问题描述
我是机器学习的初学者.我想通过教计算机玩跳棋来学习基础知识.实际上，我要学习的游戏是 Domineering 和十六进制.我选择的语言是Python
I am a machine learning beginner. I'd like to learn the basics by teaching computers to play checkers. Actually, the games I want to learn are Domineering and Hex. My language of choice is Python
这些游戏非常易于存储，规则比国际象棋简单得多，但是玩游戏的人并不多.如果我能将这个想法付诸实践，那么对尝试组合游戏非常有用理论，看看是否有计算机并找到最佳移动方式.
These games are pretty easy to store and the rules are much simpler than chess, but there aren't too many people who play. If I can get this idea off the ground it would be great for experimenting Combinatorial Game Theory to see if a computer and find the optimal move.
我从 1960年代，来自IBM的一个家伙.最初，我曾问过神经网络，但是它们说这是错误的工具.
I found this old paper on checkers from the 1960's by a guy at IBM. Originally I had asked about neural networks, but they are saying it's the wrong tool.
编辑:机器学习可能不是正确的策略.在这种情况下，出了什么问题?还有什么更好的方法?
EDIT: It could be that machine learning is not the right strategy. In that case, what goes wrong? and what is a better way?
推荐答案
您可能需要看看以下内容:支努干支(Chinook)，最高可信树，强化学习和Alpha-Beta修剪.我个人喜欢将Alpha-Beta修剪和上置树(UCT)结合起来，用于完美的信息游戏，其中每个玩家的合理动作少于10个.您可以使用时差学习"来创建位置评估功能.游戏AI可能是学习机器学习的最有趣的方式.
You might want to take a look at the following: Chinook, Upper Confidence Trees, Reinforcement Learning, and Alpha-Beta pruning. I personally like to combine Alpha-Beta Pruning and Upper Confidence Trees (UCT) for perfect information games where each player has less than 10 reasonable moves. You can use Temporal Difference Learning to create a position evaluation function. Game AI is probably the most fun way to learn machine learning.
有关所有这些主题的链接，请单击
For links to all of these topics, click on
http://artent.net/blog/2012 /09/26/checkers-and-machine-learning/
(由于堆栈溢出软件认为我是新手，所以我无法包含更多链接！)
(I was not able to include more links because the stack overflow software considers me a newbie!)
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