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

After the Second World War, chemical warfare agents and munitions were dumped in the Baltic Sea and the North Sea. In order to assess the severity of the environmental consequences, it is important that the chemical warfare agents are located and their condition is investigated as soon as possible. In order to reduce this time, the search could be performed by Autonomous Underwater Vehicles (AUVs). The goal of this thesis is to develop algorithms that can be applied during underwater operations to allow AUVs to optimize their actions based on a global objective function without centralized communications. The search problem is modeled within the Distributed Constraint Optimization Problem (DCOP) framework to be able to explicitly define both computational agents and their communications. In order to be applicable to AUV operations, both benchmark problems and real-world problems with continuous domains are modelled within the Continuous DCOP (C-DCOP) framework. This preserves the flexibility of modeling inherent in a DCOP while removing the limitations imposed by the discrete definitions. Two C-DCOP algorithms are presented in this thesis. The Compression-DPOP (C-DPOP) algorithm discretizes the domain of each of the variables and compresses their domains in order to refine the search space at every iteration. The Distributed Bayesian (D-Bay) algorithm leverages Bayesian optimization to solve C-DCOPs without any need for discretization by modelling the effects of the variables on the global utility as Gaussian processes. Results from high-fidelity simulations and real-world experiments are given for real-world multi-agent search problems. A mine countermeasures operation is simulated in which AUVs update their search areas during the search based on sonar performance. Assigned areas are re-distributed in order to optimize metrics relating to the expected time of completion and the level of confidence that all mine-like objects have been detected. Moreover, real-world experimental results are presented for a multi Unmanned Aerial Vehicle (UAV) search problem. By improving the autonomy of AUVs, the search efficiency can be increased through the cooperative optimization of their actions during the operation. The research in this thesis contributes to this strategy by means of the developed algorithms and their applicability to real-world problems.@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.

@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.

@en

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