Constraint-Based Inference in Probabilistic Logic Programs
نویسندگان
چکیده
منابع مشابه
Constraint-Based Inference in Probabilistic Logic Programs
A wide variety of models that combine logical and statistical knowledge can be expressed succinctly in the Probabilistic Logic Programming (PLP) paradigm. Specifically, models in standard statistical formalisms such as probabilistic graphical models (PGMs) (e.g. Bayesian Networks), can be easily encoded as PLP programs. For instance, Fig. 1(a) shows a program in PRISM, a pioneering PLP language...
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Action-probabilistic logic programs (ap-programs) are a class of probabilistic logic programs that have been extensively used during the last few years for modeling behaviors of entities. Rules in ap-programs have the form “If the environment in which entity E operates satisfies certain conditions, then the probability that E will take some action A is between L and U”. Given an ap-program, we ...
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Probabilistic logics combine the expressive power of logic with the ability to reason with uncertainty. Several probabilistic logic languages have been proposed in the past, each of them with their own features. In this paper, we propose a new probabilistic constraint logic programming language, which combines constraint logic programming with probabilistic reasoning. The language supports mode...
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ژورنال
عنوان ژورنال: Theory and Practice of Logic Programming
سال: 2018
ISSN: 1471-0684,1475-3081
DOI: 10.1017/s1471068418000273