Probabilistic space partitioning in constraint logic programming

Abstract

We present a language for integrating probabilistic reasoning and logic programming. The key idea is to use constraints based techniques such as the constraints store and finite domain variables. First we show how these techniques can be used to integrate a number of probabilistic inference algorithms with logic programming. We then proceed to detail a language which effects conditioning by probabilistically partitioning the constraint store. We elucidate the kinds of reasoning effected by the introduced language by means of two well known probabilistic problems: the three prisoners and Monty Hall. In particular we show how the syntax of the language can be used to avoid the pitfalls normally associated with the two problems. An elimination algorithm for computing the probability of a query in a given store is presented. © Springer-Verlag Berlin Heidelberg 2004.