2026-07-22T10:43:44Z https://drops.dagstuhl.de/oai

oai:drops-oai.dagstuhl.de:6739 2024-03-06T10:29:56Z ddc:004 open_access

Answer Set Solving with Generalized Learned Constraints Gebser, Martin Kaminski, Roland Kaufmann, Benjamin Lühne, Patrick Romero, Javier Schaub, Torsten Answer Set Programming Conflict Learning Constraint Generalization Generalized Constraint Feedback Conflict learning plays a key role in modern Boolean constraint solving. Advanced in satisfiability testing, it has meanwhile become a base technology in many neighboring fields, among them answer set programming (ASP). However, learned constraints are only valid for a currently solved problem instance and do not carry over to similar instances. We address this issue in ASP and introduce a framework featuring an integrated feedback loop that allows for reusing conflict constraints. The idea is to extract (propositional) conflict constraints, generalize and validate them, and reuse them as integrity constraints. Although we explore our approach in the context of dynamic applications based on transition systems, it is driven by the ultimate objective of overcoming the issue that learned knowledge is bound to specific problem instances. We implemented this workflow in two systems, namely, a variant of the ASP solver clasp that extracts integrity constraints along with a downstream system for generalizing and validating them. Schloss Dagstuhl – Leibniz-Zentrum für Informatik Martin Gebser and Roland Kaminski and Benjamin Kaufmann and Patrick Lühne and Javier Romero and Torsten Schaub 2016 Is Part Of OASIcs, Volume 52, Technical Communications of the 32nd International Conference on Logic Programming (ICLP 2016) InProceedings Text doc-type:ResearchArticle publishedVersion application/pdf doi:10.4230/OASIcs.ICLP.2016.9 urn:nbn:de:0030-drops-67393 https://drops.dagstuhl.de/entities/document/10.4230/OASIcs.ICLP.2016.9 eng https://creativecommons.org/licenses/by/3.0/legalcode