Towards Predicate Answer Set Programming via Coinductive Logic Programming

  • Richard Min
  • Ajay Bansal
  • Gopal Gupta
Part of the IFIP International Federation for Information Processing book series (IFIPAICT, volume 296)


Answer Set Programming (ASP) is a powerful paradigm based on logic programming for non-monotonic reasoning. Current ASP implementations are restricted to “grounded range-restricted function-free normal programs” and use an evaluation strategy that is “bottom-up” (i.e., not goal-driven). Recent introduction of coinductive Logic Programming (co-LP) has allowed the development of top-down goal evaluation strategies for ASP. In this paper we present this novel goal-directed, top-down approach to executing predicate answer set programs with co-LP. Our method eliminates the need for grounding, allows functions, and effectively handles a large class of predicate answer set programs including possibly infinite ones.


Logic Program Logic Programming Integrity Constraint Empty Clause Negative Context 
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.


  1. 1.
    Gelfond M, Lifschitz V (1988). The stable model semantics for logic programming. Proc. of International Logic Programming Conference and Symposium. 1070–1080.Google Scholar
  2. 2.
    Baral C (2003). Knowledge Representation, Reasoning and Declarative Problem Solving. Cambridge University Press.Google Scholar
  3. 3.
    Niemelä I, Simons, P (1996). Efficient implementation of the well-founded and stable model semantics. Proc. JICSLP. 289–303. The MIT Press.Google Scholar
  4. 4.
    Simons P, Niemelä I, Soininen, T (2002). Extending and implementing the stable model semantics. Artificial Intelligence 138(1–2):181–234.MathSciNetCrossRefMATHGoogle Scholar
  5. 5.
    Simons P, Syrjanen, T (2003). SMODELS (version 2.27) and LPARSE (version 1.0.13).
  6. 6.
    Simon L, Mallya A, Bansal A, Gupta G (2006). Coinductive Logic Programming. ICLP′06. Springer Verlag.Google Scholar
  7. 7.
    Gupta G, Bansal A, Min R et al (2007). Coinductive logic programming and its applications. Proc. ICLP′07. Springer Verlag.Google Scholar
  8. 8.
    Min R, Gupta G (2008). Negation in Coinductive Logic Programming. Technical Report. Department of Computer Science. University of Texas at Dallas. Scholar
  9. 9.
    Fages F (1994). Consistency of Clark's completion and existence of stable models. Journal of Methods of Logic in Computer Science 1:51–60.Google Scholar
  10. 10.
    Sato, T (1990). Completed logic programs and their consistency. J Logic Prog 9:33–44.MathSciNetCrossRefMATHGoogle Scholar
  11. 11.
    Kripke S (1985). Outline of a Theory of Truth. Journal of Philosophy 72:690–716.CrossRefMATHGoogle Scholar
  12. 12.
    Fitting, M (1985). A Kripke-Kleene semantics for logic programs. Journal of Logic Programming 2:295–312.MathSciNetCrossRefMATHGoogle Scholar
  13. 13.
    Simon L, Bansal A, Mallya A et al (2007). Co-Logic Programming. ICALP'07.Google Scholar
  14. 14.
    Colmerauer A (1978). Prolog and Infinite Trees. In: Clark KL, Tarnlund S-A (eds) Logic Programming. Prenum Press, New York.Google Scholar
  15. 15.
    Maher, MJ (1988). Complete Axiomatizations of the Algebras of Finite, Rational and Infinite Trees. Proc. 3rd Logic in Computer Science Conference. Edinburgh, UK.Google Scholar

Copyright information

© IFIP International Federation for Information Processing 2009

Authors and Affiliations

  • Richard Min
    • 1
  • Ajay Bansal
    • 1
  • Gopal Gupta
    • 1
  1. 1.Department of Computer ScienceThe University of Texas at DallasRichardsonU.S.A.

Personalised recommendations