Advertisement

Extending the CLP Engine for Reasoning under Uncertainty

  • Nicos Angelopoulos
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 2871)

Abstract

We show how the amalgamation of Logic Programming with probabilistic reasoning enhances its capabilities for intelligent reasoning. Unlike current approaches we use concepts from Constraint Logic Programming in order to achieve this. In particular, we use the constraint store for storing probabilistic information and inference, and finite domains as sets of basic elements over which distributions can be defined. We describe a new language, Probabilistic finite domains and show how it can be used to code code two examples. First the Monty Hall problem is coded and the extensional means of simulating intelligence within our system are described. Second, we illustrate the benefits of the probabilistic information over the crisp finite domains in solving a simple encoding scheme. Aspects of a prototype implementation, a Prolog meta-interpreter, are discussed.

Keywords

Logic Program Logic Programming Probabilistic Variable Probabilistic Information Constraint Logic Programming 
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.

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Angelopoulos, N.: Probabilistic Finite Domains. PhD thesis, City U., London (2001)Google Scholar
  2. 2.
    Carlsson, M., Ottosson, G., Carlson, B.: An open-ended finite domain constraint solver. In: Progr. Languages: Implem., Logics, and Programs (1997)Google Scholar
  3. 3.
    Codognet, P., Diaz, D.: Compiling constraints in clp(FD). Journal of Logic Programming 27(3), 185–226 (1996)zbMATHCrossRefMathSciNetGoogle Scholar
  4. 4.
    Cussens, J.: Stochastic logic programs: Sampling, inference and applications. In: 16th Conference on Uncertainty in AI (UAI 2000), pp. 115–122 (2000)Google Scholar
  5. 5.
    Grinstead, C.M., Snell, J.L.: Introduction to Probability. AMS, Providence (1997)zbMATHGoogle Scholar
  6. 6.
    Kameya, Y., Sato, T.: Efficient learning with tabulation for parameterized logic programs. In: 1st Int. Conf. on Comput. Logic, pp. 269–294 (2000)Google Scholar
  7. 7.
    Konheim, A.G.: Cryptography, a primer. John Willey & Sons, West Sussex (1981)zbMATHGoogle Scholar
  8. 8.
    Lukasiewicz, T.: Probabilistic logic programming. In: 13th biennial European Conference on Artificial Intelligence, Brighton, UK, August 1999, pp. 388–392 (1999)Google Scholar
  9. 9.
    Ng, R., Subrahmanian, V.: Probabilistic logic programming. Information and Computation 101, 150–201 (1992)zbMATHCrossRefMathSciNetGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2003

Authors and Affiliations

  • Nicos Angelopoulos
    • 1
  1. 1.Department of Computer ScienceYork UniversityYork

Personalised recommendations