论文标题
C-Cocoa：一种连续的合作约束近似算法来求解功能DCOPS
C-CoCoA: A Continuous Cooperative Constraint Approximation Algorithm to Solve Functional DCOPs
论文作者
论文摘要
分布式约束优化问题（DCOP）已被广泛用于协调合作多代理系统中的相互作用（即约束）。传统的DCOP模型假设代理拥有的变量只能采用离散值和约束的成本函数，以定义一组变量的每个可能值分配。尽管这种公式通常是合理的，但在许多应用程序中，变量是连续的决策变量，并且约束以功能形式。为了克服这一限制，提出了功能DCOP（F-DCOP）模型，该模型能够模拟具有连续变量的问题。现有的F-DCOPS算法经历了巨大的计算和通信开销。本文将连续的非线性优化方法应用于合作约束近似（可可）算法。我们从经验上表明，与现有的F-DCOP算法相比，我们的算法能够以较小的沟通成本和执行时间为代价提供高质量的解决方案。
Distributed Constraint Optimization Problems (DCOPs) have been widely used to coordinate interactions (i.e. constraints) in cooperative multi-agent systems. The traditional DCOP model assumes that variables owned by the agents can take only discrete values and constraints' cost functions are defined for every possible value assignment of a set of variables. While this formulation is often reasonable, there are many applications where the variables are continuous decision variables and constraints are in functional form. To overcome this limitation, Functional DCOP (F-DCOP) model is proposed that is able to model problems with continuous variables. The existing F-DCOPs algorithms experience huge computation and communication overhead. This paper applies continuous non-linear optimization methods on Cooperative Constraint Approximation (CoCoA) algorithm. We empirically show that our algorithm is able to provide high-quality solutions at the expense of smaller communication cost and execution time compared to the existing F-DCOP algorithms.
