A while ago, I built the decision-making part of a human-level automatic player for a game.
The automated player for a board game was so strong that nobody I knew (including myself) was able to consistently beat it, so much so that I had to make it dumber in a controlled way to make it enjoyable for casual players.
In this post, I explain how I programmed the “brain” of this little game using standard techniques from the AI playbook before Deep Learning. You’ll learn (in case you are not already familiar with it) how to build Minimax trees and how to develop heuristics for board evaluation. The Minimax technique can apply to any adversarial game regardless of the specifics of the game I describe here.
When mobile devices started to become popular (around 2008, after the launch of the iPhone), there were few available board games. And some of the games were not really playable on the tiny screens of the time. That was, of course, the case of chess: you can’t really display a chessboard in a 3.5-inch display with a resolution of 320 x 480 pixels. You can, but it’s going to strain your eyes.