  In this work, the novel Distributed Bayesian (D-Bay) algorithm is presented
for solving multi-agent problems within the continuous Distributed Constraint
Optimization Problem (DCOP) framework. This framework extends the classical
DCOP framework towards utility functions with continuous domains. Traditional
DCOP solvers discretize the continuous domains, which increases the problem
size exponentially. D-Bay overcomes this problem by utilizing Bayesian
optimization for the adaptive sampling of variables to avoid discretization
entirely. We theoretically show that D-Bay converges to the global optimum of
the 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 a centralized approach with equidistant discretization
of the continuous domains for the sensor coordination problem. We find that our
algorithm generates better solutions while requiring less samples.
