Hypergraph Neural Networks Accelerate MUS Enumeration

TL;DR

This paper introduces a domain-agnostic hypergraph neural network approach to speed up the enumeration of minimal unsatisfiable subsets in constraint satisfaction problems, reducing computational costs.

Contribution

It presents a novel HGNN-based reinforcement learning method that accelerates MUS enumeration without relying on explicit variable-constraint relationships.

Findings

The method effectively accelerates MUS enumeration compared to traditional approaches.

It can enumerate more MUSes within the same satisfiability check budget.

Experimental results validate the approach's efficiency and effectiveness.

Abstract

Enumerating Minimal Unsatisfiable Subsets (MUSes) is a fundamental task in constraint satisfaction problems (CSPs). Its major challenge is the exponential growth of the search space, which becomes particularly severe when satisfiability checks are expensive. Recent machine learning approaches reduce this cost for Boolean satisfiability problems but rely on explicit variable-constraint relationships, limiting their application domains. This paper proposes a domain-agnostic method to accelerate MUS enumeration using Hypergraph Neural Networks (HGNNs). The proposed method incrementally builds a hypergraph with constraints as vertices and MUSes enumerated until the current step as hyperedges, and employs an HGNN-based agent trained via reinforcement learning to minimize the number of satisfiability checks required to obtain an MUS. Experimental results demonstrate the effectiveness of our approach in accelerating MUS enumeration, showing that our method can enumerate more MUSes within the same satisfiability check budget compared to conventional methods.