C-CoCoA：一种求解泛函DCOP的连续合作约束近似算法
C-CoCoA: A Continuous Cooperative Constraint Approximation Algorithm to Solve Functional DCOPs
摘要：
分布式约束优化问题（DCOPs）已被广泛用于协调合作多智能体系统中的相互作用（即约束）。传统的DCOP模型假设代理人拥有的变量只能取离散值，并为一组变量的每一种可能的值分配定义约束的成本函数。虽然这个公式通常是合理的，但在许多应用中，变量是连续的决策变量，约束是函数形式的。为了克服这一局限性，功能DCOP（F-DCOP）模型，提出了能够建模的问题与连续变量。现有的F-DCOPs算法具有巨大的计算和通信开销。将连续非线性优化方法应用于协同约束近似算法。我们的经验表明，我们的算法是能够提供高质量的解决方案，在较小的通信成本和执行时间的代价相比，现有的F-DCOP算法。
英文摘要：
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.
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