I think the "weakness" isn't that much of a weakness in the sense, that it's still playing optimally given it's search space – but it doesn't know how to overplay to make a comeback. (Overplay is a non-optimal play that is intended to be confusing for the opponent. AlphaGo doesn't regard it's opponent in any way, or assess what might be confusing for him, it just plays moves that it thinks are optimal.)
A (min-max, alpha-beta-pruning) tree search is the optimal way to determine your best move if you can afford to search the situation space globally. However, as that's clearly impossible, there's clever ways to reduce the search space. Random pruning, as a "normal" monte carlo search would do, or semi-random pruning with a neural network estimating the situations, like AlphaGo does.
However, as the search space is now non-global, it might exclude the optimal solution. And thus, the min-max-assumption doesn't hold: your opponent might come up with moves that you didn't think of, screwing your calculations off.
If your opponent is a god ( = can afford global search space), or at least has a search space that is a strict superset of yours, it's "game over, man".
But: if your opponent isn't a god, it's likely that his search space is NOT the same as yours. And you can exploit the fact. If you have any idea what kind of search space your opponent has, you can come up with moves, that he couldn't have included in his tree search, and bet on that his/her "hidden" moves aren't better than yours.
Currently AlphaGo doesn't do that. It behaves like it'd be playing against... well, against another AlphaGo.
> If your opponent [...] has a search space that is a strict superset of yours, it's "game over, man".
Not necessarily. I think that's what we saw in game 4; that despite AlphaGo having a general advantage in terms of search space, it's still possible for the weaker of two strong-but-imperfect players to 'get lucky' and play a move that the stronger player didn't anticipate or account for.
If he didn't anticipate or account for that move, that means his search space wasn't a strict superset. Unless I'm missing something, you're essentially repeating what OP said after his "But: if your opponent isn't a god, it's likely that his search space is NOT the same as yours.".
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.
A (min-max, alpha-beta-pruning) tree search is the optimal way to determine your best move if you can afford to search the situation space globally. However, as that's clearly impossible, there's clever ways to reduce the search space. Random pruning, as a "normal" monte carlo search would do, or semi-random pruning with a neural network estimating the situations, like AlphaGo does.
However, as the search space is now non-global, it might exclude the optimal solution. And thus, the min-max-assumption doesn't hold: your opponent might come up with moves that you didn't think of, screwing your calculations off.
If your opponent is a god ( = can afford global search space), or at least has a search space that is a strict superset of yours, it's "game over, man".
But: if your opponent isn't a god, it's likely that his search space is NOT the same as yours. And you can exploit the fact. If you have any idea what kind of search space your opponent has, you can come up with moves, that he couldn't have included in his tree search, and bet on that his/her "hidden" moves aren't better than yours.
Currently AlphaGo doesn't do that. It behaves like it'd be playing against... well, against another AlphaGo.