学习混合泛函DCOP的最佳温度区域
Learning Optimal Temperature Region for Solving Mixed Integer Functional DCOPs
摘要：
分布式约束优化问题（DCOPs）是一个重要的框架，用于建模具有一组离散变量的多智能体系统的协调决策问题。后来的工作扩展了DCOPs模型的问题与一组连续的变量，称为功能DCOPs（F-DCOPs）。在本文中，我们将这两个框架结合成混合的双功能DCOP（MIF-DCOP）框架，可以处理的问题，而不管其变量的类型。然后，我们提出了一种新的算法$-$分布式并行模拟退火（DPSA），代理合作学习的最佳参数配置的算法，同时也解决了给定的问题，使用学到的知识。最后，我们经验性地评估我们的方法在DCOP，F-DCOP和MIF-DCOP设置，并表明DPSA产生的解决方案的质量显着优于国家的最先进的非精确算法在其相应的设置。
英文摘要：
Distributed Constraint Optimization Problems (DCOPs) are an important framework for modeling coordinated decision-making problems in multi-agent systems with a set of discrete variables. Later works have extended DCOPs to model problems with a set of continuous variables, named Functional DCOPs (F-DCOPs). In this paper, we combine both of these frameworks into the Mixed Integer Functional DCOP (MIF-DCOP) framework that can deal with problems regardless of their variables' type. We then propose a novel algorithm $-$ Distributed Parallel Simulated Annealing (DPSA), where agents cooperatively learn the optimal parameter configuration for the algorithm while also solving the given problem using the learned knowledge. Finally, we empirically evaluate our approach in DCOP, F-DCOP, and MIF-DCOP settings and show that DPSA produces solutions of significantly better quality than the state-of-the-art non-exact algorithms in their corresponding settings.
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