Probabilistic Constraint Handling Rules

Abstract Classical Constraint Handling Rules (CHR) provide a powerful tool for specifying and implementing constraint solvers and programs. The rules of CHR rewrite constraints (non-deterministically) into simpler ones until they are solved. In this paper we introduce an extension of Constraint Handling Rules (CHR), namely Probabilistic CHRs (PCHR). These allow the probabilistic "weighting" of rules, specifying the probability of their application. In this way we are able to formalise various randomised algorithms such as for example Simulated Annealing. The implementation is based on source-to-source transformation (STS). Using a recently developed prototype for STS for CHR, we could implement probabilistic CHR in a concise way with a few lines of code in less than one hour

CHR(PRISM)-based Probabilistic Logic Learning

PRISM is an extension of Prolog with probabilistic predicates and built-in support for expectation-maximization learning. Constraint Handling Rules (CHR) is a high-level programming language based on multi-headed multiset rewrite rules. In this paper, we introduce a new probabilistic logic formalism, called CHRiSM, based on a combination of CHR and PRISM. It can be used for high-level rapid prototyping of complex statistical models by means of "chance rules". The underlying PRISM system can then be used for several probabilistic inference tasks, including probability computation and parameter learning. We define the CHRiSM language in terms of syntax and operational semantics, and illustrate it with examples. We define the notion of ambiguous programs and define a distribution semantics for unambiguous programs. Next, we describe an implementation of CHRiSM, based on CHR(PRISM). We discuss the relation between CHRiSM and other probabilistic logic programming languages, in particular PCHR. Finally we identify potential application domains

Implementing probabilistic abductive logic programming with Constraint Handling Rules

Abstract. A class of Probabilistic Abductive Logic Programs (PALPs) is introduced and an implementation is developed in CHR for solving abductive problems, providing minimal explanations with their probabilities. Both all-explanations and most-probable-explanations versions are given. Compared with other probabilistic versions of abductive logic programming, the approach is characterized by higher generality and a flexible and adaptable architecture which incorporates integrity constraints and interaction with external constraint solvers. A PALP is transformed in a systematic way into a CHR program which serves as a query interpreter, and the resulting CHR code describes in a highly concise way, the strategies applied in the search for explanations