Explorative anytime local search for distributed constraint optimization

摘要

Distributed Constraint Optimization Problems (DCOPs) are an elegant model for representing and solving many realistic combinatorial problems that are distributed by nature. DCOPs are NP-hard and therefore many recent studies consider incomplete algorithms for solving them. Distributed local search algorithms, in which agents in the system hold value assignments to their variables and iteratively make decisions on whether to replace them, can be used for solving DCOPs. However, because of the differences between the global evaluation of a system's state and the private evaluation of states by agents, agents are unaware of the global best state that is explored by the algorithm. Previous attempts to use local search algorithms for solving DCOPs reported the state held by the system at the termination of the algorithm, which was not necessarily the (global) best state explored.