Journal
KNOWLEDGE-BASED SYSTEMS
Volume 115, Issue -, Pages 123-132Publisher
ELSEVIER SCIENCE BV
DOI: 10.1016/j.knosys.2016.10.016
Keywords
Belief propagation; Task allocation; Dynamism and openness
Categories
Funding
- National Natural Science Foundation of China [61602254]
- Natural Science Foundation of Jiangsu Province, China [BK2160968]
- Nanjing University of Information, Science and Technology, China [2015r050]
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We propose a decentralized belief propagation-based method, PD-LBP, for multi-agent task allocation in open and dynamic grid and cloud environments where both the sets of agents and tasks constantly change. PD-LBP aims at accelerating the online response to, improving the resilience from the unpredicted changing in the environments, and reducing the message passing for task allocation. To do this, PD-LBP devises two phases, pruning and decomposition. The pruning phase focuses on reducing the search space through pruning the resource providers, and the decomposition addresses decomposing the network into multiple independent parts where belief propagation can be operated in parallel. Comparison between PD-LBP and two other state-of-the-art methods, Loopy Belief Propagation-based method and Reduced Binary Loopy Belief Propagation based method, is performed. The evaluation results demonstrate the desirable efficiency of PD-LBP from both the shorter problem solving time and smaller communication requirement of task allocation in dynamic environments. (C) 2016 Elsevier B.V. All rights reserved.
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