Strength estimation and adjustment are crucial in designing human-AI interactions, particularly in games where AI surpasses human players. This paper introduces a novel strength system, including a strength estimator (SE) and an SE-based Monte Carlo tree search, denoted as SE-MCTS, which predicts strengths from games and offers different playing strengths with human styles. The strength estimator calculates strength scores and predicts ranks from games without direct human interaction. SE-MCTS utilizes the strength scores in a Monte Carlo tree search to adjust playing strength and style. We first conduct experiments in Go, a challenging board game with a wide range of ranks. Our strength estimator significantly achieves over 80% accuracy in predicting ranks by observing 15 games only, whereas the previous method reached 49% accuracy for 100 games. For strength adjustment, SE-MCTS successfully adjusts to designated ranks while achieving a 51.33% accuracy in aligning to human actions, outperforming a previous state-of-the-art, with only 42.56% accuracy. To demonstrate the generality of our strength system, we further apply SE and SE-MCTS to chess and obtain consistent results. These results show a promising approach to strength estimation and adjustment, enhancing human-AI interactions in games. Our code is available at https://rlg.iis.sinica.edu.tw/papers/strength-estimator.

翻译：实力评估与调整在设计与人工智能交互中至关重要，特别是在AI超越人类玩家的游戏中。本文提出了一种新颖的实力系统，包括一个实力评估器（SE）和一种基于SE的蒙特卡洛树搜索，记为SE-MCTS，该系统可从游戏中预测实力，并提供具有人类风格的不同对弈实力。实力评估器无需直接人类交互，即可从游戏中计算实力分数并预测段位。SE-MCTS在蒙特卡洛树搜索中利用这些实力分数来调整对弈实力与风格。我们首先在围棋中进行了实验，这是一种具有广泛段位范围的挑战性棋盘游戏。我们的实力评估器仅通过观察15局游戏，就在段位预测上显著实现了超过80%的准确率，而先前的方法在100局游戏中仅达到49%的准确率。在实力调整方面，SE-MCTS成功调整至指定段位，同时在模仿人类动作上达到了51.33%的准确率，优于先前仅42.56%准确率的最先进方法。为了证明我们实力系统的通用性，我们进一步将SE和SE-MCTS应用于国际象棋，并得到了一致的结果。这些结果表明了一种有前景的实力评估与调整方法，能够增强游戏中的人机交互。我们的代码可在 https://rlg.iis.sinica.edu.tw/papers/strength-estimator 获取。