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Constraint Answer Set Programming: Integrational and Translational (or SMT-based) Approaches

Published online by Cambridge University Press:  02 November 2021

YULIYA LIERLER Open the ORCID record for YULIYA LIERLER [Opens in a new window]
YULIYA LIERLER*
Affiliation:
University of Nebraska Omaha, Omaha, NE 68182, USA (e-mail: ylierler@unomaha.edu)
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Abstract

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Constraint answer set programming or CASP, for short, is a hybrid approach in automated reasoning putting together the advances of distinct research areas such as answer set programming, constraint processing, and satisfiability modulo theories. CASP demonstrates promising results, including the development of a multitude of solvers: acsolver, clingcon, ezcsp, idp, inca, dingo, mingo, aspmt2smt, clingo[l,dl], and ezsmt. It opens new horizons for declarative programming applications such as solving complex train scheduling problems. Systems designed to find solutions to constraint answer set programs can be grouped according to their construction into, what we call, integrational or translational approaches. The focus of this paper is an overview of the key ingredients of the design of constraint answer set solvers drawing distinctions and parallels between integrational and translational approaches. The paper also provides a glimpse at the kind of programs its users develop by utilizing a CASP encoding of Traveling Salesman problem for illustration. In addition, we place the CASP technology on the map among its automated reasoning peers as well as discuss future possibilities for the development of CASP.

Keywords

answer set programmingconstraintssatisfiability modulo theories
Type
Survey Article
Information
Theory and Practice of Logic Programming , Volume 23 , Issue 1 , January 2023 , pp. 195 - 225
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Copyright
© The Author(s), 2021. Published by Cambridge University Press

Footnotes

*

I would like to acknowledge and cordially thank many of my collaborators with whom we had a chance to contribute to an exciting field of Constraint Answer Set Programming and many of my colleagues who have fostered my understanding of the subject matter: Marcello Balduccini, Broes De Cat, Marc Denecker, Martin Gebser, Michael Gelfond, Tomi Janhunen, Roland Kaminski, Martin Nyx Brain, Joohyung Lee, Ilkka Niemelä, Max Ostrowski, Torsten Schaub, Peter Schueller, Da Shen, Benjamin Susman, Cesare Tinelli, Miroslaw Truszczynski, Philipp Wanko, Yuanlin Zhang. Thank you for the years of an incredible journey. Also, I would like to thank the anonymous reviewers for their valuable feedback, which helped to bring the article to this form. This work was partially supported by the NSF 1707371 grant.

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