The distributed constraint satisfaction problem (CSP) is a general formalization used to represent problems in distributed multi-agent systems. To deal with realistic problems, multiple local variables may be required within each autonomous agent. A number of heuristics have been developed for solving such multiple local variable problems. However, these approaches do not always guarantee agent independence and have low efficiency search mechanisms. In this paper, we are interested in increasing search efficiency for distributed CSPs. To this end we present a new algorithm using unsatisfied constraint densities to dynamically determine agent ordering during the search. Variables having a total order relationship only exist in the local agent. The independence of agents is guaranteed and agents without neighboring relationships can run concurrently and asynchronously. As a result of using nogoods to guarantee completeness, we developed a new technique called nogood repairing, which greatly reduces the number of nogoods stored and communication costs during the search, leading to further efficiency gains. In an empirical study, we show our new approach outperforms an equivalent static ordering algorithm and a current state-of-the-art technique in terms of execution time, memory usage and communication cost.
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