  Recent results have shown that the MCTS algorithm (a new, adaptive,
randomized optimization algorithm) is effective in a remarkably diverse set of
applications in Artificial Intelligence, Operations Research, and High Energy
Physics. MCTS can find good solutions without domain dependent heuristics,
using the UCT formula to balance exploitation and exploration. It has been
suggested that the optimum in the exploitation- exploration balance differs for
different search tree sizes: small search trees needs more exploitation; large
search trees need more exploration. Small search trees occur in variations of
MCTS, such as parallel and ensemble approaches. This paper investigates the
possibility of improving the performance of Ensemble UCT by increasing the
level of exploitation. As the search trees becomes smaller we achieve an
improved performance. The results are important for improving the performance
of large scale parallelism of MCTS.
