Bottom-Up Grounding in the Probabilistic Logic Programming System Fusemate

TL;DR

Fusemate introduces a bottom-up grounding approach for probabilistic logic programming, enhancing efficiency and flexibility in dynamic distribution creation, with query-guided pruning to control grounding size, outperforming existing systems in complex scenarios.

Contribution

The paper presents a novel bottom-up grounding method with query-guided relevance testing in Fusemate, improving control over grounding and performance in probabilistic logic programming.

Findings

Competitive or better performance compared to state-of-the-art systems.

Effective handling of high branching problems.

Demonstrated advantages in time-related probabilistic models.

Abstract

This paper introduces the Fusemate probabilistic logic programming system. Fusemate's inference engine comprises a grounding component and a variable elimination method for probabilistic inference. Fusemate differs from most other systems by grounding the program in a bottom-up way instead of the common top-down way. While bottom-up grounding is attractive for a number of reasons, e.g., for dynamically creating distributions of varying support sizes, it makes it harder to control the amount of ground clauses generated. We address this problem by interleaving grounding with a query-guided relevance test which prunes rules whose bodies are inconsistent with the query. We present our method in detail and demonstrate it with examples that involve "time", such as (hidden) Markov models. Our experiments demonstrate competitive or better performance compared to a state-of-the art probabilistic logic programming system, in particular for high branching problems.