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. . The inference algorithms used in many of these systems to evaluate the probability of query answers, e.g. PRISM, Problog and PITA, are based on a common notion of explanation graphs. These graphs represent explanations, which are sets of probabilistic choices that are abduced during query evaluation. Explanation graphs are implemented differently by different systems; e.g. PRISM uses tables to represent them under mutual exclusion and independence assumptions on explanations; ProbLog and PITA represents them using Binary Decision Diagrams (BDDs). Inference based on explanation graphs does not scale well to logical/statistical models with large numbers of random processes and variables. In particular, in models containing families * ACM Subject Classification
Lifted explanation graphs REF are a generalization of ground explanation graphs, which treat instances of random processes in a symbolic fashion.
Inference in Probabilistic Logic Programs using Lifted Explanations
ArXiv
ArXiv
Computer Science