Neural Decision-Propagation for Answer Set Programming

TL;DR

This paper introduces Neural DProp, a differentiable method combining neural networks with decision propagation for scalable Answer Set Programming, enhancing neuro-symbolic reasoning and learning.

Contribution

It proposes Neural DProp, a novel neural extension of decision propagation for stable model computation, improving scalability and learning in neuro-symbolic AI.

Findings

NDProp learns to efficiently compute stable models.

It improves accuracy on neuro-symbolic benchmarks.

It enhances scalability compared to classical ASP solvers.

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.