Bayesian-DPOP for continuous distributed constraint optimization problems

Conference Paper (2019)
Author(s)

Jeroen Fransman (TU Delft - Team Bart De Schutter)

Joris Sijs (TNO)

Henry Dol (TNO)

Erik Theunissen (Netherlands Defense Academy (NLDA))

BHK Schutter (TU Delft - Team Bart De Schutter, TU Delft - Delft Center for Systems and Control)

Research Group
Team Bart De Schutter
More Info
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Publication Year
2019
Language
English
Research Group
Team Bart De Schutter
Volume number
4
Pages (from-to)
1961-1963
ISBN (print)
978-1-5108-9200-2
ISBN (electronic)
978-1-4503-6309-9

Abstract

In this work, the novel algorithm Bayesian Dynamic Programming Optimization Procedure (B-DPOP) is presented to solve multi-agent problems within the Distributed Constraint Optimization Problem framework. The Bayesian optimization framework is used to prove convergence to the global optimum of the B-DPOP algorithm for Lipschitz-continuous objective functions. The proposed algorithm is assessed based on the benchmark problem known as dynamic sensor placement. Results show increased performance over related algorithms in terms of sample-efficiency.

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