Extending the CLP Engine for Reasoning under Uncertainty
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.
KeywordsLogic Program Logic Programming Probabilistic Variable Probabilistic Information Constraint Logic Programming
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- 1.Angelopoulos, N.: Probabilistic Finite Domains. PhD thesis, City U., London (2001)Google Scholar
- 2.Carlsson, M., Ottosson, G., Carlson, B.: An open-ended finite domain constraint solver. In: Progr. Languages: Implem., Logics, and Programs (1997)Google Scholar
- 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
- 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
- 8.Lukasiewicz, T.: Probabilistic logic programming. In: 13th biennial European Conference on Artificial Intelligence, Brighton, UK, August 1999, pp. 388–392 (1999)Google Scholar