  Monte-Carlo Tree Search (MCTS) is a search paradigm that first found
prominence with its success in the domain of computer Go. Early theoretical
work established the soundness and convergence bounds for Upper Confidence
bounds applied to Trees (UCT), the most popular instantiation of MCTS; however,
there remain notable gaps in our understanding of how UCT behaves in practice.
In this work, we address one such gap by considering the question of whether
UCT can exhibit lookahead pathology in adversarial settings -- a paradoxical
phenomenon first observed in Minimax search where greater search effort leads
to worse decision-making. We introduce a novel family of synthetic games that
offer rich modeling possibilities while remaining amenable to mathematical
analysis. Our theoretical and experimental results suggest that UCT is indeed
susceptible to pathological behavior in a range of games drawn from this
family.
