Finding the upper envelope of n line segments in O(n log n) time
Information Processing Letters
Information Theory, Inference & Learning Algorithms
Information Theory, Inference & Learning Algorithms
Network coverage using low duty-cycled sensors: random & coordinated sleep algorithms
Proceedings of the 3rd international symposium on Information processing in sensor networks
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
Adopt: asynchronous distributed constraint optimization with quality guarantees
Artificial Intelligence - Special issue: Distributed constraint satisfaction
A robust architecture for distributed inference in sensor networks
IPSN '05 Proceedings of the 4th international symposium on Information processing in sensor networks
Power management in energy harvesting sensor networks
ACM Transactions on Embedded Computing Systems (TECS) - Special Section LCTES'05
Decentralised coordination of low-power embedded devices using the max-sum algorithm
Proceedings of the 7th international joint conference on Autonomous agents and multiagent systems - Volume 2
A scalable method for multiagent constraint optimization
IJCAI'05 Proceedings of the 19th international joint conference on Artificial intelligence
The generalized distributive law
IEEE Transactions on Information Theory
Factor graphs and the sum-product algorithm
IEEE Transactions on Information Theory
On the optimality of solutions of the max-product belief-propagation algorithm in arbitrary graphs
IEEE Transactions on Information Theory
Local search for distributed asymmetric optimization
Proceedings of the 9th International Conference on Autonomous Agents and Multiagent Systems: volume 1 - Volume 1
Sensor placement and coordination via distributed multi-agent cooperative control
Proceedings of the 3rd International Conference on PErvasive Technologies Related to Assistive Environments
A Hybrid Continuous Max-Sum Algorithm for Decentralised Coordination
Proceedings of the 2010 conference on ECAI 2010: 19th European Conference on Artificial Intelligence
A tool for sensor placement and system monitoring in assistive environments
Proceedings of the 4th International Conference on PErvasive Technologies Related to Assistive Environments
SBDO: a new robust approach to dynamic distributed constraint optimisation
PRIMA'10 Proceedings of the 13th international conference on Principles and Practice of Multi-Agent Systems
Max/min-sum distributed constraint optimization through value propagation on an alternating DAG
Proceedings of the 11th International Conference on Autonomous Agents and Multiagent Systems - Volume 1
Concurrent forward bounding for distributed constraint optimization problems
Artificial Intelligence
Risk-neutral bounded max-sum for distributed constraint optimization
Proceedings of the 28th Annual ACM Symposium on Applied Computing
Distributed Gibbs: a memory-bounded sampling-based DCOP algorithm
Proceedings of the 2013 international conference on Autonomous agents and multi-agent systems
Agent-based decentralised coordination for sensor networks using the max-sum algorithm
Autonomous Agents and Multi-Agent Systems
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In this paper we address the problem of decentralised coordination for agents that must make coordinated decisions over continuously valued control parameters (as is required in many real world applications). In particular, we tackle the social welfare maximisation problem, and derive a novel continuous version of the max-sum algorithm. In order to do so, we represent the utility function of the agents by multivariate piecewise linear functions, which in turn are encoded as simplexes. We then derive analytical solutions for the fundamental operations required to implement the max-sum algorithm (specifically, addition and marginal maximisation of general n-ary piecewise linear functions). We empirically evaluate our approach on a simulated network of wireless, energy constrained sensors that must coordinate their sense/sleep cycles in order to maximise the system-wide probability of event detection. We compare the conventional discrete max-sum algorithm with our novel continuous version, and show that the continuous approach obtains more accurate solutions (up to a 10% increase) with a lower communication overhead (up to half of the total message size).