Neural Decision-Propagation for Answer Set Programming
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 ...
| Authors | Thomas Eiter, Katsumi Inoue, Sota Moriyama |
|---|---|
| Primary category | cs.AI |
| Published | 3 May 2026 |
| Last updated at source | 30 May 2026 |
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