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Unifying Framework for Optimizations in Non-Boolean Formalisms

Published online by Cambridge University Press:  15 November 2022

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

Search-optimization problems are plentiful in scientific and engineering domains. Artificial intelligence (AI) has long contributed to the development of search algorithms and declarative programming languages geared toward solving and modeling search-optimization problems. Automated reasoning and knowledge representation are the subfields of AI that are particularly vested in these developments. Many popular automated reasoning paradigms provide users with languages supporting optimization statements. Recall integer linear programming, MaxSAT, optimization satisfiability modulo theory, (constraint) answer set programming. These paradigms vary significantly in their languages in ways they express quality conditions on computed solutions. Here we propose a unifying framework of so-called extended weight systems that eliminates syntactic distinctions between paradigms. They allow us to see essential similarities and differences between optimization statements provided by distinct automated reasoning languages. We also study formal properties of the proposed systems that immediately translate into formal properties of paradigms that can be captured within our framework.

Keywords

designanalysis and implementation of languagesknowledge representation and nonmonotonic reasoningtheory
Type
Original Article
Information
Theory and Practice of Logic Programming , Volume 23 , Issue 6 , November 2023 , pp. 1248 - 1280
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Copyright
© The Author(s), 2022. Published by Cambridge University Press

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Footnotes

*

The work was partially supported by NSF grant 1707371. We are grateful to anonymous reviewers for valuable comments on this paper.

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