{"type":"ScholarlyArticle","title":"Neural Decision-Propagation for Answer Set Programming","authors":[{"name":"Thomas Eiter","slug":"thomas-eiter","given":"Thomas","family":"Eiter","affiliation":""},{"name":"Katsumi Inoue","slug":"katsumi-inoue","given":"Katsumi","family":"Inoue","affiliation":""},{"name":"Sota Moriyama","slug":"sota-moriyama","given":"Sota","family":"Moriyama","affiliation":""}],"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.","doi":"10.24963/ijcai.2026/425","url":"https://ijcai.org/proceedings/2026/425","pdf":"https://ijcai.org/proceedings/2026/0425.pdf","markdown":"https://ijcai.org/proceedings/2026/425.md","bibtex":"https://ijcai.org/proceedings/2026/bibtex/425","volume":{"slug":"2026","title":"Proceedings of the Thirty-Fifth International Joint Conference on Artificial Intelligence","short_title":"IJCAI 2026","url":"https://ijcai.org/proceedings/2026/","year":2026,"location":"Bremen, Germany","dates":"15-21 August 2026","editors":"Diego Calvanese","isbn":"978-1-956792-09-6","publisher":"International Joint Conferences on Artificial Intelligence Organization","doi_prefix":"10.24963/ijcai.2026"},"section":"Knowledge Representation and Reasoning","track":"Main Track","number":425,"pages":{"first":3819,"last":3828},"keywords":["Knowledge Representation and Reasoning: Logic programming","Machine Learning: Neuro-symbolic methods/Abductive Learning","Knowledge Representation and Reasoning: Non-monotonic reasoning"],"videos":[],"references":[],"license_note":"Copyright International Joint Conferences on Artificial Intelligence."}