TY - GEN

T1 - Distributed partial constraint satisfaction problem

AU - Hirayama, Katsutoshi

AU - Yokoo, Makoto

N1 - Publisher Copyright:
© Springer-Verlag Berlin Heidelberg 1997.

PY - 1997

Y1 - 1997

N2 - Many problems in multi-agent systems can be described as distributed Constraint Satisfaction Problems (distributed CSPs), where the goal is to find a set of assignments to variables that satisfies all constraints among agents. However, when real problems are formalized as distributed CSPs, they are often over-constrained and have no solution that satisfies all constraints. This paper provides the Distributed Partial Constraint Satisfaction Problem (DPCSP) as a new framework for dealing with over-constrained situations. We also present new algorithms for solving Distributed Maximal Constraint Satisfaction Problems (DMCSPs), which belong to an important class of DPCSP. The algorithms are called the Synchronous Branch and Bound (SBB) and the Iterative Distributed Breakout (IDB). Both algorithms were tested on hard classes of over-constrained random binary distributed CSPs. The results can be summarized as SBB is preferable when we are mainly concerned with the optimality of a solution~ while IDB is preferable when we want to get a nearly optimal solution quickly.

AB - Many problems in multi-agent systems can be described as distributed Constraint Satisfaction Problems (distributed CSPs), where the goal is to find a set of assignments to variables that satisfies all constraints among agents. However, when real problems are formalized as distributed CSPs, they are often over-constrained and have no solution that satisfies all constraints. This paper provides the Distributed Partial Constraint Satisfaction Problem (DPCSP) as a new framework for dealing with over-constrained situations. We also present new algorithms for solving Distributed Maximal Constraint Satisfaction Problems (DMCSPs), which belong to an important class of DPCSP. The algorithms are called the Synchronous Branch and Bound (SBB) and the Iterative Distributed Breakout (IDB). Both algorithms were tested on hard classes of over-constrained random binary distributed CSPs. The results can be summarized as SBB is preferable when we are mainly concerned with the optimality of a solution~ while IDB is preferable when we want to get a nearly optimal solution quickly.

UR - http://www.scopus.com/inward/record.url?scp=84948973649&partnerID=8YFLogxK

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U2 - 10.1007/bfb0017442

DO - 10.1007/bfb0017442

M3 - Conference contribution

AN - SCOPUS:84948973649

SN - 3540637532

SN - 9783540637530

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

SP - 222

EP - 236

BT - Principles and Practice of Constraint Programming - CP 1997 - 3rd International Conference, CP 1997, Proceedings

A2 - Smolka, Gert

PB - Springer Verlag

T2 - 3rd International Conference on Principles and Practice of Constraint Programming, CP 1997

Y2 - 29 October 1997 through 1 November 1997

ER -