RMB-DPOP: Refining MB-DPOP by Reducing Redundant Inferences

25 Feb 2020 Chen Ziyu Zhang Wenxin Deng Yanchen Chen Dingding Li Qing

MB-DPOP is an important complete algorithm for solving Distributed Constraint Optimization Problems (DCOPs) by exploiting a cycle-cut idea to implement memory-bounded inference. However, each cluster root in the algorithm is responsible for enumerating all the instantiations of its cycle-cut nodes, which would cause redundant inferences when its branches do not have the same cycle-cut nodes... (read more)

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  • MULTIAGENT SYSTEMS
  • DISTRIBUTED, PARALLEL, AND CLUSTER COMPUTING