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Artificial Intelligence Review

, Volume 50, Issue 2, pp 161–199 | Cite as

BD-ADOPT: a hybrid DCOP algorithm with best-first and depth-first search strategies

Article

Abstract

Distributed Constraint Optimization Problem (DCOP) is a promising framework for modeling a wide variety of multi-agent coordination problems. Best-First search (BFS) and Depth-First search (DFS) are two main search strategies used for search-based complete DCOP algorithms. Unfortunately, BFS often has to deal with a large number of solution reconstructions whereas DFS is unable to promptly prune sub-optimal branch. However, their weaknesses will be remedied if the two search strategies are combined based on agents’ positions in a pseudo-tree. Therefore, a hybrid DCOP algorithm with the combination of BFS and DFS, called BD-ADOPT, is proposed, in which a layering boundary is introduced to divide all agents into BFS-based agents and DFS-based agents. Furthermore, this paper gives a rule to find a suitable layering boundary with a new strategy for the agents near the boundary to realize the seamless joint between BFS and DFS strategies. Detailed experimental results show that BD-ADOPT outperforms some famous search-based complete DCOP algorithms on the benchmark problems.

Keywords

Multi-agent systems Distributed constraint optimization problem Depth-first search strategy Best-first search strategy BD-ADOPT 

Notes

Acknowledgements

This work is partly supported by the Fundamental Research Funds for the Central University of China (Project No.106112013 CDJZR180013), the Postdoctoral Science Foundation of Chongqing in China (Project No. Xm201324) and the Innovation Projects in Chongqing (Project No. CYS14018). We are also grateful to the reviewers of this article for their kind suggestions.

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Copyright information

© Springer Science+Business Media Dordrecht 2017

Authors and Affiliations

  1. 1.College of Computer ScienceChongqing UniversityChongqingChina
  2. 2.College of Electrical EngineeringChongqing UniversityChongqingChina

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