非对称分布约束优化问题
Asymmetric Distributed Constraint Optimization Problems
摘要：
分布式约束优化（DCOP）是一个强大的框架，用于表示和解决分布式组合问题，其中问题的变量由不同的代理拥有。许多多代理问题包括约束，产生不同的收益（或成本）的参与代理。受约束代理的非对称收益不能自然地由标准DCOP模型表示。本文提出了一个非对称DCOP（ADCOP）的一般框架。在ADCOP中，不同的代理可能对它们所涉及的约束具有不同的估值。新框架的桥梁之间的差距，往往有非对称结构的多智能体问题和标准的对称DCOP模型。所提出的模型比以前的尝试推广DCOP模型的好处进行了讨论和评估。创新的算法，适用于所提出的ADCOP模型的特殊属性的详细介绍。这些包括完整的算法，在运行时间和网络负载方面比使用替代表示的现有算法（对于标准DCOP）具有实质性优势。此外，标准的不完全算法（即，局部搜索算法）不适用于现有的非对称约束的DCOP表示，并且当它们被应用于新的ADCOP框架时，它们经常不能收敛到局部最优并且产生差的结果。本文提出的局部搜索算法收敛到高质量的解决方案。提出的实验证据表明，建议的本地搜索算法的ADCOPs实现高质量的解决方案，同时保持高水平的隐私。
英文摘要：
Distributed Constraint Optimization (DCOP) is a powerful framework for representing and solving distributed combinatorial problems, where the variables of the problem are owned by different agents. Many multi-agent problems include constraints that produce different gains (or costs) for the participating agents. Asymmetric gains of constrained agents cannot be naturally represented by the standard DCOP model. The present paper proposes a general framework for Asymmetric DCOPs (ADCOPs). In ADCOPs different agents may have different valuations for constraints that they are involved in. The new framework bridges the gap between multi-agent problems which tend to have asymmetric structure and the standard symmetric DCOP model. The benefits of the proposed model over previous attempts to generalize the DCOP model are discussed and evaluated. Innovative algorithms that apply to the special properties of the proposed ADCOP model are presented in detail. These include complete algorithms that have a substantial advantage in terms of runtime and network load over existing algorithms (for standard DCOPs) which use alternative representations. Moreover, standard incomplete algorithms (i.e., local search algorithms) are inapplicable to the existing DCOP representations of asymmetric constraints and when they are applied to the new ADCOP framework they often fail to converge to a local optimum and yield poor results. The local search algorithms proposed in the present paper converge to high quality solutions. The experimental evidence that is presented reveals that the proposed local search algorithms for ADCOPs achieve high quality solutions while preserving a high level of privacy.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
