ASPeRiX, a first-order forward chaining approach for answer set computing

Author:

LEFÈVRE CLAIRE,BÉATRIX CHRISTOPHER,STÉPHAN IGOR,GARCIA LAURENT

Abstract

AbstractThe natural way to use Answer Set Programming (ASP) to represent knowledge in Artificial Intelligence or to solve a combinatorial problem is to elaborate a first-order logic program with default negation. In a preliminary step, this program with variables is translated in an equivalent propositional one by a first tool: the grounder. Then, the propositional program is given to a second tool: the solver. This last one computes (if they exist) one or many answer sets (stable models) of the program, each answer set encoding one solution of the initial problem. Until today, almost all ASP systems apply this two steps computation. In this article, the projectASPeRiX. is presented as a first-order forward chaining approach for Answer Set Computing. This project was among the first to introduce an approach of answer set computing that escapes the preliminary phase of rule instantiation by integrating it in the search process. The methodology applies a forward chaining of first-order rules that are grounded on the fly by means of previously produced atoms. Theoretical foundations of the approach are presented, the main algorithms of the ASP solverASPeRiX. are detailed and some experiments and comparisons with existing systems are provided.

Publisher

Cambridge University Press (CUP)

Subject

Artificial Intelligence,Computational Theory and Mathematics,Hardware and Architecture,Theoretical Computer Science,Software

Cited by 21 articles. 订阅此论文施引文献 订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献

1. A Unifying Mathematical Definition of Particle Methods;IEEE Open Journal of the Computer Society;2023

2. ASP-based Multi-shot Reasoning via DLV2 with Incremental Grounding;Proceedings of the 24th International Symposium on Principles and Practice of Declarative Programming;2022-09-20

3. On the Foundations of Grounding in Answer Set Programming;Theory and Practice of Logic Programming;2022-07-25

4. On the Generalization of Learned Constraints for ASP Solving in Temporal Domains;Rules and Reasoning;2022

5. Abstraction for non-ground answer set programs;Artificial Intelligence;2021-11

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