Is this accurate? Does MasterP also use this style? Are there humans that can play this way?
(I'm asking you because you seem to know what you are talking about here.)
What alphago showed however, is that once in the lead it made mistakes: small mistakes that didnt jeopardize the game, but lost the lead. If all paths lead to rome, it doesnt matter which is shorter. Humans however, always think of the best after safety.
What was terribly cruel to see as a Go player was the computer playing poorly early on: showing that it already knew it was going to win.
Humans using the engine don't like this however, and so the authors build in heuristics to make it play more "humanly". Things like using scores for positions, rather than "chance of winning" is also largely for the sake of the users.
Humans do the same thing, playing conservatively in a situation where they're far enough ahead. The difference is that a human sometimes looks at a move and says "This move works, and gains points. There is no risk." For a bot using MCTS, everything is a probability.
However, you are correct insofar that it doesn't care about winning by large margins, it prefers winning by smaller margins if it can achieve this with a higher win probability.
Is this not done in go?
To become a pro you have to go through insane levels of competitions, you need to be strong, not find a weakness in the 100's of players you will face to just have a shot to become the lowest level of professional.
No matter how I've played against it to get to where we are, it'll play the same from that point on. It won't identify me as a risky player from my pattern, nor will it try and classify me as "unpredictable" in some way. It'll play each move as though it has sat down at an already in-progress game between two random opponents.
> It's possible for an AI to evaluate a move based solely on the resulting board position, but it wouldn't be very good. Pretty much all AIs play many turns out to see if the move is any good.
I would strongly argue that these are identical situations. Playing out scenarios in your head but taking into account no history is the same as "evaluating a move based solely on the board position".
In the end of a well-played game the losing player will find that they perhaps get more points than they expect, but each time they win a tradeoff with a slight margin the position on the board becomes much more solid and fixed than it ought to be. A 10 point lead with a large variance consolidates toward an unfaltering 1/2 point lead.
Indeed, commentary on Master (P)s games seemed to suggest this was exactly how endgames went.
Early in the game, confidence in winning is usually correlated exactly with point margin... but at the same time, score in the early parts of the game is very hard to estimate. It's repeatedly noted that AlphaGo plays a very "influence oriented" game which means that it eschews confidently having many points for having lots of "power" on the board which will later translate to points.
So, all together I'd say that AlphaGo plays very well here and doesn't have too much of an obvious computational bias.
The one true oddity is once AlphaGo's internal win probability reaches 100% it starts playing idiotic moves. The reason is simple: it's just searching for moves which have the highest expected win rate and at this point nothing it can do is bad. It won't lose points or anything, but instead just plays moves that are obviously pointless.