In this paper, the novel Distributed Bayesian (D-Bay) algorithm is presented for solving multi-agent problems within the Continuous Distributed Constraint Optimization Problem (C-DCOP) framework. This framework extends the classical DCOP framework towards utility functions with continuous domains. D-Bay solves a C-DCOP by utilizing Bayesian optimization for the adaptive sampling of variables. We theoretically show that D-Bay converges to the global optimum of the C-DCOP for Lipschitz continuous utility functions. The performance of the algorithm is evaluated empirically based on the sample efficiency. The proposed algorithm is compared to state-of-the-art DCOP and C-DCOP solvers. The algorithm generates better solutions while requiring fewer samples.@en

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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In this paper, Mine Counter-Measures (MCM) operations with multiple cooperative Autonomous Underwater Vehicles (AUVs) are examined within the Distributed Constraint optimization Problem (DCOP) framework. The goal of an MCM-operation is to search for mines and mine-like objects within a predetermined area so that ships can pass the area through a safe transit corridor. Performance metrics, such as the expected time of completion and the level of confidence that all mine-like objects within the area have been detected, are used to quantity the utility of the operation. The AUVs coordinate their individual search segments in a distributed manner in order to maximize the global utility. The segmentation is optimized by the Compression-DPOP (C-DPOP) algorithm, which allows explicit reasoning by the AUVs about their actions based on the performance metrics. After initial segmentation of the mine threat area, subsequent optimizations are triggered by the AUVs based on the variations in sonar performance. The performance of the C-DPOP algorithm is compared to a static segmentation approach and validated using the high-fidelity Unmanned Underwater Vehicle (UUV) simulation environment based on the Gazebo simulator@en

DCOP (Distributed Constraint optimization Problem) is a framework for representing distributed multi- agent problems. However, it only allows discrete values for the decision variables, which limits its application for real-world problems. In this paper, an extension of DCOP is investigated to handle variables with continuous domains. Additionally, an iterative any-time algorithm Compression-DPOP (C-DPOP) is presented that is based on the Distributed Pseudo-tree Opti- mization Procedure (DPOP). C-DPOP iteratively samples the search space in order to handle problems that are restricted by time and memory limitations. The performance of the algorithm is examined through a mobile sensor coordination problem. The proposed algorithm outperforms DPOP with uniform sampling regarding both resource requirement and performance.

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