  In black-box function optimization, we need to consider not only controllable
design variables but also uncontrollable stochastic environment variables. In
such cases, it is necessary to solve the optimization problem by taking into
account the uncertainty of the environmental variables. Chance-constrained (CC)
problem, the problem of maximizing the expected value under a certain level of
constraint satisfaction probability, is one of the practically important
problems in the presence of environmental variables. In this study, we consider
distributionally robust CC (DRCC) problem and propose a novel DRCC Bayesian
optimization method for the case where the distribution of the environmental
variables cannot be precisely specified. We show that the proposed method can
find an arbitrary accurate solution with high probability in a finite number of
trials, and confirm the usefulness of the proposed method through numerical
experiments.
