Although algorithms for Distributed Constraint Optimization Problems (DCOPs) have emerged as a key technique for distributed reasoning, their
application faces significant hurdles in many multiagent domains due to their
inefficiency. Preprocessing techniques have been successfully used to speed up
algorithms for centralized constraint satisfaction problems. This paper introduces
a framework of very different preprocessing techniques that speed up ADOPT, an
asynchronous optimal DCOP algorithm that significantly outperforms competing
DCOP algorithms by more than one order of magnitude.