The server component for Tic-tac-toe Collection has always been based on Azure Functions, a serverless compute platform by Microsoft. It is currently used to run the cloud AI and the game simulation used for fairness estimation, and does this by running the exact same code as the app. And, pretty much like the blog, deploying it was a slightly error-prone mostly manual process.

But like the blog, I’ve automated the deployment using an Azure pipeline.

The first update deployed with the new system is an improvement to the fairness estimation.

Estimating fairness

The fairness estimation is performed by simulating a game for a few moves, and then seeing how each of the AI rates its best possible next move. A big problem with this method is that the AI is probabilistic, so for a single simulation it is unlikely to be accurate (and some times it will end up being terrible). And, since the result for each combination of settings is cached, if an estimate is bad, it will stay bad.

With the update however, each subsequent request for a fairness estimation queues another one to run, and the results are averaged. So hopefully over time the estimation will actually become accurate.

Considering draws

Another change is draws are now considered. For example, previously, the estimation for standard tic-tac-toe gave the first player a score of 0.91 (on a scale of 0 - 1). Which more or less means a 91% chance of winning, assuming draws aren’t possible. Now, the first player gets a score of 0.68, which is closer to what your intuition would suggest your chances of winning tic-tac-toe is.