Incremental DCOP search algorithms for solving dynamic DCOPs
William Yeoh, Pradeep Reddy Varakantham, Xiaoxun Sun, Sven Koenig · Adaptive Agents and Multi-Agents Systems · 2011
Distributed constraint optimization problems (DCOPs) are well-suited for modeling multi-agent coordination problems. However, most research has focused on developing algorithms for solving static DCOPs. In this paper, we model dynamic DCOPs as sequences of (static) DCOPs with changes from one DCOP to the next one in the sequence. We introduce the ReuseBounds procedure, which can be used by any-space ADOPT and any-space BnB-ADOPT to find cost-minimal solutions for all DCOPs in the sequence faster than by solving each DCOP individually. This procedure allows those agents that are guaranteed to remain unaffected by a change to reuse their lower and upper bounds from the previous DCOP when solving the next one in the sequence. Our experimental results show that the speedup gained from this procedure increases with the amount of memory the agents have available.