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How does a Board Game AI Work? (Connect 4, Othello, Chess, Checkers) – Minimax Algorithm Explained

In this video I build up the intuition for how an expert level board game AI works. We start with a very simple approach of making random moves and then progress to building board scoring heuristics and then finally to the minimax algorithm.

I will try to make a video on how to actually program this type of AI in python within the next few weeks. Make sure to subscribe to not miss that!

As a bit of background, the minimax algorithm was the same basic algorithm used in IBM Deep Blue that defeated Chess Grandmaster Gary Kasparov in 1997.

Link to my first video on how to program Connect 4 in Python:

Read more about the Minimax Algorithm:
https://en.wikipedia.org/wiki/Minimax
https://en.wikipedia.org/wiki/Alpha%E2%80%93beta_pruning
https://en.wikipedia.org/wiki/Monte_Carlo_tree_search

Link to Stanford Paper on Othello AIs that I got some diagrams from:
https://web.stanford.edu/class/cs221/2017/restricted/p-final/man4/final.pdf

Link to Medium Article on Programming a Chess AI:
https://medium.freecodecamp.org/simple-chess-ai-step-by-step-1d55a9266977

Thank you guys for supporting my channel! Make sure to through this video a thumbs up and also subscribe! 🙂

Leave me a comment on what my next video should be on!

Video transcription:

Tags:

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