machine learning in Python to play checkers? [closed]

Question

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

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.

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.

EDIT: It could be that machine learning is not the right strategy. In that case, what goes wrong? and what is a better way?

Answer 1

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.

For links to all of these topics, click on

http://artent.net/blog/2012/09/26/checkers-and-machine-learning/

(I was not able to include more links because the stack overflow software considers me a newbie!)