随机快速无损失专家系统,像人类一样玩井字游戏

IF 1.2 Q4 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE
Aditya Jyoti Paul
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引用次数: 2

摘要

这项研究引入了一种非常快速、无损失的三字棋专家系统,该系统使用决策树T3DT,试图尽可能地模仿人类的游戏玩法。它不使用任何蛮力、极大极小或进化技术,但仍然是不可战胜的。为了让游戏玩法更像人类,我们优先考虑了随机性,《T3DT》在每一步随机选择多个最优移动之一。由于它不需要在任何时候分析完整的游戏树,所以T3DT比任何暴力破解或极大极小算法都要快得多,这已经从理论上和经验上从本研究的时钟时间分析中得到了证明。T3DT也不需要数据集或时间来训练进化模型,这使得它成为一种实用的零损失方法来玩井字游戏。
本文章由计算机程序翻译,如有差异,请以英文原文为准。

Randomised fast no-loss expert system to play tic-tac-toe like a human

Randomised fast no-loss expert system to play tic-tac-toe like a human

This study introduces a blazingly fast, no-loss expert system for tic-tac-toe using decision trees called T3DT, which 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. To make the gameplay more human-like, randomisation is prioritised 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 study. T3DT also does not need the data sets or the time to train an evolutionary model, making it a practical no-loss approach to play tic-tac-toe.

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来源期刊
Cognitive Computation and Systems
Cognitive Computation and Systems Computer Science-Computer Science Applications
CiteScore
2.50
自引率
0.00%
发文量
39
审稿时长
10 weeks
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