Inference in Probabilistic Logic Programs Using Lifted Explanations
نویسندگان
چکیده
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 compact structures introduced in this paper, called “lifted explanation graphs”. In contrast to existing lifted inference techniques, our method for constructing lifted explanations naturally generalizes existing methods for constructing explanation graphs. To compute probability of query answers, we solve recurrences generated from the lifted graphs. We show examples where the use of our technique reduces the asymptotic complexity of inference. 1998 ACM Subject Classification D.1.6 Logic Programming, D.3.3 Language Constructs and Features – Constraints,F.3.2 Semantics of Programming Languages – Operational semantics, I.2.3 Deduction and Theorem Proving – Logic programming, Resolution, Uncertain, “fuzzy”, and probabilistic reasoning
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...
متن کاملLifted Inference for Probabilistic Programming
A probabilistic program often gives rise to a complicated underlying probabilistic model. Performing inference in such a model is challenging. One solution to this problem is lifted inference which improves tractability by exploiting symmetries in the underlying model. Our group is pursuing a lifted approach to inference for probabilistic logic programs.
متن کاملLifted Variable Elimination for Probabilistic Logic Programming
Lifted inference has been proposed for various probabilistic logical frameworks in order to compute the probability of queries in a time that depends on the size of the domains of the random variables rather than the number of instances. Even if various authors have underlined its importance for probabilistic logic programming (PLP), lifted inference has been applied up to now only to relationa...
متن کاملLifted Inference for Probabilistic Logic Programs
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...
متن کاملConstraint Processing in Lifted Probabilistic Inference
First-order probabilistic models combine representational power of first-order logic with graphical models. There is an ongoing effort to design lifted inference algorithms for first-order probabilistic models. We analyze lifted inference from the perspective of constraint processing and, through this viewpoint, we analyze and compare existing approaches and expose their advantages and limitati...
متن کامل