Proceedings

Neural Decision-Propagation for Answer Set Programming

Thomas Eiter, Katsumi Inoue, Sota Moriyama

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Proceedings of the Thirty-Fifth International Joint Conference on Artificial Intelligence. Main Track. Pages 3819-3828.

https://doi.org/10.24963/ijcai.2026/425

Keywords

  • Knowledge Representation and Reasoning: Logic programming
  • Machine Learning: Neuro-symbolic methods/Abductive Learning
  • Knowledge Representation and Reasoning: Non-monotonic reasoning

Abstract

Integration of Answer Set Programming (ASP) with neural networks has emerged as a promising tool in Neuro-symbolic AI. While existing approaches extend the capabilities of ASP to real world domains, their reasoning pipelines depend on classical solvers, which is a bottleneck for scalability. To tackle this problem, we propose a new method to compute stable models, called decision-propagation (DProp), which alternates falsity decisions and truth propagations. Successful DProp computations are shown to capture the stable model semantics. We then develop Neural DProp (NDProp), a differentiable extension of DProp with neural computation for decisions and fuzzy evaluation for propagations. We evaluate the capabilities of NDProp for learning decision heuristics as well as neuro-symbolic integration, and compare it with existing neuro-symbolic approaches. The results show that NDProp can learn to efficiently compute stable models, and it improves accuracy and scalability on neuro-symbolic benchmarks.

Thomas Eiter, Katsumi Inoue, and Sota Moriyama. 2026. Neural Decision-Propagation for Answer Set Programming. In Proceedings of the Thirty-Fifth International Joint Conference on Artificial Intelligence, IJCAI-26. IJCAI, 3819–3828. https://doi.org/10.24963/ijcai.2026/425
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