Constraint composite graph-based lifted message passing for distributed constraint optimization problems

The Distributed Constraint Optimization Problem (DCOP) offers a powerful approach for the description and resolution of cooperative multi-agent problems. In this model, a group of agents coordinates their actions to optimize a global objective function, taking into account their local preferences. In the majority of DCOP algorithms, agents operate on three main graphical representations of the problem: (a) the constraint graph, (b) the pseudo-tree, or (c) the factor graph. In this paper, we introduce the Constraint Composite Graph (CCG) for DCOPs, an alternative graphical representation on which agents can coordinate their assignments to solve the distributed problem suboptimally. By leveraging this representation, agents are able to reduce the size of the problem. We propose a novel variant of Max-Sum–a popular DCOP incomplete algorithm–called CCG-Max-Sum, which is applied to CCGs. We also demonstrate the efficiency and effectiveness of CCG-Max-Sum on DCOP benchmarks based on several network topologies.