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Go Issues!

I'm sure Andy is better placed to reply, but I'll make my points briefly.

1) The tree size in theory is as Andy describes it. If you add some expert knowledge to your AI by removing that factor, then you've optimised your search slightly. You could do that in many other cases too (not put a stone where it will be instantly removed for example). I think the point is for a learning algorithm, you may want to make the AI aware of that option in the first place.

2) Groups of stones in the start of the game are generally physically isolated though. Then it all clicks together, and you have a bigger problem to deal with!

5) As Rodney Brooks once said, when something is solved by an algorithm, AI researchers claim it's no longer part of their field. AI thereby never gets any successes! Minimax is by this definition just a search algorithm.

Though the rough outline of minimax is indeed similar to the human deliberative process, many specific details of current "standard" implementations make it very unhuman-like (problems with depth cut off, considering all options, not learning opponents reactions, static evaluation functions and assumptions).

In any case, I do not believe in the ability of Minimax to "solve" go any time soon...

Anyway, my 2p ;)

935 posts.
Sunday 19 May, 13:37
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