An efficient propositional system for Abductive Logic Programming

Abstract Abductive logic programming (ALP) extends logic programming with hypothetical reasoning by means of abducibles, an extension able to handle interesting problems, such as diagnosis, planning, and verification with formal methods. Implementations of this extension have been using Prolog meta-interpreters and Prolog programs with Constraint Handling Rules (). While the latter adds a clean and efficient interface to the host system, it still suffers in performance for large programs. Here, the concern is to obtain a more performant implementation of the system following a compiled approach. This paper, as a first step in this long term goal, sets out a propositional ALP system following , eliminating the need for and achieving better performance.

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Publication Details

Journal
Artificial Intelligence Review
Published
2024-10-17
DOI
https://doi.org/10.1007/s10462-024-10928-7
Primary Topic
Logic, Reasoning, and Knowledge
Type
article
Field-Weighted Citation Impact
0.00

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article

An efficient propositional system for Abductive Logic Programming

Pascual Julián-Iranzo, Fernando Sáenz-Pérez, Marco Gavanelli
Artificial Intelligence Review
Logic, Reasoning, and Knowledge
article

An efficient propositional system for Abductive Logic Programming

Pascual Julián-Iranzo, Fernando Sáenz-Pérez, Marco Gavanelli
article en

Abstract

Abstract Abductive logic programming (ALP) extends logic programming with hypothetical reasoning by means of abducibles, an extension able to handle interesting problems, such as diagnosis, planning, and verification with formal methods. Implementations of this extension have been using Prolog meta-interpreters and Prolog programs with Constraint Handling Rules (). While the latter adds a clean and efficient interface to the host system, it still suffers in performance for large programs. Here, the concern is to obtain a more performant implementation of the system following a compiled approach. This paper, as a first step in this long term goal, sets out a propositional ALP system following , eliminating the need for and achieving better performance.

Artificial Intelligence ReviewVol. 57(12)
Universidad Complutense de Madrid (ES), University of Ferrara (IT), University of Castilla-La Mancha (ES), Software (Spain) (ES)
Comunidad de Madrid, European Commission, Ministerio de Ciencia e Innovación, Università degli Studi di Ferrara, Gruppo Nazionale per il Calcolo Scientifico, Agencia Estatal de Investigación
Openalex Percentile: Top 84%
Logic, Reasoning, and Knowledge
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