  Diverse, top-k, and top-quality planning are concerned with the generation of
sets of solutions to sequential decision problems. Previously this area has
been the domain of classical planners that require a symbolic model of the
problem instance. This paper proposes a novel alternative approach that uses
Monte Carlo Tree Search (MCTS), enabling application to problems for which only
a black-box simulation model is available. We present a procedure for
extracting bounded sets of plans from pre-generated search trees in best-first
order, and a metric for evaluating the relative quality of paths through a
search tree. We demonstrate this approach on a path-planning problem with
hidden information, and suggest adaptations to the MCTS algorithm to increase
the diversity of generated plans. Our results show that our method can generate
diverse and high-quality plan sets in domains where classical planners are not
applicable.
