Network-based heuristics for constraint-satisfaction problems
Artificial Intelligence
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Journal of the ACM (JACM)
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AAMAS '04 Proceedings of the Third International Joint Conference on Autonomous Agents and Multiagent Systems - Volume 1
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UAI '04 Proceedings of the 20th conference on Uncertainty in artificial intelligence
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Artificial Intelligence
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Artificial Intelligence - Special issue: Distributed constraint satisfaction
Decentralised coordination of mobile sensors using the max-sum algorithm
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CP'10 Proceedings of the 16th international conference on Principles and practice of constraint programming
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Agent-based decentralised coordination for sensor networks using the max-sum algorithm
Autonomous Agents and Multi-Agent Systems
Group planning with time constraints
Annals of Mathematics and Artificial Intelligence
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In this paper we address efficient decentralised coordination of co-operative multi-agent systems by taking into account the actual computation and communication capabilities of the agents. We consider coordination problems that can be framed as Distributed Constraint Optimisation Problems, and as such, are suitable to be deployed on large scale multi-agent systems such as sensor networks or multiple unmanned aerial vehicles. Specifically, we focus on techniques that exploit structural independence among agents' actions to provide optimal solutions to the coordination problem, and, in particular, we use the Generalized Distributive Law (GDL) algorithm. In this settings, we propose a novel resource aware heuristic to build junction trees and to schedule GDL computations across the agents. Our goal is to minimise the total running time of the coordination process, rather than the theoretical complexity of the computation, by explicitly considering the computation and communication capabilities of agents. We evaluate our proposed approach against DPOP, RDPI and a centralized solver on a number of benchmark coordination problems, and show that our approach is able to provide optimal solutions for DCOPs faster than previous approaches. Specifically, in the settings considered, when resources are scarce our approach is up to three times faster than DPOP (which proved to be the best among the competitors in our settings).