Advertisement

Web Based System for Weighted Defeasible Argumentation

  • Alsinet Teresa
  • Béjar Ramón
  • Francesc Guitart
  • Lluís Godo
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8143)

Abstract

In a previous work we defined a recursive semantics for reasoning about which arguments should be warranted when extending Defeasible Argumentation with defeasibility levels for arguments. Our approach is based on a general notion of collective conflict among arguments and on the fact that if an argument is warranted it must be that all its sub-arguments also are warranted. An output of a program is a pair consisting of a set of warranted and a set of blocked arguments with maximum strength. Arguments that are neither warranted nor blocked correspond to rejected arguments. On this recursive semantics a program may have multiple outputs in case of circular definitions of conflicts among arguments and for these circular definitions of conflicts we define what output, called maximal ideal output, should be considered based on the claim that if an argument is excluded from an output, then all the arguments built on top of it should also be excluded from that output. In this paper we show a web based system we have designed and implemented to compute the output for programs with single and multiple outputs. For programs with multiple outputs the system also computes the maximal ideal output. An interesting feature of the system is that it provides not only both sets of warranted an blocked arguments with maximum strength but also useful information that allows to better understand why an argument is either warranted, blocked or rejected.

Keywords

weighted defeasible argumentation recursive semantics web based technologies 

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Alsinet, T., Béjar, R., Godo, L.: A characterization of collective conflict for defeasible argumentation. In: Proceedings of COMMA 2010. Frontiers in Artificial Intelligence and Applications, vol. 216, pp. 27–38. IOS Press (2010)Google Scholar
  2. 2.
    Alsinet, T., Béjar, R., Godo, L., Guitart, F.: Maximal ideal recursive semantics for defeasible argumentation. In: Benferhat, S., Grant, J. (eds.) SUM 2011. LNCS, vol. 6929, pp. 96–109. Springer, Heidelberg (2011)CrossRefGoogle Scholar
  3. 3.
    Alsinet, T., Béjar, R., Godo, L., Guitart, F.: Using answer set programming for an scalable implementation of defeasible argumentation. In: ICTAI, pp. 1016–1021 (2012)Google Scholar
  4. 4.
    Alsinet, T., Béjar, R., Godo, L., Guitart, F.: On the implementation of a multiple outputs algorithm for defeasible argumentation. In: Proceedings of SUM 2013 (in press, 2013)Google Scholar
  5. 5.
    Alsinet, T., Béjar, R., Godo, L., Guitart, F.: RP-DeLP: A weighted defeasible argumentation framework based on a recursive semantics. Journal of Logic and Computation: Special Issue on Loops in Argumentation (submitted)Google Scholar
  6. 6.
    Amgoud, L.: Postulates for logic-based argumentation systems. In: Proceedings of the ECAI 2012 Workshop WL4AI, pp. 59–67 (2012)Google Scholar
  7. 7.
    Bench-Capon, T.J.M., Dunne, P.E.: Argumentation in artificial intelligence. Artif. Intell. 171(10-15), 619–641 (2007)MathSciNetCrossRefzbMATHGoogle Scholar
  8. 8.
    Besnard, P., Hunter, A.: Elements of Argumentation. The MIT Press (2008)Google Scholar
  9. 9.
    Bouyias, Y.N., Demetriadis, S.N., Tsoukalas, I.A.: iargue: A web-based argumentation system supporting collaboration scripts with adaptable fading. In: Proceedings of ICALT 2008, pp. 477–479 (2008)Google Scholar
  10. 10.
    Cartwright, D., Atkinson, K.: Using computational argumentation to support e-participation. IEEE Intelligent Systems 24(5), 42–52 (2009)CrossRefGoogle Scholar
  11. 11.
    Minh Dung, P., Mancarella, P., Toni, F.: A dialectic procedure for sceptical, assumption-based argumentation. In: Proceedings of COMMA 2008. Frontiers in Artificial Intelligence and Applications, vol. 172, pp. 145–156. IOS Press (2006)Google Scholar
  12. 12.
    Minh Dung, P., Mancarella, P., Toni, F.: Computing ideal sceptical argumentation. Artif. Intell. 171(10-15), 642–674 (2007)CrossRefGoogle Scholar
  13. 13.
    Eén, N., Sörensson, N.: An extensible SAT-solver. In: Giunchiglia, E., Tacchella, A. (eds.) SAT 2003. LNCS, vol. 2919, pp. 502–518. Springer, Heidelberg (2004)CrossRefGoogle Scholar
  14. 14.
    García, A., Simari, G.: Defeasible Logic Programming: An Argumentative Approach. Theory and Practice of Logic Programming 4(1), 95–138 (2004)MathSciNetCrossRefzbMATHGoogle Scholar
  15. 15.
    García, A.J., Rotstein, N.D., Tucat, M., Simari, G.R.: An argumentative reasoning service for deliberative agents. In: Zhang, Z., Siekmann, J.H. (eds.) KSEM 2007. LNCS (LNAI), vol. 4798, pp. 128–139. Springer, Heidelberg (2007)CrossRefGoogle Scholar
  16. 16.
    Gebser, M., Kaminski, R., Kaufmann, B., Ostrowski, M., Schaub, T., Schneider, M.: Potassco: The Potsdam answer set solving collection. AI Commun. 24(2), 107–124 (2011)MathSciNetzbMATHGoogle Scholar
  17. 17.
    Governatori, G., Maher, M.J., Antoniou, G., Billington, D.: Argumentation semantics for defeasible logic. J. Log. Comput. 14(5), 675–702 (2004)MathSciNetCrossRefzbMATHGoogle Scholar
  18. 18.
    Pollock, J.L.: A recursive semantics for defeasible reasoning. In: Rahwan, I., Simari, G.R. (eds.) Argumentation in Artificial Intelligence, ch. 9, pp. 173–198. Springer (2009)Google Scholar
  19. 19.
    Prakken, H., Vreeswijk, G.: Logical Systems for Defeasible Argumentation. In: Gabbay, D., Guenther, F. (eds.) Handbook of Phil. Logic, pp. 219–318. Kluwer (2002)Google Scholar
  20. 20.
    Rahwan, I., Simari, G.R. (eds.): Argumentation in Artificial Intelligence. Springer (2009)Google Scholar
  21. 21.
    Schlesinger, F., Errecalde, M., Aguirre, G.: An approach to integrate web services and argumentation into a BDI system (extended abstract). In: van der Hoek, Kaminka, Lespérance, Luck, Sen (eds.) Proceedings of AAMAS 2010, pp. 1371–1372 (2010)Google Scholar
  22. 22.
    Tsai, C.Y., Jack, B.M., Huang, T.C., Yang, J.T.: Using the cognitive apprenticeship web-based argumentation system to improve argumentation instruction. Journal of Science Education and Technology 21, 476–486 (2012)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  • Alsinet Teresa
    • 1
  • Béjar Ramón
    • 1
  • Francesc Guitart
    • 1
  • Lluís Godo
    • 2
  1. 1.Department of Computer ScienceUniversity of LleidaLleidaSpain
  2. 2.Artificial Intelligence Research Institute (IIIA-CSIC), Campus UABBarcelonaSpain

Personalised recommendations