Recent progress in Distributed Constraint Optimization Problems (DCOP) has led to a range of algorithms now available which differ in their amount of problem centralization. Problem centralization can have a significant impact on the amount of computation required by an agent but unfortunately the dominant evaluation metric of "number of cycles" fails to account for this cost. We analyze the relative performance of two recent algorithms for DCOP: OptAPO, which performs partial centralization, and Adopt, which maintains distribution of the DCOP. Previous comparison of Adopt and OptAPO has found that OptAPO requires fewer cycles than Adopt. We extend the cycles metric to define "Cycle-Based Runtime (CBR)" to account for both the amount of computation required in each cycle and the communication latency between cycles. Using the CBR metric, we show that Adopt outperforms OptAPO under a range of communication latencies. We also ask: What level of centralization is most suitable for a given communication latency? We use CBR to create performance curves for three algorithms that vary in degree of centralization, namely Adopt, OptAPO, and centralized Branch and Bound search.
The Multiagent Agreement Problem (MAP) is a special form of Distributed Constraint Optimization (DCOP) that requires agents to choose values for variables to satisfy not only their own constraints, but also equality constraints with other agents. We introduce the AdoptMVA algorithm, an extension of the existing Adopt algorithm, designed to take advantage of MAP domains where agents often control multiple variables. We also propose an approach to agent ordering which leverages known ordering techniques from the centralized and distributed constraint satisfaction literature and applies them to MAPs. By combining ordering at the agent level with orderings at the variable level, we hope to obtain efficient global orderings. While the contributions discussed in this paper are applicable to general DCOPs, we focus our evaluation on MAPs because we feel it is a significant problem class worthy of specific attention.
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