Partial constraint satisfaction
Artificial Intelligence - Special volume on constraint-based reasoning
The Distributed Constraint Satisfaction Problem: Formalization and Algorithms
IEEE Transactions on Knowledge and Data Engineering
ICMAS '00 Proceedings of the Fourth International Conference on MultiAgent Systems (ICMAS-2000)
Solving Distributed Constraint Optimization Problems Using Cooperative Mediation
AAMAS '04 Proceedings of the Third International Joint Conference on Autonomous Agents and Multiagent Systems - Volume 1
Distributed Constraint Satisfaction and Optimization with Privacy Enforcement
IAT '04 Proceedings of the IEEE/WIC/ACM International Conference on Intelligent Agent Technology
A mediation-based approach to cooperative, distributed problem solving
A mediation-based approach to cooperative, distributed problem solving
Adopt: asynchronous distributed constraint optimization with quality guarantees
Artificial Intelligence - Special issue: Distributed constraint satisfaction
Preprocessing techniques for accelerating the DCOP algorithm ADOPT
Proceedings of the fourth international joint conference on Autonomous agents and multiagent systems
Hierarchical variable ordering for distributed constraint optimization
AAMAS '06 Proceedings of the fifth international joint conference on Autonomous agents and multiagent systems
A Multi-stage Graph Decomposition Algorithm for Distributed Constraint Optimisation
IAT '06 Proceedings of the IEEE/WIC/ACM international conference on Intelligent Agent Technology
Airlift mission monitoring and dynamic rescheduling
Engineering Applications of Artificial Intelligence
DCOPolis: a framework for simulating and deploying distributed constraint reasoning algorithms
Proceedings of the 7th international joint conference on Autonomous agents and multiagent systems: demo papers
Coordination of first responders under communication and resource constraints
Proceedings of the 7th international joint conference on Autonomous agents and multiagent systems - Volume 3
Centralized, distributed or something else? making timely decisions in multi-agent systems
AAAI'07 Proceedings of the 22nd national conference on Artificial intelligence - Volume 1
M-DPOP: faithful distributed implementation of efficient social choice problems
Journal of Artificial Intelligence Research
PC-DPOP: a new partial centralization algorithm for distributed optimization
IJCAI'07 Proceedings of the 20th international joint conference on Artifical intelligence
Multi-issue negotiation protocol for agents: exploring nonlinear utility spaces
IJCAI'07 Proceedings of the 20th international joint conference on Artifical intelligence
On modeling multiagent task scheduling as a distributed constraint optimization problem
IJCAI'07 Proceedings of the 20th international joint conference on Artifical intelligence
Analyzing the performance of distributed algorithms
PerMIS '07 Proceedings of the 2007 Workshop on Performance Metrics for Intelligent Systems
Measurement techniques for multiagent systems
PerMIS '08 Proceedings of the 8th Workshop on Performance Metrics for Intelligent Systems
Asynchronous algorithms for approximate distributed constraint optimization with quality bounds
Proceedings of the 9th International Conference on Autonomous Agents and Multiagent Systems: volume 1 - Volume 1
Balancing local resources and global goals in multiply-constrained DCOP
Multiagent and Grid Systems
On communication in solving distributed constraint satisfaction problems
CEEMAS'05 Proceedings of the 4th international Central and Eastern European conference on Multi-Agent Systems and Applications
iCO2: multi-user eco-driving training environment based on distributed constraint optimization
Proceedings of the 2013 international conference on Autonomous agents and multi-agent systems
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Recent progress in Distributed Constraint Optimization Problems (DCOP) has led to a range of algorithms now available which differ in their amount of problem centralization. Problem centralization can have a significant impact on the amount of computation required by an agent but unfortunately the dominant evaluation metric of "number of cycles" fails to account for this cost. We analyze the relative performance of two recent algorithms for DCOP: OptAPO, which performs partial centralization, and Adopt, which maintains distribution of the DCOP. Previous comparison of Adopt and OptAPO has found that OptAPO requires fewer cycles than Adopt. We extend the cycles metric to define "Cycle-Based Runtime (CBR)" to account for both the amount of computation required in each cycle and the communication latency between cycles. Using the CBR metric, we show that Adopt outperforms OptAPO under a range of communication latencies. We also ask: What level of centralization is most suitable for a given communication latency? We use CBR to create performance curves for three algorithms that vary in degree of centralization, namely Adopt, OptAPO, and centralized Branch and Bound search.