Weighted Answer Sets and Applications in Intelligence Analysis

  • Davy Van Nieuwenborgh
  • Stijn Heymans
  • Dirk Vermeir
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3452)

Abstract

The extended answer set semantics for simple logic programs, i.e. programs with only classical negation, allows for the defeat of rules to resolve contradictions. In addition, a partial order relation on the program’s rules can be used to deduce a preference relation on its extended answer sets. In this paper, we propose a “quantitative” preference relation that associates a weight with each rule in a program. Intuitively, these weights define the “cost” of defeating a rule. An extended answer set is preferred if it minimizes the sum of the weights of its defeated rules. We characterize the expressiveness of the resulting semantics and show that it can capture negation as failure. Moreover the semantics can be conveniently extended to sequences of weight preferences, without increasing the expressiveness. We illustrate an application of the approach by showing how it can elegantly express subgraph isomorphic approximation problems, a concept often used in intelligence analysis to find specific regions of interest in a large graph of observed activities.

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Akutsu, T., Halldórsson, M.M.: On the approximation of largest common subtrees and largest common point sets. Theoretical Comp. Science 233(1-2), 33–50 (2000)MATHCrossRefGoogle Scholar
  2. 2.
    Baral, C.: Knowledge Representation, Reasoning and Declarative Problem Solving. Cambridge Press (2003)Google Scholar
  3. 3.
    Buccafurri, F., Leone, N., Rullo, P.: Strong and weak constraints in disjunctive datalog. In: Fuhrbach, U., Dix, J., Nerode, A. (eds.) LPNMR 1997. LNCS, vol. 1265, pp. 2–17. Springer, Heidelberg (1997)CrossRefGoogle Scholar
  4. 4.
    Coffman, T., Greenblatt, S., Marcus, S.: Graph-based technologies for intelligence analysis. Communications of the ACM 47(3), 45–47 (2004)CrossRefGoogle Scholar
  5. 5.
    Console, L., Torasso, P.: A spectrum of logical definitions of model-based diagnosis. Computational Intelligence 7(3), 133–141 (1991)CrossRefGoogle Scholar
  6. 6.
    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
  7. 7.
    Eiter, T., Faber, W., Leone, N., Pfeifer, G.: Declarative problemsolving using the dlv system. In: Logic-Based Artificial Intelligence, pp. 79–103 (2000)Google Scholar
  8. 8.
    Gelfond, M., Lifschitz, V.: The stable model semantics for logic programming. In: Proceedings of the Fifth International Conference and Symposium on Logic Programming, pp. 1070–1080. MIT Press, Cambridge (1988)Google Scholar
  9. 9.
    Heuer, R.J.: Psychology of intelligence analysis. Center for the Study of Intelligence, Central Intelligence Agency (2001)Google Scholar
  10. 10.
    Lifschitz, V.: Answer set programming and plan generation. Journal of Artificial Intelligence 138(1-2), 39–54 (2002)MathSciNetMATHCrossRefGoogle Scholar
  11. 11.
    Syrjänen, T., Niemelä, I.: The smodels system. In: Eiter, T., Faber, W., Truszczyński, M. (eds.) LPNMR 2001. LNCS (LNAI), vol. 2173, pp. 434–438. Springer, Heidelberg (2001)Google Scholar
  12. 12.
    Ullman, J.R.: An algorithm for subgraph isomorphism. J. of the ACM 23(1), 31–42 (1976)MATHCrossRefGoogle Scholar
  13. 13.
    Van Nieuwenborgh, D., Heymans, S., Vermeir, D.: On programs with linearly ordered multiple preferences. In: Demoen, B., Lifschitz, V. (eds.) ICLP 2004. LNCS, vol. 3132, pp. 180–194. Springer, Heidelberg (2004)CrossRefGoogle Scholar
  14. 14.
    Van Nieuwenborgh, D., Vermeir, D.: Preferred answer sets for ordered logic programs. In: Flesca, S., Greco, S., Leone, N., Ianni, G. (eds.) JELIA 2002. LNCS (LNAI), vol. 2424, pp. 432–443. Springer, Heidelberg (2002)CrossRefGoogle Scholar
  15. 15.
    Van Nieuwenborgh, D., Vermeir, D.: Ordered diagnosis. In: Y. Vardi, M., Voronkov, A. (eds.) LPAR 2003. LNCS, vol. 2850, pp. 244–258. Springer, Heidelberg (2003)CrossRefGoogle Scholar
  16. 16.
    De Vos, M., Vermeir, D.: Logic programming agents playing games. In: Research and Development in Intelligent Systems XIX (ES 2002). BCS Conference Series, pp. 323–336. Springer, Heidelberg (2002)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2005

Authors and Affiliations

  • Davy Van Nieuwenborgh
    • 1
  • Stijn Heymans
    • 1
  • Dirk Vermeir
    • 1
  1. 1.Dept. of Computer ScienceVrije Universiteit Brussel, VUBBrusselsBelgium

Personalised recommendations