Adapting Improved Upper Confidence Bounds for Monte-Carlo Tree Search
  The UCT algorithm, which combines the UCB algorithm and Monte-Carlo Tree
Search (MCTS), is currently the most widely used variant of MCTS. Recently, a
number of investigations into applying other bandit algorithms to MCTS have
produced interesting results. In this research, we will investigate the
possibility of combining the improved UCB algorithm, proposed by Auer et al.
(2010), with MCTS. However, various characteristics and properties of the
improved UCB algorithm may not be ideal for a direct application to MCTS.
Therefore, some modifications were made to the improved UCB algorithm, making
it more suitable for the task of game tree search. The Mi-UCT algorithm is the
application of the modified UCB algorithm applied to trees. The performance of
Mi-UCT is demonstrated on the games of $9\times 9$ Go and $9\times 9$ NoGo, and
has shown to outperform the plain UCT algorithm when only a small number of
playouts are given, and rougly on the same level when more playouts are
available.
