Phase transitions and the search problem
Artificial Intelligence - Special volume on frontiers in problem solving: phase transitions and complexity
Algorithms for Distributed Constraint Satisfaction: A Review
Autonomous Agents and Multi-Agent Systems
The Distributed Constraint Satisfaction Problem: Formalization and Algorithms
IEEE Transactions on Knowledge and Data Engineering
Nexus: Small Worlds and the Groundbreaking Theory of Networks
Nexus: Small Worlds and the Groundbreaking Theory of Networks
Framework for Modeling Reordering Heuristics for Asynchronous Backtracking
IAT '06 Proceedings of the IEEE/WIC/ACM international conference on Intelligent Agent Technology
Exploiting weak dependencies in tree-based search
Proceedings of the 2009 ACM symposium on Applied Computing
Distributed Meeting Scheduling
Proceedings of the 2007 conference on Artificial Intelligence Research and Development
Journal of Artificial Intelligence Research
IJCAI'99 Proceedings of the 16th international joint conference on Artificial intelligence - Volume 2
IJCAI'01 Proceedings of the 17th international joint conference on Artificial intelligence - Volume 1
Asynchronous backtracking without adding links: a new member in the ABT family
Artificial Intelligence - Special issue: Distributed constraint satisfaction
Dynamic configuration of agent organizations
IJCAI'09 Proceedings of the 21st international jont conference on Artifical intelligence
Preferential Attachment in Constraint Networks
ICTAI '09 Proceedings of the 2009 21st IEEE International Conference on Tools with Artificial Intelligence
Asynchronous inter-level forward-checking for DisCSPs
CP'09 Proceedings of the 15th international conference on Principles and practice of constraint programming
Dynamic distributed backjumping
CSCLP'04 Proceedings of the 2004 joint ERCIM/CoLOGNET international conference on Recent Advances in Constraints
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A Distributed Constraint Satisfaction Problem DisCSP is a constraint satisfaction problem in which variables and constraints are distributed among multiple agents. Various algorithms for solving DisCSPs have been developed, which are intended for general purposes, i.e., they can be applied to any network structure. However, if a network has some particular structure, e.g., the network structure is scale-free, we can expect that some specialized algorithms/heuristics, which are tuned for the network structure, can outperform general purpose algorithms/heuristics. In this paper, as an initial step toward developing specialized algorithms for particular network structures, we examine variable-ordering heuristics in scale-free networks. We use the classic asynchronous backtracking algorithm ABT as a baseline algorithm and examine the effect of variable-ordering heuristics. First, we show that the choice of variable-ordering heuristics is more influential in scale-free networks than in random networks. Furthermore, we develop a novel variable-ordering heuristic that is specialized to scale-free networks. In the evaluations, we show that our new variable-ordering heuristic is more effective than a standard degree-based variable-ordering heuristic. Our proposed heuristic reduces the required cycles by 30% at the critical point where the required cycles are maximum.