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.

翻译：蒙特卡洛树搜索（MCTS）是一种搜索范式，最初因其在计算机围棋领域的成功而受到广泛关注。早期的理论研究为应用于树的置信上界（UCT）——MCTS最流行的实现方式——建立了正确性和收敛性边界；然而，我们对UCT在实际中如何行为的理解仍存在显著空白。在本研究中，我们通过探讨UCT在对抗性环境中是否可能表现出前瞻病理学来填补其中一个空白——这是一种最初在Minimax搜索中观察到的悖论现象，即更大的搜索努力反而导致更差的决策。我们引入了一个新颖的合成博弈家族，该家族在保持数学可分析性的同时提供了丰富的建模可能性。我们的理论和实验结果表明，从该家族中提取的一系列博弈中，UCT确实容易表现出病理行为。