Can MCTS dynamically determine that it needs to analyze a certain line to a much higher depth than normal due to the specifics of the situation?
That’s the type of flexible reflection that is needed. I think most people would agree that the hard-coded ladder solver in Katago is not ideal, and feels like a dirty hack. The system should learn when it needs to do special analysis, not have us tell it when to. It’s good that it works, but it’d be better if it didn’t need us to hard-code such knowledge.
Humans are capable of realizing what a ladder is on their own (even if many learn from external sources). And it definitely isn’t hard-coded into us :)
Traditional MCTS analyzes each line all the way to endgame.
I believe neural-net based MCTS (ex: AlphaZero and similar) use the neural-net to determine how deep any line should go. (Ex: which moves are worth exploring? Well, might as well have that itself part of the training / inference neural net).
In my understanding, in KataGo, the decision of how long to follow a line is made solely by MCTS via its exploration/exploitation components. These in turn are influence by the policy/value outputs of the DCNN. So in practical terms, your statement might just be called true.
The raw net output includes some values that could be used in addition, but they are not used. I don't know if they were ever looked at closely for this purpose.
That’s the type of flexible reflection that is needed. I think most people would agree that the hard-coded ladder solver in Katago is not ideal, and feels like a dirty hack. The system should learn when it needs to do special analysis, not have us tell it when to. It’s good that it works, but it’d be better if it didn’t need us to hard-code such knowledge.
Humans are capable of realizing what a ladder is on their own (even if many learn from external sources). And it definitely isn’t hard-coded into us :)