Towards End-to-End ASP Computation

We propose an end-to-end approach for Answer Set Programming (ASP) and linear algebraically compute stable models satisfying given constraints. The idea is to implement Lin-Zhao’s theorem together with constraints directly in vector spaces as numerical minimization of a cost function constructed from a matricized normal logic program, loop formulas in Lin-Zhao’s theorem and constraints, thereby no use of symbolic ASP or SAT solvers involved in our approach. We also propose precomputation that shrinks the program size and heuristics for loop formulas to reduce computational difficulty. We empirically test our approach with programming examples including the three-coloring and Hamiltonian cycle problems.

Publication Details

Journal
Neurosymbolic Artificial Intelligence
Published
2026-01-01
DOI
https://doi.org/10.1177/29498732261419298
Primary Topic
Logic, Reasoning, and Knowledge
Type
article
Field-Weighted Citation Impact
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article

Towards End-to-End ASP Computation

Neurosymbolic Artificial Intelligence
Logic, Reasoning, and Knowledge
article

Towards End-to-End ASP Computation

article en

Abstract

We propose an end-to-end approach for Answer Set Programming (ASP) and linear algebraically compute stable models satisfying given constraints. The idea is to implement Lin-Zhao’s theorem together with constraints directly in vector spaces as numerical minimization of a cost function constructed from a matricized normal logic program, loop formulas in Lin-Zhao’s theorem and constraints, thereby no use of symbolic ASP or SAT solvers involved in our approach. We also propose precomputation that shrinks the program size and heuristics for loop formulas to reduce computational difficulty. We empirically test our approach with programming examples including the three-coloring and Hamiltonian cycle problems.

Neurosymbolic Artificial IntelligenceVol. 2
Openalex Percentile: Top 12%
Logic, Reasoning, and Knowledge
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Towards End-to-End ASP Computation · Neurosymbolic Artificial Intelligence (2026) | TGRS Research Map | TGRS