Inference in Probabilistic Logic Programs using Lifted Explanations

TL;DR

This paper introduces lifted explanation graphs for probabilistic logic programming, enabling more efficient inference by summarizing explanations into compact structures, reducing computational complexity in large-scale problems.

Contribution

The paper defines lifted explanation graphs and operations, generalizing existing explanation graph methods for probabilistic logic programs, and demonstrates reduced asymptotic complexity.

Findings

Lifted explanation graphs are more compact than traditional explanation graphs.

The method reduces the asymptotic complexity of inference in large probabilistic logic programs.

Examples show significant efficiency improvements with the proposed technique.

Abstract

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 and computing probabilities over this graph. When evaluating queries over probabilistic logic programs with a large number of instances of random variables, traditional methods treat each instance separately. For many programs and queries, we observe that explanations can be summarized into substantially more compact structures, which we call lifted explanation graphs. In this paper, we define lifted explanation graphs and operations over them. 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.