论文标题
学习用于解决混合整数功能DCOP的最佳温度区域
Learning Optimal Temperature Region for Solving Mixed Integer Functional DCOPs
论文作者
论文摘要
分布式约束优化问题（DCOPS）是在具有一组离散变量的多代理系统中建模协调决策问题的重要框架。后来的工作扩展了DCOP，以模拟一组连续变量的问题，该变量命名为功能DCOP（F-DCOPS）。在本文中，我们将这两个框架结合到混合整数功能DCOP（MIF-DCOP）框架中，无论其变量类型如何，都可以解决问题。然后，我们提出了一种新颖的算法$ - $分布式并行模拟退火（DPSA），在该算法中，代理商合作学习了该算法的最佳参数配置，同时还使用学习知识来解决给定的问题。最后，我们在DCOP，F-DCOP和MIF-DCOP设置中经验评估了我们的方法，并表明DPSA产生的解决方案的质量明显优于其相应设置中的最先进的非脱离算法。
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.
