C-CoCoA: A Continuous Cooperative Constraint Approximation Algorithm to Solve Functional DCOPs

Abstract

Distributed Constraint Optimization Problems (DCOPs) are a suitable formulation for coordinating interactions (i.e. constraints) in cooperative multi-agent systems. The traditional DCOP model deals with variables that can take only discrete values. However, there are many applications where the variables are continuous decision variables. The existing methods for solving DCOPs with continuous variables come with a huge computation and communication overhead. In this paper, we apply continuous non-linear optimization methods on Cooperative Constraint Approximation (CoCoA) algorithm. Empirical results show that our algorithm is able to provide high-quality solutions at the expense of small communication cost and execution time.