TY - GEN

T1 - Pseudo-tree-based incomplete algorithm for distributed constraint optimization with quality bounds

AU - Okimoto, Tenda

AU - Joe, Yongjoon

AU - Iwasaki, Atsushi

AU - Yokoo, Makoto

AU - Faltings, Boi

N1 - Copyright:
Copyright 2011 Elsevier B.V., All rights reserved.

PY - 2011

Y1 - 2011

N2 - A Distributed Constraint Optimization Problem (DCOP) is a fundamental problem that can formalize various applications related to multi-agent cooperation. Since it is NP-hard, considering faster incomplete algorithms is necessary for large-scale applications. Most incomplete algorithms generally do not provide any guarantees on the quality of solutions. Some notable exceptions are DALO, the bounded max-sum algorithm, and ADPOP. In this paper, we develop a new solution criterion called p-optimality and an incomplete algorithm for obtaining a p-optimal solution. The characteristics of this algorithm are as follows: (i) it can provide the upper bounds of the absolute/relative errors of the solution, which can be obtained a priori/a posteriori, respectively, (ii) it is based on a pseudo-tree, which is a widely used graph structure in complete DCOP algorithms, (iii) it is a one-shot type algorithm, which runs in polynomial-time in the number of agents n assuming p is fixed, and (iv) it has adjustable parameter p, so that agents can trade-off better solution quality against computational overhead. The evaluation results illustrate that this algorithm can obtain better quality solutions and bounds compared to existing bounded incomplete algorithms, while the run time of this algorithm is shorter.

AB - A Distributed Constraint Optimization Problem (DCOP) is a fundamental problem that can formalize various applications related to multi-agent cooperation. Since it is NP-hard, considering faster incomplete algorithms is necessary for large-scale applications. Most incomplete algorithms generally do not provide any guarantees on the quality of solutions. Some notable exceptions are DALO, the bounded max-sum algorithm, and ADPOP. In this paper, we develop a new solution criterion called p-optimality and an incomplete algorithm for obtaining a p-optimal solution. The characteristics of this algorithm are as follows: (i) it can provide the upper bounds of the absolute/relative errors of the solution, which can be obtained a priori/a posteriori, respectively, (ii) it is based on a pseudo-tree, which is a widely used graph structure in complete DCOP algorithms, (iii) it is a one-shot type algorithm, which runs in polynomial-time in the number of agents n assuming p is fixed, and (iv) it has adjustable parameter p, so that agents can trade-off better solution quality against computational overhead. The evaluation results illustrate that this algorithm can obtain better quality solutions and bounds compared to existing bounded incomplete algorithms, while the run time of this algorithm is shorter.

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U2 - 10.1007/978-3-642-23786-7_50

DO - 10.1007/978-3-642-23786-7_50

M3 - Conference contribution

AN - SCOPUS:80053015399

SN - 9783642237850

T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

SP - 660

EP - 674

BT - Principles and Practice of Constraint Programming, CP 2011 - 17th International Conference, Proceedings

T2 - 17th International Conference on Principles and Practice of Constraint Programming, CP 2011

Y2 - 12 September 2011 through 16 September 2011

ER -