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Preferences in Answer Set Programming

  • Gerhard Brewka
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4177)

Abstract

Preferences play a major role in many AI applications. We give a brief overview of methods for adding qualitative preferences to answer set programming, a promising declarative programming paradigm. We show how these methods can be used in a variety of different applications such as configuration, abduction, diagnosis, inconsistency handling and game theory.

Keywords

Logic Program Logic Programming Satisfaction Degree Paraconsistent Logic Nonmonotonic 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.

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References

  1. 1.
    Baral, C.: Knowledge representation, reasoning and declarative problem solving. Cambridge University Press, Cambridge (2003)MATHCrossRefGoogle Scholar
  2. 2.
    Balduccini, M., Gelfond, M.: Logic programs with consistency-restoring rules. In: Doherty, P., McCarthy, J., Williams, M.-A. (eds.) International Symposium on Logical Formalization of Commonsense Reasoning, AAAI 2003 Spring Symposium Series, pp. 9–18 (2003)Google Scholar
  3. 3.
    Brewka, G.: Logic programming with ordered disjunction. In: Proc. AAAI-02, pp. 100–105. Morgan Kaufmann, San Francisco (2002)Google Scholar
  4. 4.
    Brewka, G.: Answer sets and qualitative optimization. Logic Journal of the IGPL (2006) (to appear)Google Scholar
  5. 5.
    Brewka, G., Eiter, T.: Preferred answer sets for extended logic programs. Artificial Intelligence 109, 297–356 (1999)MATHCrossRefMathSciNetGoogle Scholar
  6. 6.
    Brewka, G., Niemelä, I., Truszczynski, M.: Answer set optimization. In: Proc. IJCAI 2003, Acapulco, pp. 867–872 (2003)Google Scholar
  7. 7.
    Brewka, G., Niemelä, I., Syrjänen, T.: Logic programs with ordered disjunction. Computational Intelligence 20(2), 335–357 (2004)CrossRefMathSciNetGoogle Scholar
  8. 8.
    Console, L., Dupre, D.T., Torasso, P.: On the relation between abduction and deduction. Journal of Logic and Computation 1(5), 661–690 (1991)MATHCrossRefMathSciNetGoogle Scholar
  9. 9.
    Damasio, C.V., Pereira, L.M.: A survey on paraconsistent semantics for extended logic programas. In: Gabbay, D.M., Smets, Ph. (eds.) Handbook of Defeasible Reasoning and Uncertainty Management Systems, vol. 2, pp. 241–320. Kluwer Academic Publishers, Dordrecht (1998)Google Scholar
  10. 10.
    Eiter, T., Leone, N., Mateis, C., Pfeifer, G., Scarcello, F.: The KR system dlv: progress report, comparisons and benchmarks. In: Proc. Principles of Knowledge Representation and Reasoning, KR 1998, pp. 86–97. Morgan Kaufmann, San Francisco (1998)Google Scholar
  11. 11.
    Eiter, T., Faber, W., Leone, N., Pfeifer, G.: The diagnosis frontend of the dlv system. AI Communications 12(1-2), 99–111 (1999)MathSciNetGoogle Scholar
  12. 12.
    Foo, N., Meyer, T., Brewka, G.: LPOD answer sets and Nash equilibria. In: Maher, M.J. (ed.) ASIAN 2004. LNCS, vol. 3321, pp. 343–351. Springer, Heidelberg (2004)Google Scholar
  13. 13.
    Gelfond, M., Lifschitz, V.: Classical negation in logic programs and disjunctive databases. New Generation Computing 9, 365–385 (1991)CrossRefGoogle Scholar
  14. 14.
    Konolige, K.: Abduction versus closure in causal theories. Artificial Intelligence 53, 255–272 (1992)CrossRefMathSciNetMATHGoogle Scholar
  15. 15.
    Lifschitz, V.: Answer set programming and plan generation. Artificial Intelligence 138(1-2), 39–54 (2002)MATHCrossRefMathSciNetGoogle Scholar
  16. 16.
    Marek, V., Truszczynski, M.: Stable models and an alternative logic programming paradigm. In: The Logic Programming Paradigm: a 25-Year Perspective, pp. 375–398. Springer Verlag, Heidelberg (1999)Google Scholar
  17. 17.
    Niemelä, I.: Logic programs with stable model semantics as a constraint programming paradigm. Annals of Mathematics and Artificial Intelligence 25(3,4), 241–273 (1999)MATHCrossRefMathSciNetGoogle Scholar
  18. 18.
    Niemelä, I., Simons, P.: Smodels - an implementation of the stable model and well-founded semantics for normal logic programs. In: Fuhrbach, U., Dix, J., Nerode, A. (eds.) LPNMR 1997. LNCS, vol. 1265, pp. 420–429. Springer, Heidelberg (1997)Google Scholar
  19. 19.
    Peng, Y., Reggia, J.: Abductive inference models for diagnostic problem solving. Symbolic Computation - Artificial Intelligence. Springer, Heidelberg (1990)MATHGoogle Scholar
  20. 20.
    Pereira, L.M., Alferes, J.J., Aparicio, J.: Contradiction removal within well founded semantics. In: Nerode, A., Marek, W., Subrahmanian, V.S. (eds.) Logic Programming and Nonmonotonic Reasoning, pp. 105–119. MIT Press, Cambridge (1991)Google Scholar
  21. 21.
    Poole, D.: An architecture for default and abductive reasoning. Computational Intelligence 5(1), 97–110 (1989)CrossRefMathSciNetGoogle Scholar
  22. 22.
    Sakama, C., Inoue, K.: Prioritized logic programming and its application to commonsense reasoning. Artificial Intelligence 123(1-2), 185–222 (2000)MATHCrossRefMathSciNetGoogle Scholar
  23. 23.
    Simons, P., Niemelä, I., Soininen, T.: Extending and implementing the stable model semantics. Artificial Intelligence 138(1-2), 181–234 (2002)MATHCrossRefMathSciNetGoogle Scholar
  24. 24.
    Soininen, T.: An approach to knowledge representation and reasoning for product configuration tasks, PhD thesis, Helsinki University of Technology, Finland (2000)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Gerhard Brewka
    • 1
  1. 1.Dept. of Computer ScienceUniversity of LeipzigLeipzigGermany

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