Document Type

Article

Publication Date

1-1-2015

Publication Title

Constraints

Volume

22

First Page

307

Last Page

337

DOI

https://doi-org.leo.lib.unomaha.edu/10.1007/s10601-016-9257-7

Abstract

Propositional satisfiability (or satisfiability) and answer set programming are two closely related subareas of Artificial Intelligence that are used to model and solve difficult combinatorial search problems. Satisfiability solvers and answer set solvers are the software systems that find satisfying interpretations and answer sets for given propositional formulas and logic programs, respectively. These systems are closely related in their common design patterns. In satisfiability, a propositional formula is used to encode problem specifications in a way that its satisfying interpretations correspond to the solutions of the problem. To find solutions to a problem it is then sufficient to use a satisfiability solver on a corresponding formula. Niemelä, Marek, and Truszczyński coined answer set programming paradigm in 1999: in this paradigm a logic program encodes problem specifications in a way that the answer sets of a logic program represent the solutions of the problem. As a result, to find solutions to a problem it is sufficient to use an answer set solver on a corresponding program. These parallels that we just draw between paradigms naturally bring up a question: what is a fundamental difference between the two? This paper takes a close look at this question.

Comments

Open Access This article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made.

Creative Commons License

Creative Commons Attribution 4.0 License
This work is licensed under a Creative Commons Attribution 4.0 License.

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