Exploiting Rough Argumentation in an Online Dispute Resolution Mediator

  • Ioan Alfred Letia
  • Adrian Groza
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4585)


Online dispute resolution is becoming the main method when dealing with a conflict in e-commerce. Our framework exploits the argumentation semantics of defeasible logic, shown to be a suitable choice for legal reasoning. We introduce the rough set theory within defeasible logic for handling the gradual information revealed in a legal dispute. The rough sets are being used in the generation of defeasible theories from available cases, but also in the inference rules required for the argumentation process. The framework can cover both aspects of the law: case based reasoning and legal syllogism.


Dispute Resolution Case Base Reasoning Argumentation Framework Certainty Factor Defeasible Reasoning 
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.


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    Tyler, M.C., Bretherton, D.: Seventy-six and counting: An analysis of ODR sites. In: Workshop on Online Dispute Resolution at the International Conference on Artificial Intelligence and Law, Edinburgh, pp. 13–28 (2003)Google Scholar
  2. 2.
    Walton, D., Godden, D.: Persuasion dialogues in online dispute resolution. Artificial Intelligence and Law 13, 273–295 (2006)CrossRefGoogle Scholar
  3. 3.
    Johnston, B., Governatori, G.: An algorithm for the induction of defeasible logic theories from databases. In: 14th Australasian Database Conference, Darlinghurst, Australia, pp. 75–83 (2003)Google Scholar
  4. 4.
    Rule, C., Friedberg, L.: The appropriate role of dispute resolution in building trust online. Artificial Intelligence and Law 13, 193–205 (2006)CrossRefGoogle Scholar
  5. 5.
    Hage, J.: Law and defeasibility. Artificial Intelligence and Law 11, 221–243 (2003)CrossRefMathSciNetGoogle Scholar
  6. 6.
    Pawlak, Z.: A primer on rough sets: a new approach to drawing conclusion from data. Cardozo Law Review 22, 1407–1415 (2002)Google Scholar
  7. 7.
    Li, J., Cercone, N.: Introducing a rule importance measure. In: Peters, J.F., Skowron, A. (eds.) Transactions on Rough Sets V. LNCS, vol. 4100, pp. 167–189. Springer, Heidelberg (2006)CrossRefGoogle Scholar
  8. 8.
    Szczuka, M.: Techniques for managing rule-based concept approximation. In: International Workshop on Soft Computing at Intelligent Agent Technology, Compiegne, France, pp. 31–35 (2005)Google Scholar
  9. 9.
    Pollock, J.L.: Defeasible reasoning with variable degrees of justification. Artificial Intelligence 133, 233–282 (2001)zbMATHCrossRefMathSciNetGoogle Scholar
  10. 10.
    Doherty, P., Lukaszewicz, W., Szalas, A.: Communication between agents with heterogeneous perceptual capabilities. Information Fusion 8, 56–69 (2007)CrossRefGoogle Scholar
  11. 11.
    Rebolledo, M.: Rough intervals – enhancing intervals for qualitative modeling of technical systems. Artificial Intelligence 170, 667–685 (2006)CrossRefGoogle Scholar
  12. 12.
    Parsons, S., Wooldridge, M., Amgoud, L.: Properties and complexity of some formal inter-agent dialogues. Journal of Logic and Computation 13, 347–376 (2003)zbMATHCrossRefMathSciNetGoogle Scholar
  13. 13.
    Governatori, G., Stranieri, A.: Towards the application of association rules for defeasible rules discovery. In: Verheij, B., Lodder, A., Loui, R.P., Muntjerwerff, A.J. (eds.) Legal Knowledge and Information Systems, pp. 63–75. IOS Press, Amsterdam (2001)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2007

Authors and Affiliations

  • Ioan Alfred Letia
    • 1
  • Adrian Groza
    • 1
  1. 1.Technical University of Cluj-Napoca, Department of Computer Science, Baritiu 28, RO-400391 Cluj-NapocaRomania

Personalised recommendations