  UCT has recently emerged as an exciting new adversarial reasoning technique
based on cleverly balancing exploration and exploitation in a Monte-Carlo
sampling setting. It has been particularly successful in the game of Go but the
reasons for its success are not well understood and attempts to replicate its
success in other domains such as Chess have failed. We provide an in-depth
analysis of the potential of UCT in domain-independent settings, in cases where
heuristic values are available, and the effect of enhancing random playouts to
more informed playouts between two weak minimax players. To provide further
insights, we develop synthetic game tree instances and discuss interesting
properties of UCT, both empirically and analytically.
