PADUA Protocol: Strategies and Tactics

  • Maya Wardeh
  • Trevor Bench-Capon
  • Frans Coenen
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4724)


In this paper we describe an approach to classifying objects in a domain where classifications are uncertain using a novel combination of argumentation and data mining. Classification is the topic of a dialogue game between two agents, based on an argument scheme and critical questions designed for use by agents whose knowledge of the domain comes from data mining. Each agent has its own set of examples which it can mine to find arguments based on association rules for and against a classification of a new instance. These arguments are exchanged in order to classify the instance. We describe the dialogue game, and in particular discuss the strategic considerations which agents can use to select their moves. Different strategies give rise to games with different characteristics, some having the flavour of persuasion dialogues and other deliberation dialogues.


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    Agrawal, R., Imielinski, T., Swami, A.N.: Association rules between sets of items in large databases. In: Proc. of ACM SIGMOD Int. Conf. on Management of Data, Washington, pp. 207–216 (May 1993)Google Scholar
  2. 2.
    Amgoud, L., Maudet, N.: Strategical considerations for argumentative agents (preliminary report). In: Proc. of 9th Int. Workshop on Non-Monotonic Reasoning (NMR), Toulouse, France, April 2002, Special session on Argument, Dialogue, Decision, pp. 409–417 (2002)Google Scholar
  3. 3.
    Amgoud, L., Parsons, S.: Agent dialogues with conflicting preferences. In: Proc. of 8th Int. Workshop on Agent Theories, Architectures and Languages, Seattle, Washignton, August 2001, pp. 1–15 (2001)Google Scholar
  4. 4.
    Coenen, F.P., Leng, P., Goulbourne, G.: Tree Structures for Mining Association Rules. Journal of Data Mining and Knowledge Discovery 8(1), 25–51 (2004)MathSciNetCrossRefGoogle Scholar
  5. 5.
    Hamblin, C.L.: Fallacies. Methuen (1970)Google Scholar
  6. 6.
    Kakas, A.C., Maudet, N., Moraitis, P.: In: Rahwan, I., Moraïtis, P., Reed, C. (eds.) ArgMAS 2004. LNCS (LNAI), vol. 3366. Springer, Heidelberg (2005)CrossRefGoogle Scholar
  7. 7.
    Mcburney, P., Parsons, S.: Games That Agents Play: A Formal Framework for Dialogues between Autonomous Agents. Jo. of logic, language and information 11(3), 315–334 (2002)MathSciNetCrossRefMATHGoogle Scholar
  8. 8.
    Moore, D.: Dialogue game theory for intelligent tutoring systems. Ph.D thesis, Leeds Metropolitan University (1993)Google Scholar
  9. 9.
    Newman, D.J., Hettich, S., Blake, C.L., Merz, C.J.: UCI Repository of machine learning databases. University of California, Irvine, Dept. of Information and Computer Sciences (1998),
  10. 10.
    Oren, N., Norman, T.J., Preece, A.: Loose Lips Sink Ships: a Heuristic for Argumentation. In: Proc. of 3rd Int. Workshop on Argumentation in Multi-Agent Systems (Argmas 2006), Hakodate, Japan, May 2006, pp. 121–134 (2006),
  11. 11.
    Walton, D.N., Krabbe, E.C.W.: Commitment in Dialogue: Basic Concepts of Interpersonal Reasoning. SUNY Press, Albany (1995)Google Scholar
  12. 12.
    Walton, D.N.: Argument Schemes for Presumptive Reasoning. Lawrence Erlbaum Associates, Mahwah (1996)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2007

Authors and Affiliations

  • Maya Wardeh
    • 1
  • Trevor Bench-Capon
    • 1
  • Frans Coenen
    • 1
  1. 1.Department of Computer ScienceThe University of LiverpoolLiverpoolUK

Personalised recommendations