论文标题
分布式贝叶斯：连续分布式约束优化问题解决者
Distributed Bayesian: a continuous Distributed Constraint Optimization Problem solver
论文作者
论文摘要
在这项工作中，提出了新型的分布式贝叶斯（D-Bay）算法，用于解决连续分布式约束优化问题（DCOP）框架中的多代理问题。该框架将经典的DCOP框架扩展到具有连续域的实用程序函数。传统的DCOP求解器将连续域离散，这将问题大小呈指数增加。 D-bay通过利用贝叶斯优化的变量自适应采样来克服这个问题，以避免完全离散化。从理论上讲，我们表明D-Bay会收敛到Lipschitz连续实用程序功能的DCOP的全局最佳。根据样品效率对算法的性能进行经验评估。将所提出的算法与传感器协调问题的连续域的等距离散化进行了比较。我们发现我们的算法会生成更好的解决方案，同时需要更少的样品。
In this work, the novel Distributed Bayesian (D-Bay) algorithm is presented for solving multi-agent problems within the continuous Distributed Constraint Optimization Problem (DCOP) framework. This framework extends the classical DCOP framework towards utility functions with continuous domains. Traditional DCOP solvers discretize the continuous domains, which increases the problem size exponentially. D-Bay overcomes this problem by utilizing Bayesian optimization for the adaptive sampling of variables to avoid discretization entirely. We theoretically show that D-Bay converges to the global optimum of the DCOP for Lipschitz continuous utility functions. The performance of the algorithm is evaluated empirically based on the sample efficiency. The proposed algorithm is compared to a centralized approach with equidistant discretization of the continuous domains for the sensor coordination problem. We find that our algorithm generates better solutions while requiring less samples.
