Probabilistic Inductive Logic Programming
نویسنده
چکیده
The field of Probabilistic Logic Programming (PLP) has seen significant advances in the last 20 years, with many proposals for languages that combine probability with logic programming. Since the start, the problem of learning probabilistic logic programs has been the focus of much attention and a special issue of Theory and Practice of Logic Programming on Probability, Logic, and Learning has just appeared online. Learning PLP represents a whole subfield of Inductive Logic Programming (ILP). In Probabilistic ILP (PILP) two problems are considered: learning the parameters of a program given the structure (the rules) and learning the structure and the parameters at the same time. Usually structure learning systems use parameter learning as a subroutine. In this article we present the field of PILP and discuss the main results.
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