Pretrained Cost Model for Distributed Constraint Optimization Problems

TL;DR

This paper introduces GAT-PCM, a pretrained graph attention network-based heuristic model for distributed constraint optimization problems, enabling effective, decentralized problem-solving across various algorithms and benchmarks.

Contribution

The paper proposes a novel graph-based representation and a pretrained GAT model for DCOPs, facilitating generalizable and decentralized heuristics for multiple search algorithms.

Findings

GAT-PCM significantly improves performance over state-of-the-art methods.

The decentralized embedding schema enables efficient distributed inference.

Extensive experiments validate the effectiveness of GAT-PCM across benchmarks.

Abstract

Distributed Constraint Optimization Problems (DCOPs) are an important subclass of combinatorial optimization problems, where information and controls are distributed among multiple autonomous agents. Previously, Machine Learning (ML) has been largely applied to solve combinatorial optimization problems by learning effective heuristics. However, existing ML-based heuristic methods are often not generalizable to different search algorithms. Most importantly, these methods usually require full knowledge about the problems to be solved, which are not suitable for distributed settings where centralization is not realistic due to geographical limitations or privacy concerns. To address the generality issue, we propose a novel directed acyclic graph representation schema for DCOPs and leverage the Graph Attention Networks (GATs) to embed graph representations. Our model, GAT-PCM, is then pretrained with optimally labelled data in an offline manner, so as to construct effective heuristics to boost a broad range of DCOP algorithms where evaluating the quality of a partial assignment is critical, such as local search or backtracking search. Furthermore, to enable decentralized model inference, we propose a distributed embedding schema of GAT-PCM where each agent exchanges only embedded vectors, and show its soundness and complexity. Finally, we demonstrate the effectiveness of our model by combining it with a local search or a backtracking search algorithm. Extensive empirical evaluations indicate that the GAT-PCM-boosted algorithms significantly outperform the state-of-the-art methods in various benchmarks. The pretrained model is available at https://github.com/dyc941126/GAT-PCM.