Towards Computing Revised Models for FO Theories

  • Johan Wittocx
  • Broes De Cat
  • Marc Denecker
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6547)


In many real-life computational search problems, one is not only interested in finding a solution, but also in maintaining a solution under varying circumstances. For example, in the area of network configuration, an initial configuration of a computer network needs to be obtained, but also a new configuration when one of the machines in the network breaks down. Currently, most such revision problems are solved manually, or with highly specialized software.

A recent declarative approach to solve (hard) computational search problems involving a lot of domain knowledge, is by finite model generation. Here, the domain knowledge is specified as a logic theory T, and models of T correspond to solutions of the problem. In this paper, we extend this approach to solve revision problems. In particular, our method allows to use the same theory to describe the search problem and the revision problem, and applies techniques from current model generators to find revised solutions.


Logic Program Predicate Symbol Domain Element Mail Server Propositional Theory 
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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  1. 1.
    Brain, M., Watson, R., De Vos, M.: An interactive approach to answer set programming. In: Answer Set Programming. CEUR Workshop Proceedings, vol. 142 (2005),
  2. 2.
    Cadoli, M., Ianni, G., Palopoli, L., Schaerf, A., Vasile, D.: NP-SPEC: an executable specification language for solving all problems in NP. Computer Languages 26(2-4), 165–195 (2000)CrossRefzbMATHGoogle Scholar
  3. 3.
    De Cat, B.: Ontwikkeling van algoritmes voor modelrevisie, met toepassingen in treinplanning en netwerkconfiguratie. Master’s thesis, Katholieke Universiteit Leuven, Leuven, Belgium (June 2009) (in Dutch)Google Scholar
  4. 4.
    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
  5. 5.
    Enderton, H.B.: A Mathematical Introduction To Logic. Academic Press, London (1972)zbMATHGoogle Scholar
  6. 6.
    Fox, M., Gerevini, A., Long, D., Serina, I.: Plan stability: Replanning versus plan repair. In: Long, D., Smith, S.F., Borrajo, D., McCluskey, L. (eds.) ICAPS, pp. 212–221. AAAI, Menlo Park (2006)Google Scholar
  7. 7.
    Li, C.M., Manyà, F., Planes, J.: New inference rules for max-sat. J. Artif. Intell. Res. (JAIR) 30, 321–359 (2007)zbMATHGoogle Scholar
  8. 8.
    Marek, V.W., Truszczyński, M.: Stable models and an alternative logic programming paradigm. In: Apt, K.R., Marek, V.W., Truszczyński, M., Warren, D.S. (eds.) The Logic Programming Paradigm: a 25-Year Perspective, pp. 375–398. Springer, Heidelberg (1999)CrossRefGoogle Scholar
  9. 9.
    Mariën, M., Wittocx, J., Denecker, M., Bruynooghe, M.: SAT(ID): Satisfiability of Propositional Logic Extended with Inductive Definitions. In: Kleine Büning, H., Zhao, X. (eds.) SAT 2008. LNCS, vol. 4996, pp. 211–224. Springer, Heidelberg (2008)CrossRefGoogle Scholar
  10. 10.
    Mitchell, D.G.: A SAT solver primer. Bulletin of the European Association for Theoretical Computer Science 85, 112–132 (2005)zbMATHGoogle Scholar
  11. 11.
    Mitchell, D.G., Ternovska, E., Hach, F., Mohebali, R.: Model expansion as a framework for modelling and solving search problems. Technical Report TR 2006-24, Simon Fraser University, Canada (2006)Google Scholar
  12. 12.
    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)CrossRefzbMATHGoogle Scholar
  13. 13.
    Patterson, M., Liu, Y., Ternovska, E., Gupta, A.: Grounding for model expansion in k-guarded formulas with inductive definitions. In: Veloso, M.M. (ed.) IJCAI, pp. 161–166 (2007)Google Scholar
  14. 14.
    Perri, S., Scarcello, F., Catalano, G., Leone, N.: Enhancing DLV instantiator by backjumping techniques. Annals of Mathematics and Artificial Intelligence 51(2-4), 195–228 (2007)CrossRefzbMATHGoogle Scholar
  15. 15.
    Pipatsrisawat, K., Darwiche, A.: Clone: Solving Weighted Max-SAT in a Reduced Search Space. In: Orgun, M.A., Thornton, J. (eds.) AI 2007. LNCS (LNAI), vol. 4830, pp. 223–233. Springer, Heidelberg (2007)CrossRefGoogle Scholar
  16. 16.
    Selman, B., Kautz, H., Cohen, B.: Local search strategies for satisfiability testing. In: DIMACS Series in Discrete Mathematics and Theoretical Computer Science, pp. 521–532 (1995)Google Scholar
  17. 17.
    Wittocx, J., Mariën, M., Denecker, M.: GidL: A grounder for FO + . In: Pagnucco, M., Thielscher, M. (eds.) NMR, pp. 189–198. University of New South Wales (2008)Google Scholar
  18. 18.
    Wittocx, J., Mariën, M., Denecker, M.: The idp system: a model expansion system for an extension of classical logic. In: Denecker, M. (ed.) LaSh, pp. 153–165 (2008)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2011

Authors and Affiliations

  • Johan Wittocx
    • 1
  • Broes De Cat
    • 1
  • Marc Denecker
    • 1
  1. 1.Department of Computer ScienceK.U. LeuvenBelgium

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