Probabilistic reasoning in intelligent systems: networks of plausible inference
Probabilistic reasoning in intelligent systems: networks of plausible inference
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CL '00 Proceedings of the First International Conference on Computational Logic
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User-definable rule priorities for CHR
Proceedings of the 9th ACM SIGPLAN international conference on Principles and practice of declarative programming
A glimpse of symbolic-statistical modeling by PRISM
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ICLP '09 Proceedings of the 25th International Conference on Logic Programming
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LOPSTR'06 Proceedings of the 16th international conference on Logic-based program synthesis and transformation
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PADL'06 Proceedings of the 8th international conference on Practical Aspects of Declarative Languages
Probabilistic termination of CHRiSM programs
LOPSTR'11 Proceedings of the 21st international conference on Logic-Based Program Synthesis and Transformation
Using Generalized Annotated Programs to Solve Social Network Diffusion Optimization Problems
ACM Transactions on Computational Logic (TOCL)
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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.