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Probabilistic Argumentation Frameworks

  • Conference paper
Theorie and Applications of Formal Argumentation (TAFA 2011)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 7132))

Abstract

In this paper, we extend Dung’s seminal argument framework to form a probabilistic argument framework by associating probabilities with arguments and defeats. We then compute the likelihood of some set of arguments appearing within an arbitrary argument framework induced from this probabilistic framework. We show that the complexity of computing this likelihood precisely is exponential in the number of arguments and defeats, and thus describe an approximate approach to computing these likelihoods based on Monte-Carlo simulation. Evaluating the latter approach against the exact approach shows significant computational savings. Our probabilistic argument framework is applicable to a number of real world problems; we show its utility by applying it to the problem of coalition formation.

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References

  1. Agresti, A., Coull, B.A.: Approximate is better than “exact” for interval estimation of binomial proportions. The American Statistician 52(2), 119–126 (1998)

    Article  MathSciNet  Google Scholar 

  2. Amgoud, L., Cayrol, C.: Inferring from inconsistency in preference-based argumentation frameworks. Journal of Automated Reasoning 29, 125–169 (2002)

    Article  MathSciNet  MATH  Google Scholar 

  3. Baroni, P., Dunne, P.E., Giacomin, M.: On extension counting problems in argumentation frameworks. In: Proceeding of the 2010 Conference on Computational Models of Argument: Proceedings of COMMA 2010, pp. 63–74. IOS Press, Amsterdam (2010)

    Google Scholar 

  4. Baroni, P., Giacomin, M.: Semantics of abstract argument systems. In: Simari, G., Rahwan, I. (eds.) Argumentation in Artificial Intelligence, pp. 25–44. Springer, US (2009)

    Chapter  Google Scholar 

  5. Bench-Capon, T.: Value based argumentation frameworks. In: Proceedings of the 9th International Workshop on Nonmonotonic Reasoning, Toulouse, France, pp. 444–453 (2002)

    Google Scholar 

  6. Burnett, C., Norman, T.J., Sycara, K.: Stereotypical trust and bias in dynamic multi-agent systems. ACM Transactions on Intelligent Systems and Technology (in press)

    Google Scholar 

  7. Cayrol, C., Lagasquie-Schiex, M.C.: Bipolar Abstract Argumentation Systems. In: Rahwan, I., Simari, G. (eds.) Argumentation in Artificial Intelligence, ch. 4, pp. 65–84. Springer, Heidelberg (2009), http://www.springerlink.com

    Chapter  Google Scholar 

  8. Coulom, R.: Efficient Selectivity and Backup Operators in Monte-Carlo Tree Search. In: van den Herik, H.J., Ciancarini, P., Donkers, H.H.L.M(J.) (eds.) CG 2006. LNCS, vol. 4630, pp. 72–83. Springer, Heidelberg (2007)

    Chapter  Google Scholar 

  9. Dung, P.M.: On the acceptability of arguments and its fundamental role in nonmonotonic reasoning, logic programming and n-person games. Artificial Intelligence 77(2), 321–357 (1995)

    Article  MathSciNet  MATH  Google Scholar 

  10. 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)

    Article  MathSciNet  MATH  Google Scholar 

  11. Dunne, P.E., Wooldridge, M.: Complexity of abstract argumentation. In: Simari, G., Rahwan, I. (eds.) Argumentation in Artificial Intelligence, pp. 85–104. Springer, US (2009)

    Chapter  Google Scholar 

  12. Emele, C.D., Norman, T.J., Parsons, S.: Argumentation strategies for plan resourcing. In: Proceedings of the Tenth International Conference on Autonomous Agents and Multiagent Systems (2011)

    Google Scholar 

  13. Erriquez, E., van der Hoek, W., Wooldridge, M.: An abstract framework for reasoning about trust. In: Proceedings of AAMAS 2011 (to appear, 2011)

    Google Scholar 

  14. Gómez Lucero, M.J., Chesñevar, C.I., Simari, G.R.: Modelling Argument Accrual in Possibilistic Defeasible Logic Programming. In: Sossai, C., Chemello, G. (eds.) ECSQARU 2009. LNCS, vol. 5590, pp. 131–143. Springer, Heidelberg (2009)

    Chapter  Google Scholar 

  15. Janssen, J., Cock, M.D., Vermeir, D.: Fuzzy argumentation frameworks. In: Proceedings of IMPU 2008 (12th International Conference on Information Processing and Management of Uncertainty in Knowledge-Based Systems), pp. 513–520 (2008)

    Google Scholar 

  16. Kohlas, J., Haenni, R.: Assumption-based reasoning and probabilistic argumentation systems. Tech. Rep. 96–07, Institute of Informatics, University of Fribourg, Switzerland (1996)

    Google Scholar 

  17. Krause, P., Ambler, S., Elvang-Goransson, M., Fox, J.: A logic of argumentation for reasoning under uncertainty. Computational Intelligence 11(1), 113–131 (1995)

    Article  MathSciNet  Google Scholar 

  18. Lewicki, P., Hill, T.: Statistics: Methods and Applications. StatSoft Inc. (2005)

    Google Scholar 

  19. Mitchell, T.M.: Machine Learning. McGraw-Hill Higher Education (1997)

    Google Scholar 

  20. Oren, N., Norman, T.J.: Semantics for evidence-based argumentation. In: Computational Models of Argument: Proceedings of COMMA 2008, Toulouse, France, May 28-30, pp. 276–284 (2008)

    Google Scholar 

  21. Oren, N., Norman, T.J.: Arguing Using Opponent Models. In: McBurney, P., Rahwan, I., Parsons, S., Maudet, N. (eds.) ArgMAS 2009. LNCS, vol. 6057, pp. 160–174. Springer, Heidelberg (2010)

    Chapter  Google Scholar 

  22. Oren, N., Norman, T.J., Preece, A.: Arguing with confidential information. In: Proceedings of the 18th European Conference on Artificial Intelligence, Riva del Garda, Italy, pp. 280–284 (August 2006)

    Google Scholar 

  23. Parsons, S.: Normative Argumentation and Qualitative Probability. In: Gabbay, D.M., Kruse, R., Nonnengart, A., Ohlbach, H.J. (eds.) FAPR 1997 and ECSQARU 1997. LNCS, vol. 1244, pp. 466–480. Springer, Heidelberg (1997)

    Chapter  Google Scholar 

  24. Patel, J., Teacy, W.T.L., Jennings, N.R., Luck, M., Chalmers, S., Oren, N., Norman, T.J., Preece, A., Gray, P.M.D., Shercliff, G., Stockreisser, P.J., Shao, J., Gray, W.A., Fiddian, N.J., Thompson, S.: Agent-based virtual organisations for the grid. Multiagent and Grid Systems 1(4), 237–249 (2006)

    Google Scholar 

  25. Pearl, J.: Probabilistic reasoning in intelligent systems: networks of plausible inference. Morgan Kaufmann Publishers Inc., San Francisco (1988)

    Google Scholar 

  26. Pechoucek, M., Marík, V., Bárta, J.: A knowledge-based approach to coalition formation. IEEE Intelligent Systems 17, 17–25 (2002)

    Google Scholar 

  27. Pollock, J.L.: Cognitive Carpentry. Bradford/MIT Press (1995)

    Google Scholar 

  28. Rahwan, T.: Algorithms for Coalition Formation in Multi-Agent Systems. Ph.D. thesis, University of Southampton (2007)

    Google Scholar 

  29. Riveret, R., Prakken, H., Rotolo, A., Sartor, G.: Heuristics in argumentation: A game theory investigation. In: Computational Models of Argument: Proceedings of COMMA 2008, Toulouse, France, May 28-30, pp. 324–335 (2008)

    Google Scholar 

  30. Rotstein, N., Oren, N., Norman, T.J.: Resource bounded argumentation frameworks. Tech. rep., University of Aberdeen (2011)

    Google Scholar 

  31. Sutton, R.S., Barto, A.G.: Reinforcement Learning: An Introduction (Adaptive Computation and Machine Learning). The MIT Press (March 1998)

    Google Scholar 

  32. Teacy, W.T.L., Patel, J., Jennings, N.R., Luck, M.: Travos: Trust and reputation in the context of inaccurate information sources. Autonomous Agents and Multi-Agent Systems 12(2), 183–198 (2006)

    Article  Google Scholar 

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Author information

Authors and Affiliations

  1. Department of Computing Science, University of Aberdeen, Aberdeen, AB24 3UE, Scotland, UK

    Hengfei Li, Nir Oren & Timothy J. Norman

Authors
  1. Hengfei Li
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  2. Nir Oren
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  3. Timothy J. Norman
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Editor information

Editors and Affiliations

  1. King’s College London, Strand, WC2R 2LS, London, UK

    Sanjay Modgil

  2. University of Aberdeen, AB24 3UE, Aberdeen, Scotland, UK

    Nir Oren

  3. Department of Computing, Imperial College London, South Kensington Campus, SW7 2AZ, London, UK

    Francesca Toni

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© 2012 Springer-Verlag Berlin Heidelberg

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Li, H., Oren, N., Norman, T.J. (2012). Probabilistic Argumentation Frameworks. In: Modgil, S., Oren, N., Toni, F. (eds) Theorie and Applications of Formal Argumentation. TAFA 2011. Lecture Notes in Computer Science(), vol 7132. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-29184-5_1

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  • DOI: https://doi.org/10.1007/978-3-642-29184-5_1

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-29183-8

  • Online ISBN: 978-3-642-29184-5

  • eBook Packages: Computer ScienceComputer Science (R0)

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