  We study the problem of identifying the best action among a set of possible
options when the value of each action is given by a mapping from a number of
noisy micro-observables in the so-called fixed confidence setting. Our main
motivation is the application to the minimax game search, which has been a
major topic of interest in artificial intelligence. In this paper we introduce
an abstract setting to clearly describe the essential properties of the
problem. While previous work only considered a two-move game tree search
problem, our abstract setting can be applied to the general minimax games where
the depth can be non-uniform and arbitrary, and transpositions are allowed. We
introduce a new algorithm (LUCB-micro) for the abstract setting, and give its
lower and upper sample complexity results. Our bounds recover some previous
results, which were only available in more limited settings, while they also
shed further light on how the structure of minimax problems influence sample
complexity.
