arXiv AI By Yuliya Lierler

EZSMT Version 3, Matured

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arXiv:2607. 13344v1 Announce Type: new Abstract: Constraint Answer Set Programming (CASP) is a hybrid reasoning paradigm that combines Answer Set Programming (ASP) with Constraint Processing and Satisfiability Modulo Theories (SMT), enabling powerful declarative encodings of complex combinatorial search problems.

Summary generated by The Flow from the publisher's feed. The full article lives at arXiv AI.

arXiv AI
Jul 24

Bound-Founded Semantics for Answer Set Programming with Difference Constraints: Preliminary Report

arXiv:2607. 21201v1 Announce Type: new Abstract: While the integration of linear constraints has significantly expanded the reach of Answer Set Programming (ASP), existing hybrid solvers often rely on disparate semantic underpinnings that lack a unified logical foundation.

By Pedro Cabalar (University of A Corunna, Spain), Jorge Fandinno (University of Nebraska at Omaha, USA), Nicolas R\"uhling (University of Potsdam, Germany), Torsten Schaub (University of Potsdam, Germany,Potassco Solutions, Germany), Sebastian Schellhorn (University of Potsdam, Germany), Philipp Wanko (University of Potsdam, Germany,Potassco Solutions, Germany)
arXiv AI
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Streamliners for Answer Set Programming

arXiv:2604. 19251v2 Announce Type: replace-cross Abstract: Streamliner constraints reduce the search space of combinatorial problems by ruling out portions of the solution space.

By Florentina Voboril (TU Wien), Martin Gebser (University of Klagenfurt), Stefan Szeider (TU Wien), Alice Tarzariol (University of Klagenfurt)