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
this https URL

该论文提出了一种基于图神经网络的 DCOPs 解决方案，通过预训练的优化标签数据构建出有效的启发式方法，在分布式环境中实现了去中心化的模型推理，并结合局部搜索或回溯搜索算法，在各种测试中都取得了显著的优异表现。

分布式约束优化问题的预训练成本模型

Pretrained Cost Model for Distributed Constraint Optimization Problems

This paper explores the use of Answer Set Programming (ASP) in solving
Distributed Constraint Optimization Problems (DCOPs). The paper provides the
following novel contributions: (1) It shows how one can formulate DCOPs as
logic programs; (2) It introduces ASP-DPOP, the first DCOP algorithm that is
based on logic programming; (3) It experimentally shows that ASP-DPOP can be up
to two orders of magnitude faster than DPOP (its imperative programming
counterpart) as well as solve some problems that DPOP fails to solve, due to
memory limitations; and (4) It demonstrates the applicability of ASP in a wide
array of multi-agent problems currently modeled as DCOPs. Under consideration
in Theory and Practice of Logic Programming (TPLP).

本文探讨了 Answer Set Programming 在解决 Distributed Constraint Optimization Problems 方面的应用。其贡献包括：将 DCOPs 表示为逻辑程序；介绍了基于逻辑编程的第一个 DCOP 算法 ASP-DPOP；实验证明 ASP-DPOP 可以比 DPOP 快两个数量级，并且可以解决 DPOP 无法解决的一些问题；以及演示了 ASP 在当前被建模为 DCOPs 的各种多代理问题中的适用性。

利用逻辑编程解决分布式约束优化问题

Solving Distributed Constraint Optimization Problems Using Logic  Programming

The field of Multi-Agent System (MAS) is an active area of research within
Artificial Intelligence, with an increasingly important impact in industrial
and other real-world applications. Within a MAS, autonomous agents interact to
pursue personal interests and/or to achieve common objectives. Distributed
Constraint Optimization Problems (DCOPs) have emerged as one of the prominent
agent architectures to govern the agents' autonomous behavior, where both
algorithms and communication models are driven by the structure of the specific
problem. During the last decade, several extensions to the DCOP model have
enabled them to support MAS in complex, real-time, and uncertain environments.
This survey aims at providing an overview of the DCOP model, giving a
classification of its multiple extensions and addressing both resolution
methods and applications that find a natural mapping within each class of
DCOPs. The proposed classification suggests several future perspectives for
DCOP extensions, and identifies challenges in the design of efficient
resolution algorithms, possibly through the adaptation of strategies from
different areas.

该篇论文概述了分布式约束优化问题在多智能体系统中的应用，对其各种扩展和解析方法进行了分类并提出了未来研究方向和挑战。

分布式约束优化问题及其应用：综述

Distributed Constraint Optimization Problems and Applications: A Survey

The field of Distributed Constraint Optimization Problems (DCOPs) has gained
momentum, thanks to its suitability in capturing complex problems (e.g.,
multi-agent coordination and resource allocation problems) that are naturally
distributed and cannot be realistically addressed in a centralized manner. The
state of the art in solving DCOPs relies on the use of ad-hoc infrastructures
and ad-hoc constraint solving procedures. This paper investigates an
infrastructure for solving DCOPs that is completely built on logic programming
technologies. In particular, the paper explores the use of a general constraint
solver (a constraint logic programming system in this context) to handle the
agent-level constraint solving. The preliminary experiments show that logic
programming provides benefits over a state-of-the-art DCOP system, in terms of
performance and scalability, opening the doors to the use of more advanced
technology (e.g., search strategies and complex constraints) for solving DCOPs.

本文旨在探究利用基于逻辑编程技术的基础设施，通过使用一种通用约束求解器（此处为约束逻辑编程系统）来处理代理层次的约束求解，以解决分布式约束优化问题，并通过初步实验表明其在性能和可扩展性方面较目前最先进的 DCOP 系统有所提高，为解决 DCOP 问题提供更先进的技术手段（例如搜索策略和复杂约束) 。