Tabling Optimization for Contextual Abduction

Ridhwan Dewoprabowo
Ari Saptawijaya

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.

In Francesco Ricca, Alessandra Russo, Sergio Greco, Nicola Leone, Alexander Artikis, Gerhard Friedrich, Paul Fodor, Angelika Kimmig, Francesca Lisi, Marco Maratea, Alessandra Mileo and Fabrizio Riguzzi: Proceedings 36th International Conference on Logic Programming (Technical Communications) (ICLP 2020), UNICAL, Rende (CS), Italy, 18-24th September 2020, Electronic Proceedings in Theoretical Computer Science 325, pp. 137–150.
Published: 19th September 2020.

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