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Computing Dialectical Trees Efficiently in Possibilistic Defeasible Logic Programming

  • Carlos I. Chesñevar
  • Guillermo R. Simari
  • Lluis Godo
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3662)

Abstract

Possibilistic Defeasible Logic Programming (P-DeLP) is a logic programming language which combines features from argumentation theory and logic programming, incorporating as well the treatment of possibilistic uncertainty and fuzzy knowledge at object-language level. Solving a P-DeLP query Q accounts for performing an exhaustive analysis of arguments and defeaters for Q, resulting in a so-called dialectical tree, usually computed in a depth-first fashion. Computing dialectical trees efficiently in P-DeLP is an important issue, as some dialectical trees may be computationally more expensive than others which lead to equivalent results. In this paper we explore different aspects concerning how to speed up dialectical inference in P-DeLP. We introduce definitions which allow to characterize dialectical trees constructively rather than declaratively, identifying relevant features for pruning the associated search space. The resulting approach can be easily generalized to be applied in other argumentation frameworks based in logic programming.

Keywords

Defeasible Argumentation Logic Programming Dialectical Reasoning 

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References

  1. 1.
    Chesñevar, C.I., Simari, G., Alsinet, T., Godo, L.: A Logic Programming Framework for Possibilistic Argumentation with Vague Knowledge. In: Proc. of the Intl. Conf. in Uncertainty in Art. Intelligence (UAI 2004), Banff, Canada, pp. 76–84 (2004)Google Scholar
  2. 2.
    Alsinet, T., Godo, L.: A complete calculus for possibilistic logic programming with fuzzy propositional variables. In: Proc. of the UAI 2000 Conference, pp. 1–10 (2000)Google Scholar
  3. 3.
    Dubois, D., Lang, J., Prade, H.: Possibilistic logic. In: Gabbay, D., Hogger, C., Robinson, J. (eds.) Handbook of Logic in Art. Int. and Logic Prog (Nonmonotonic Reasoning and Uncertain Reasoning), pp. 439–513. Oxford Univ. Press, Oxford (1994)Google Scholar
  4. 4.
    Kakas, A., Toni, F.: Computing argumentation in logic programming. Journal of Logic Programming 9, 515–562 (1999)zbMATHMathSciNetGoogle Scholar
  5. 5.
    Chesñevar, C.I., Maguitman, A., Loui, R.: Logical Models of Argument. ACM Computing Surveys 32, 337–383 (2000)CrossRefGoogle Scholar
  6. 6.
    Prakken, H., Vreeswijk, G.: Logical Systems for Defeasible Argumentation. In: Gabbay, D., Guenther, F. (eds.) Handbook of Philosophical Logic, pp. 219–318. Kluwer Academic Publishers, Dordrecht (2002)Google Scholar
  7. 7.
    Besnard, P., Hunter, A.: A logic-based theory of deductive arguments. Artificial Intelligence 1(2), 203–235 (2001)CrossRefMathSciNetGoogle Scholar
  8. 8.
    Prakken, H., Sartor, G.: Argument-based extended logic programming with defeasible priorities. Journal of Applied Non-classical Logics 7, 25–75 (1997)zbMATHMathSciNetGoogle Scholar
  9. 9.
    García, A., Simari, G.: Defeasible Logic Programming: An Argumentative Approach. Theory and Practice of Logic Programming 4, 95–138 (2004)zbMATHCrossRefMathSciNetGoogle Scholar
  10. 10.
    Gómez, S., Chesñevar, C.: A Hybrid Approach to Pattern Classification Using Neural Networks and Defeasible Argumentation. In: Proc. of 17th Intl. FLAIRS Conf. Miami, Florida, USA, pp. 393–398. AAAI Press, Menlo Park (2004)Google Scholar
  11. 11.
    Chesñevar, C., Maguitman, A.: An Argumentative Approach to Assessing Natural Language Usage based on the Web Corpus. In: Proc. of the ECAI 2004 Conference, Valencia, Spain, pp. 581–585 (2004)Google Scholar
  12. 12.
    Prakken, H.: Relating protocols for dynamic dispute with logics for defeasible argumentation. Synthese (special issue on New Perspectives in Dialogical Logic) 127, 187–219 (2001)zbMATHMathSciNetGoogle Scholar
  13. 13.
    Brewka, G.: Dynamic argument systems: A formal model of argumentation processes based on situation calculus. J. of Logic and Computation 11, 257–282 (2001)zbMATHCrossRefMathSciNetGoogle Scholar
  14. 14.
    Hunter, A.: Towards Higher Impact Argumentation. In: Proc. of the 19th American National Conf. on Artificial Intelligence (AAAI 2004), pp. 275–280. MIT Press, Cambridge (2004)Google Scholar
  15. 15.
    Chesñevar, C., Simari, G., Godo, L., Alsinet, T.: Argument-based expansion operators in possibilistic defeasible logic programming: Characterization and logical properties. In: Godo, L. (ed.) ECSQARU 2005. LNCS (LNAI), vol. 3571, pp. 353–365. Springer, Heidelberg (2005)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2005

Authors and Affiliations

  • Carlos I. Chesñevar
    • 1
  • Guillermo R. Simari
    • 2
  • Lluis Godo
    • 3
  1. 1.Departament of Computer ScienceUniversitat de LleidaLleidaSpain
  2. 2.Department of Computer Science and EngineeringUniversidad Nacional del Sur, Alem 1253Bahía BlancaArgentina
  3. 3.Artificial Intelligence Research Institute (IIIA-CSIC)Bellaterra, BarcelonaSpain

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