Abstract
Participants in argumentation often have some doubts in their arguments and/or the arguments of the other participants. In this paper, we model uncertainty in beliefs using a probability distribution over models of the language, and use this to identify which are good arguments (i.e. those with support with a probability on or above a threshold). We then investigate three strategies for participants in dialogical argumentation that use this uncertainty information. The first is an exhaustive strategy for presenting a participant’s good arguments, the second is a refinement of the first that selects the good arguments that are also good arguments for the opponent, and the third selects any argument as long as it is a good argument for the opponent. We show that the advantage of the second strategy is that on average it results in shorter dialogues than the first strategy, and the advantage of the third strategy is that under some general circumstances the participant can always win the dialogue.
Access provided by Autonomous University of Puebla. Download to read the full chapter text
Chapter PDF
Similar content being viewed by others
Keywords
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.
References
Amgoud, L., Prade, H.: Using arguments for making and explaining decisions. Artificial Intelligence 173(3-4), 413–436 (2009)
Dung, P.: On the acceptability of arguments and its fundamental role in nonmonotonic reasoning, logic programming, and n-person games. Artificial Intelligence 77, 321–357 (1995)
Paris, J.: The Uncertain Reasoner’s Companion: A Methematical Perspective. Cambridge University Press (1994)
Hunter, A.: A probabilistic approach to modelling uncertain logical arguments. International Journal of Approximate Reasoning 54(1), 47–81 (2013)
Elvang-Gransson, M., Krause, P., Fox, J.: Acceptability of arguments as logical uncertainty. In: Moral, S., Kruse, R., Clarke, E. (eds.) ECSQARU 1993. LNCS, vol. 747, pp. 85–90. Springer, Heidelberg (1993)
Cayrol, C.: On the relation between argumentation and non-monotonic coherence-based entailment. In: Proceedings of the Fourteenth International Joint Conference on Artificial Intelligence (IJCAI 1995), pp. 1443–1448 (1995)
Kendall, M.: A new measure of rank correlation. Biometrika 30(1-2), 81–93 (1938)
Amgoud, L., Cayrol, C.: A reasoning model based on the production of acceptable arguments. Annals of Mathematics and Artificial Intelligence 34, 197–216 (2002)
Bench-Capon, T.: Persuasion in practical argument using value based argumentationframeworks. Journal of Logic and Computation 13(3), 429–448 (2003)
Dunne, P.E., Hunter, A., McBurney, P., Parsons, S., Wooldridge, M.: Weighted argument systems: Basic definitions, algorithms, and complexity results. Artificial Intelligence 175(2), 457–486 (2011)
Haenni, R.: Cost-bounded argumentation. International Journal of Approximate Reasoning 26(2), 101–127 (2001)
Dung, P., Thang, P.: Towards (probabilistic) argumentation for jury-based dispute resolution. In: Computational Models of Argument (COMMA 2010), pp. 171–182. IOS Press (2010)
Li, H., Oren, N., Norman, T.: Probabilistic argumentation frameworks. In: Modgil, S., Oren, N., Toni, F. (eds.) TAFA 2011. LNCS, vol. 7132, pp. 1–16. Springer, Heidelberg (2012)
Thimm, M.: A probabilistic semantics for abstract argumentation. In: Proceedings of the European Conference on Artificial Intelligence (ECAI 2012), pp. 750–755 (2012)
Hunter, A.: Some foundations for probabilistic argumentation. In: Proceedings of the International Comference on Computational Models of Argument (COMMA 2012), pp. 117–128 (2012)
Alsinet, T., Chesñevar, C., Godo, L., Simari, G.: A logic programming framework for possibilistic argumentation: Formalization and logical properties. Fuzzy Sets and Systems 159(10), 1208–1228 (2008)
Amgoud, L., Maudet, N., Parsons, S.: Arguments, dialogue and negotiation. In: Fourteenth European Conference on Artifcial Intelligence (ECAI 2000), pp. 338–342. IOS Press (2000)
Prakken, H.: Formal sytems for persuasion dialogue. Knowledge Engineering Review 21(2), 163–188 (2006)
Prakken, H.: Coherence and flexibility in dialogue games for argumentation. Journal of Logic and Computation 15(6), 1009–1040 (2005)
Black, E., Hunter, A.: An inquiry dialogue system. Autonomous Agents and Multi-Agent Systems 19(2), 173–209 (2009)
Fan, X., Toni, F.: Assumption-based argumentation dialogues. In: Proceedings of International Joint Conference on Artificial Intelligence (IJCAI 2011), pp. 198–203 (2011)
Caminada, M., Podlaszewski, M.: Grounded semantics as persuasion dialogue. In: Computational Models of Argument (COMMA 2012), pp. 478–485 (2012)
Rahwan, I., Larson, K.: Mechanism design for abstract argumentation. In: Proceedings of the 7th International Joint Conference on Autonomous Agents and Multiagent Systems (AAMAS 2008, IFAAMAS), pp. 1031–1038 (2008)
Rahwan, I., Larson, K., Tohmé, F.: A characterisation of strategy-proofness for grounded argumentation semantics. In: Proceedings of the 21st International Joint Conference on Artificial Intelligence (IJCAI 2009), pp. 251–256 (2009)
Fan, X., Toni, F.: Mechanism design for argumentation-based persuasion. In: Computational Models of Argument (COMMA 2012), pp. 322–333 (2012)
Oren, N., Atkinson, K., Li, H.: Group persuasion through uncertain audience modelling. In: Proceedings of the International Comference on Computational Models of Argument (COMMA 2012), pp. 350–357 (2012)
Hunter, A.: Towards higher impact argumentation. In: Proceedings of the 19th National Conference on Artificial Intelligence (AAAI 2004), pp. 275–280. MIT Press (2004)
Hunter, A.: Making argumentation more believable. In: Proceedings of the 19th National Conference on Artificial Intelligence (AAAI 2004), pp. 269–274. MIT Press (2004)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2013 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Hunter, A. (2013). Modelling Uncertainty in Persuasion. In: Liu, W., Subrahmanian, V.S., Wijsen, J. (eds) Scalable Uncertainty Management. SUM 2013. Lecture Notes in Computer Science(), vol 8078. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-40381-1_5
Download citation
DOI: https://doi.org/10.1007/978-3-642-40381-1_5
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-40380-4
Online ISBN: 978-3-642-40381-1
eBook Packages: Computer ScienceComputer Science (R0)