Right, but that seems like it'd be a limitation of algorithmic play, but not necessarily of the neural nets of AlphaGo - though since the neural nets are primarily built through AlphaGo playing against itself, I would suspect that such deep "flaws" would be difficult to root out.
I'd imagine human players don't have as deep search trees as computers, but stronger policy networks. That means that you can exploit the humans by choosing move sequences that evaluate lowly up to some depth, and surge in value in the deepest depth.
Also, I'd imagine that you could have a NN that tries to evaluate how "confusing" or "hard to read" a move is to human player, and use that as a factor in evaluating moves. But I'd imagine it's hard to find data for training that kind of a NN.
