论文标题
随机快速的无损专家系统，像人类一样玩TIC TAC TOE
Randomized fast no-loss expert system to play tic tac toe like a human
论文作者
论文摘要
本文使用称为T3DT的决策树介绍了一个非常快速的，无损害的专家系统，用于TIC TAC TOE，该系统试图尽可能地模仿人类游戏玩法。它不利用任何蛮力，最小值或进化技术，但仍然总是无与伦比的。为了使游戏玩法更像人性化，将随机化优先考虑，T3DT随机选择每个步骤的多个最佳移动之一。由于它不需要在任何时候分析完整的游戏树，因此T3DT的速度比任何蛮力或最小算法都要快，因此在本文中，这在理论上和经验上都显示了这一点。 T3DT还不需要培训进化模型的数据集或时间，这使其成为玩TIC TAC TOE的实用方法。
This paper introduces a blazingly fast, no-loss expert system for Tic Tac Toe using Decision Trees called T3DT, that tries to emulate human gameplay as closely as possible. It does not make use of any brute force, minimax or evolutionary techniques, but is still always unbeatable. In order to make the gameplay more human-like, randomization is prioritized and T3DT randomly chooses one of the multiple optimal moves at each step. Since it does not need to analyse the complete game tree at any point, T3DT is exceptionally faster than any brute force or minimax algorithm, this has been shown theoretically as well as empirically from clock-time analyses in this paper. T3DT also doesn't need the data sets or the time to train an evolutionary model, making it a practical no-loss approach to play Tic Tac Toe.
