Techniques used to trim down large decision trees?

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So I’ve been reading about how in chess there are an extremely high combination of moves that can be made and trying to write out every combination is impossible. In an eli5 style, how are algos like alphago able to overcome this fundamental issue? How do they go about solving this problem?

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Anonymous 0 Comments

A lot of it is heuristic based. Basically you use a heuristic that can estimate the quality of a future state, without working it all the way to completion.

For instance, in chess we might decide the count of our pieces Vs the opponents pieces.

So you can look a few steps ahead in each direction, and get a numeric quality of that branch. Then you abandon the branches where you’ve lost a bunch of pieces Vs the opponent, and focus on the more promising one.

Obviously you can come up with much better heuristics than that, e.g. weighting the importance of the pieces or maybe applying some logic about where you’d like your pieces to be on the board.

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