relationships and have been applied to problems like decision support and social media analysis. Semantics assign strength values to arguments based on an initial belief and their relationships. The final assignment should usually satisfy some common-sense properties. One property that may currently be missing in the literature is Open-Mindedness. Intuitively, Open-Mindedness is the ability to move away from the initial belief in an argument if sufficient evidence against this belief is given by other arguments. We generalize and refine a previously introduced notion of Open-Mindedness and use this definition to analyze nine gradual argumentation approaches from the literature.

Keywords

Gradual argumentation Weighted argumentation Semantical properties 

References

  1. 1.
    Alsinet, T., Argelich, J., Béjar, R., Fernández, C., Mateu, C., Planes, J.: Weighted argumentation for analysis of discussions in Twitter. Int. J. Approximate Reasoning 85, 21–35 (2017)MathSciNetCrossRefGoogle Scholar
  2. 2.
    Amgoud, L., Ben-Naim, J.: Axiomatic foundations of acceptability semantics. In: International Conference on Principles of Knowledge Representation and Reasoning (KR), pp. 2–11 (2016)Google Scholar
  3. 3.
    Amgoud, L., Ben-Naim, J.: Evaluation of arguments from support relations: axioms and semantics. In: International Joint Conferences on Artificial Intelligence (IJCAI), p. 900 (2016)Google Scholar
  4. 4.
    Amgoud, L., Ben-Naim, J.: Evaluation of arguments in weighted bipolar graphs. In: Antonucci, A., Cholvy, L., Papini, O. (eds.) ECSQARU 2017. LNCS (LNAI), vol. 10369, pp. 25–35. Springer, Cham (2017).  https://doi.org/10.1007/978-3-319-61581-3_3CrossRefGoogle Scholar
  5. 5.
    Amgoud, L., Ben-Naim, J., Doder, D., Vesic, S.: Ranking arguments with compensation-based semantics. In: International Conference on Principles of Knowledge Representation and Reasoning (KR) (2016)Google Scholar
  6. 6.
    Amgoud, L., Ben-Naim, J., Doder, D., Vesic, S.: Acceptability semantics for weighted argumentation frameworks. In: IJCAI, vol. 2017, pp. 56–62 (2017)Google Scholar
  7. 7.
    Amgoud, L., Cayrol, C., Lagasquie-Schiex, M.C., Livet, P.: On bipolarity in argumentation frameworks. Int. J. Intell. Syst. 23(10), 1062–1093 (2008)CrossRefGoogle Scholar
  8. 8.
    Baroni, P., Rago, A., Toni, F.: How many properties do we need for gradual argumentation? In: AAAI Conference on Artificial Intelligence (AAAI), pp. 1736–1743. AAAI (2018)Google Scholar
  9. 9.
    Baroni, P., Romano, M., Toni, F., Aurisicchio, M., Bertanza, G.: An argumentation-based approach for automatic evaluation of design debates. In: Leite, J., Son, T.C., Torroni, P., van der Torre, L., Woltran, S. (eds.) CLIMA 2013. LNCS (LNAI), vol. 8143, pp. 340–356. Springer, Heidelberg (2013).  https://doi.org/10.1007/978-3-642-40624-9_21CrossRefGoogle Scholar
  10. 10.
    Besnard, P., Hunter, A.: A logic-based theory of deductive arguments. Artif. Intell. 128(1–2), 203–235 (2001)MathSciNetCrossRefGoogle Scholar
  11. 11.
    Bonzon, E., Delobelle, J., Konieczny, S., Maudet, N.: A comparative study of ranking-based semantics for abstract argumentation. In: AAAI Conference on Artificial Intelligence (AAAI), pp. 914–920 (2016)Google Scholar
  12. 12.
    Cocarascu, O., Rago, A., Toni, F.: Extracting dialogical explanations for review aggregations with argumentative dialogical agents. In: International Conference on Autonomous Agents and MultiAgent Systems (AAMAS), pp. 1261–1269. International Foundation for Autonomous Agents and Multiagent Systems (2019)Google Scholar
  13. 13.
    Dung, P.M.: On the acceptability of arguments and its fundamental role in nonmonotonic reasoning, logic programming and n-person games. Artif. Intell. 77(2), 321–357 (1995)MathSciNetCrossRefGoogle Scholar
  14. 14.
    Hunter, A., Polberg, S., Potyka, N.: Updating belief in arguments in epistemic graphs. In: International Conference on Principles of Knowledge Representation and Reasoning (KR), pp. 138–147 (2018)Google Scholar
  15. 15.
    Hunter, A., Thimm, M.: Probabilistic reasoning with abstract argumentation frameworks. J. Artif. Intell. Res. 59, 565–611 (2017)MathSciNetCrossRefGoogle Scholar
  16. 16.
    Leite, J., Martins, J.: Social abstract argumentation. In: International Joint Conferences on Artificial Intelligence (IJCAI), vol. 11, pp. 2287–2292 (2011)Google Scholar
  17. 17.
    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_1CrossRefGoogle Scholar
  18. 18.
    Mossakowski, T., Neuhaus, F.: Modular semantics and characteristics for bipolar weighted argumentation graphs. arXiv preprint arXiv:1807.06685 (2018)
  19. 19.
    Polberg, S., Doder, D.: Probabilistic abstract dialectical frameworks. In: Fermé, E., Leite, J. (eds.) JELIA 2014. LNCS (LNAI), vol. 8761, pp. 591–599. Springer, Cham (2014).  https://doi.org/10.1007/978-3-319-11558-0_42CrossRefGoogle Scholar
  20. 20.
    Potyka, N.: Continuous dynamical systems for weighted bipolar argumentation. In: International Conference on Principles of Knowledge Representation and Reasoning (KR), pp. 148–157 (2018)Google Scholar
  21. 21.
    Potyka, N.: Extending modular semantics for bipolar weighted argumentation. In: International Conference on Autonomous Agents and MultiAgent Systems (AAMAS), pp. 1722–1730. International Foundation for Autonomous Agents and Multiagent Systems (2019)Google Scholar
  22. 22.
    Rago, A., Toni, F., Aurisicchio, M., Baroni, P.: Discontinuity-free decision support with quantitative argumentation debates. In: International Conference on Principles of Knowledge Representation and Reasoning (KR), pp. 63–73 (2016)Google Scholar
  23. 23.
    Rienstra, T., Thimm, M., Liao, B., van der Torre, L.: Probabilistic abstract argumentation based on SCC decomposability. In: International Conference on Principles of Knowledge Representation and Reasoning (KR), pp. 168–177 (2018)Google Scholar
  24. 24.
    Thiel, M., Ludwig, P., Mossakowski, T., Neuhaus, F., Nürnberger, A.: Web-retrieval supported argument space exploration. In: ACM SIGIR Conference on Human Information Interaction and Retrieval (CHIIR), pp. 309–312. ACM (2017)Google Scholar

Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  1. 1.Institute of Cognitive ScienceUniversity of OsnabrückOsnabrückGermany

Personalised recommendations