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A Bound-Independent Pruning Technique to Speeding up Tree-Based Complete Search Algorithms for Distributed Constraint Optimization Problems

Authors Xiangshuang Liu, Ziyu Chen, Dingding Chen, Junsong Gao

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Xiangshuang Liu
  • College of Computer Science, Chongqing University, China
Ziyu Chen
  • College of Computer Science, Chongqing University, China
Dingding Chen
  • College of Computer Science, Chongqing University, China
Junsong Gao
  • College of Computer Science, Chongqing University, China

Acknowledgements

We would like to thank the reviewers for their valuable comments, and Dr. Ferdinando Fioretto and Dr. Yanchen Deng for their helpful suggestions.

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Xiangshuang Liu, Ziyu Chen, Dingding Chen, and Junsong Gao. A Bound-Independent Pruning Technique to Speeding up Tree-Based Complete Search Algorithms for Distributed Constraint Optimization Problems. In 27th International Conference on Principles and Practice of Constraint Programming (CP 2021). Leibniz International Proceedings in Informatics (LIPIcs), Volume 210, pp. 41:1-41:17, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2021)
https://doi.org/10.4230/LIPIcs.CP.2021.41

Abstract

Complete search algorithms are important methods for solving Distributed Constraint Optimization Problems (DCOPs), which generally utilize bounds to prune the search space. However, obtaining high-quality lower bounds is quite expensive since it requires each agent to collect more information aside from its local knowledge, which would cause tremendous traffic overheads. Instead of bothering for bounds, we propose a Bound-Independent Pruning (BIP) technique for existing tree-based complete search algorithms, which can independently reduce the search space only by exploiting local knowledge. Specifically, BIP enables each agent to determine a subspace containing the optimal solution only from its local constraints along with running contexts, which can be further exploited by any search strategies. Furthermore, we present an acceptability testing mechanism to tailor existing tree-based complete search algorithms to search the remaining space returned by BIP when they hold inconsistent contexts. Finally, we prove the correctness of our technique and the experimental results show that BIP can significantly speed up state-of-the-art tree-based complete search algorithms on various standard benchmarks.

Subject Classification

ACM Subject Classification
  • Computing methodologies → Cooperation and coordination
Keywords
  • DCOP
  • complete algorithms
  • search

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