mhlepore wrote:I recall reading a while back that bots with a lead will sometimes play sub-optimally in the endgame to ensure their win. That is, trade down the expected margin of victory for an increase in probability of victory.
Well, the impression that many people, myself included, have is that top bots, going back to the MCTS bots before AlphaGo, typically win games by smaller margins than an amateur dan typically would, and maybe even weaker humans. The claim has been made in the bots' defense that they give up points in order to secure the win. To my mind, that claim has never been proven. OTOH, I am unaware of anybody coming up with a case where a top bot would have lost a game versus human play because of giving up a few points in the endgame.
There was a case a while back where a top bot lost a point at the end of play by unnecessarily filling in a point of territory, thus losing the game by ½ pt. But that was by territory scoring with a 6½ pt. komi, which is not the game the bot was playing.
The main problem with the defense of the bots, it seems to me is what is meant by a winrate. IIUC, a winrate estimate assumes that the bot is playing against itself. That weakens the defense argument, because a bot could well have a blind spot that it would share with itself as the opponent, but which a different opponent would exploit. The argument then becomes that the bots make objectively suboptimal endgame plays that increase their estimate of the odds of winning the game against a player that makes the same mistakes that it does. Hardly compelling.
We already know that strong amateurs are still better than the bots in certain situations such as those with long ladders and large semeai. Humans are good at depth first search in local situations, local being a fuzzy concept. By contrast today's top bots do a kind of best first search over the whole board. That can put them at a disadvantage versus humans. Because a game of go tends to divide into a number of local situations in the endgame, human play can approach perfection, because depth first local search pays off. There is still the question of which local region to play in, but humans have good heuristics and algorithms for that. Anyway, I doubt that any of today's top bots could solve every problem in Berlekamp and Wolfe's
Mathematical Go, if they were amended for a 7½ pt. komi.
I have not been motivated to look for endgame mistakes by top bots because, well, who cares? And I am not at all sure that top bots of 2018 and 2019 make game losing endgame errors (Edit: simply by making small plays). For instance, I ran across an example in the Elf commentaries where Elf recommended filling a ⅓ pt. ko instead of nailing down the win by filling a larger ko, so that it would not matter whether it won the ⅓ pt. ko or not. What human would play that way? Well, as it turns out, Elf would have won the larger ko, as well, so no harm done.
