2018 • In Principles of Knowledge Representation and Reasoning: Proceedings of the Sixteenth International Conference, KR 2018, Tempe, Arizona 30 October - 2 November 2018.
[en] In this paper we introduce a new set of general principles for probabilistic abstract argumentation. The main principle is a probabilistic analogue of SCC decomposability, which ensures that the probabilistic evaluation of an argumentation framework complies with the probabilistic (in)dependencies implied by the graph topology. We introduce various examples of probabilistic semantics and determine which principles they satisfy. Our work also provides new insights into the relationship between abstract argumentation and the theory of Bayesian networks.
Disciplines :
Computer science
Author, co-author :
Rienstra, Tjitze
Thimm, Matthias
Liao, Beishui ; University of Luxembourg > Faculty of Science, Technology and Communication (FSTC) > Computer Science and Communications Research Unit (CSC)
van der Torre, Leon ; University of Luxembourg > Faculty of Science, Technology and Communication (FSTC) > Computer Science and Communications Research Unit (CSC)
External co-authors :
yes
Language :
English
Title :
Probabilistic Abstract Argumentation Based on SCC Decomposability
Publication date :
2018
Event name :
Sixteenth International Conference on Principles of Knowledge Representation and Reasoning (KR 2018)
Event date :
October 27 - November 2 2018
Main work title :
Principles of Knowledge Representation and Reasoning: Proceedings of the Sixteenth International Conference, KR 2018, Tempe, Arizona 30 October - 2 November 2018.
Baroni, P., and Giacomin, M. 2007. On principle-based evaluation of extension-based argumentation semantics. Artificial Intelligence 171(10-15):675–700.
Baroni, P.; Boella, G.; Cerutti, F.; Giacomin, M.; van der Torre, L.; and Villata, S. 2014. On the input/output behavior of argumentation frameworks. Artificial Intelligence 217:144–197.
Baroni, P.; Gabbay, D.; Giacomin, M.; and van der Torre, L., eds. 2018. Handbook of Formal Argumentation. College Publications.
Baroni, P.; Dunne, P. E.; and Giacomin, M. 2011. On the resolution-based family of abstract argumentation semantics and its grounded instance. Artif. Intell. 175(3-4):791–813.
Baroni, P.; Giacomin, M.; and Guida, G. 2005. Sccrecursiveness: a general schema for argumentation semantics. Artificial Intelligence 168(1-2):162–210.
Baroni, P.; Giacomin, M.; and Vicig, P. 2014. On rationality conditions for epistemic probabilities in abstract argumentation. In Proceedings of the Fifth International Conference on Computational Models of Argumentation (COMMA’14).
Caminada, M. 2006. On the issue of reinstatement in argumentation. In Logics in Artificial Intelligence, 10th European Conference, JELIA 2006, Liverpool, UK, September 13-15, 2006, Proceedings, volume 4160 of LNCS, 111–123. Springer.
Dung, P. M. 1995. On the acceptability of arguments and its fundamental role in nonmonotonic reasoning, logic programming and n-person games. Artificial Intelligence 77(2):321–358.
Gabbay, D. M., and Rodrigues, O. 2016. Introducing bayesian argumentation networks. The IfColog Journal of Logics and their Applications 3(2):241–278.
Gabbay, D. M. 2009. Fibring argumentation frames. Studia Logica 93(2-3):231–295.
Grossi, D., and van der Hoek, W. 2013. Audience-based uncertainty in abstract argument games. In IJCAI, 143–149. IJCAI/AAAI.
Hunter, A., and Thimm, M. 2016a. On partial information and contradictions in probabilistic abstract argumentation. In Baral, C.; Delgrande, J. P.; and Wolter, F., eds., Principles of Knowledge Representation and Reasoning: Proceedings of the Fifteenth International Conference, KR 2016, Cape Town, South Africa, April 25-29, 2016., 53–62. AAAI Press.
Hunter, A., and Thimm, M. 2016b. Optimization of dialectical outcomes in dialogical argumentation. International Journal of Approximate Reasoning 78:73–102.
Hunter, A., and Thimm, M. 2017. Probabilistic reasoning with abstract argumentation frameworks. Journal of Artificial Intelligence Research 59:565–611.
Hunter, A. 2013. A probabilistic approach to modelling uncertain logical arguments. International Journal of Approximate Reasoning 54(1):47–81.
Hunter, A. 2015. Modelling the persuadee in asymmetric argumentation dialogues for persuasion. In Yang, Q., and Wooldridge, M., eds., Proceedings of the Twenty-Fourth International Joint Conference on Artificial Intelligence, IJCAI 2015, Buenos Aires, Argentina, July 25-31, 2015, 3055–3061. AAAI Press.
Koller, D., and Friedman, N. 2009. Probabilistic Graphical Models - Principles and Techniques. MIT Press.
Li, H.; Oren, N.; and Norman, T. J. 2011. Probabilistic argumentation frameworks. In Theory and Applications of Formal Argumentation - First International Workshop, TAFA 2011. Barcelona, Spain, July 16-17, 2011, Revised Selected Papers, volume 7132 of LNCS, 1–16. Springer.
Nielsen, S. H., and Parsons, S. 2007. An application of formal argumentation: Fusing bayesian networks in multiagent systems. Artif. Intell. 171(10-15):754–775.
Rienstra, T., and Thimm, M. 2018. Ranking functions over labelings. In Proceedings of the Seventh International Conference on Computational Models of Argument (COMMA-2018), September 11-14, 2018, Warsaw, Poland. IOS Press.
Rienstra, T.; Thimm, M.; and Oren, N. 2013. Opponent models with uncertainty for strategic argumentation. In IJCAI, 332–338. IJCAI/AAAI.
Rienstra, T. 2012. Towards a probabilistic Dung-style argumentation system. In The First International Conference on Agreement Technologies, AT 2012, Dubrovnik, Croatia, October 15-16, 2012, volume 918 of CEUR Workshop Proceedings, 138–152. CEUR-WS.org.
Thimm, M.; Baroni, P.; Giacomin, M.; and Vicig, P. 2017. Probabilities on extensions in abstract argumentation. In Proceedings of the 2017 International Workshop on Theory and Applications of Formal Argument (TAFA’17).
Thimm, M. 2012. A probabilistic semantics for abstract argumentation. In ECAI 2012 - 20th European Conference on Artificial Intelligence, Montpellier, France, August 27-31, 2012, volume 242 of Frontiers in Artificial Intelligence and Applications, 750–755. IOS Press.
Timmer, S. T.; Meyer, J.-J. C.; Prakken, H.; Renooij, S.; and Verheij, B. 2017. A two-phase method for extracting explanatory arguments from bayesian networks. International Journal of Approximate Reasoning 80:475–494.
Toniolo, A.; Norman, T. J.; and Oren, N. 2017. Enumerating preferred extensions: A case study of human reasoning. In Proceedings of the 2017 International Workshop on Theory and Applications of Formal Argument (TAFA’17).
van der Torre, L., and Vesic, S. 2017. The principle-based approach to abstract argumentation semantics. The IfCoLog Journal of Logics and their Applications.
Vreeswijk, G. 2004. Argumentation in bayesian belief networks. In ArgMAS, volume 3366 of Lecture Notes in Computer Science, 111–129. Springer.
