Tabling Optimization for Contextual Abduction

TL;DR

This paper improves tabling techniques for contextual abduction in logic programming by proposing program transformations and optimizations that enhance scalability and reduce memory usage, validated through experiments.

Contribution

It introduces a new program transformation for integrity constraints and optimization strategies for selective tabling and representation simplification.

Findings

Enhanced scalability of tabling in contextual abduction.

Reduced memory consumption in tabled solutions.

Effective handling of real-world problems with improved performance.

Abstract

Tabling for contextual abduction in logic programming has been introduced as a means to store previously obtained abductive solutions in one context to be reused in another context. This paper identifies a number of issues in the existing implementations of tabling in contextual abduction and aims to mitigate the issues. We propose a new program transformation for integrity constraints to deal with their proper application for filtering solutions while also reducing the table memory usage. We further optimize the table memory usage by selectively picking predicates to table and by pragmatically simplifying the representation of the problem. The evaluation of our proposed approach, on both artificial and real world problems, shows that they improve the scalability of tabled abduction compared to previous implementations.