This project was inspired by the desire to teach a computer to do things that even we aren't very good at.

What it does

Plays an advanced and difficult form of tic tac toe, and learns without supervision.

How we built it

Combining two machine learning algorithms to provide an answer in every case. The two algoritms are Monte Carlo Trees, and Artificial Neural Networks.

Challenges we ran into

The main challenge was discovering our own set of learning algorithms that allow for normal neural network training when you don't know exactly what went wrong. So we had to come up with our own. Because of the high complexity and search space of the game, storing the monte carlo tree in memory forced us to add more memory to the system.

Accomplishments that we're proud of

We mapped out almost a million states of game, and had the AI play over 40,000 matches.

What we learned

How to use machine learning libraries, and how to get data such that we can successfully teach an AI to play a complex game.

What's next for Ultic AI

The next step would be to try out the system on an even more complex game, such as Go, and to give it better hardware so it can become a master.

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