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This content will become publicly available on January 7, 2026

Title: Finite-Choice Logic Programming
Logic programming, as exemplified by datalog, defines the meaning of a program as its unique smallest model: the deductive closure of its inference rules. However, many problems call for an enumeration of models that vary along some set of choices while maintaining structural and logical constraints—there is no single canonical model. The notion of stable models for logic programs with negation has successfully captured programmer intuition about the set of valid solutions for such problems, giving rise to a family of programming languages and associated solvers known as answer set programming. Unfortunately, the definition of a stable model is frustratingly indirect, especially in the presence of rules containing free variables. We propose a new formalism, finite-choice logic programming, that uses choice, not negation, to admit multiple solutions. Finite-choice logic programming contains all the expressive power of the stable model semantics, gives meaning to a new and useful class of programs, and enjoys a least-fixed-point interpretation over a novel domain. We present an algorithm for exploring the solution space and prove it correct with respect to our semantics. Our implementation, the Dusa logic programming language, has performance that compares favorably with state-of-the-art answer set solvers and exhibits more predictable scaling with problem size.  more » « less
Award ID(s):
2319894
PAR ID:
10635171
Author(s) / Creator(s):
; ;
Editor(s):
Hicks, Michael
Publisher / Repository:
Proceedings of the ACM on Programming Languages
Date Published:
Journal Name:
Proceedings of the ACM on Programming Languages
Volume:
9
Issue:
POPL
ISSN:
2475-1421
Page Range / eLocation ID:
362 to 390
Subject(s) / Keyword(s):
logic and databases constraint and logic languages nonmonotonic, default reasoning and belief revision
Format(s):
Medium: X
Sponsoring Org:
National Science Foundation
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