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.
Original language | English |
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Title of host publication | Proceedings of the 18th International Conference on Autonomous Agents and Multiagent Systems (AAMAS 2019) |
Publisher | International Foundation for Autonomous Agents and Multiagent Systems (IFAAMAS) |
Pages | 1961-1963 |
Volume | 4 |
ISBN (Electronic) | 978-1-4503-6309-9 |
ISBN (Print) | 978-1-5108-9200-2 |
Publication status | Published - 2019 |
Event | AAMAS 2019: The 18th International Conference on Autonomous Agents and MultiAgent Systems - Montreal, Canada Duration: 13 May 2019 → 17 May 2019 |
Conference
Conference | AAMAS 2019 |
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Country/Territory | Canada |
City | Montreal |
Period | 13/05/19 → 17/05/19 |
Keywords
- Bayesian optimization
- DCOP
- Distributed optimization
- DPOP