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Lazy Explanation-Based Approximation for Probabilistic Logic Programming

Year: 2015

Type: conference-paper

Abstract: We introduce a lazy approach to the explanation-based approximation of probabilistic logic programs. It uses only the most significant part of the program when searching for explanations. The result is a fast and anytime approximate inference algorithm which returns hard lower and upper bounds on the exact probability. We experimentally show that this method outperforms state-of-the-art approximate inference.

Source: Lirias

DOI: 10.48550/arxiv.1507.02873

Authors: Renkens, Joris, Kimmig, Angelika, De Raedt, Luc

Language: English

Topic: Bayesian Modeling and Causal Inference

Subfield: Artificial Intelligence

Field: Computer Science

Domain: Physical Sciences

Open Access status: green

OpenAlex ID: W834486978

FWCI: 0.186

Cites: 9

Cited by: 1

Related to: 10