Lifted discriminative learning of probabilistic logic programs
نویسندگان
چکیده
منابع مشابه
Lifted Discriminative Learning of Probabilistic Logic Programs
Probabilistic logic programming (PLP) provides a powerful tool for reasoning with uncertain relational models. However, learning probabilistic logic programs is expensive due to the high cost of inference. Among the proposals to overcome this problem, one of the most promising is lifted inference. In this paper we consider PLP models that are amenable to lifted inference and present an algorith...
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First-order model counting emerged recently as a novel reasoning task, at the core of efficient algorithms for probabilistic logics such as MLNs. For certain subsets of first-order logic, lifted model counters were shown to run in time polynomial in the number of objects in the domain of discourse, where propositional model counters require exponential time. However, these guarantees apply only...
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In this paper, we consider the problem of lifted inference in the context of Prism-like probabilistic logic programming languages. Traditional inference in such languages involves the construction of an explanation graph for the query that treats each instance of a random variable separately. For many programs and queries, we observe that explanations can be summarized into substantially more c...
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ژورنال
عنوان ژورنال: Machine Learning
سال: 2018
ISSN: 0885-6125,1573-0565
DOI: 10.1007/s10994-018-5750-0