What I’m curious about is how much this reflects an attempt by AlphaGo to conserve computational resources.
If I understand correctly, at least according to the Nature paper, it doesn’t explicitly optimize for this. Game-playing software is often perceived as playing “conservatively”, this is a general property of minimax search, and in the limit the Nash equilibrium consists of maximally conservative strategies.
but I was still surprised by the amount of thought that went into some of the moves.
Maybe these obvious moves weren’t so obvious at that level.
I don’t know about that level, but I can think of at least one circumstance where I think far longer than would be expected over a forced move. If I’ve worked out the forced sequence in my head and determined that the opponent doesn’t gain anything by it, but they play it anyway, I start thinking “Danger, Danger, they’ve seen something I haven’t and I’d better re-evaluate.”
Most of the time it’s nothing and they just decided to play out the position earlier than I would have. But every so often I discover a flaw in the “forced” defense and have to start scrabbling for an alternative.
This is very true in Go. If you are both playing down a sequence of moves without hesitation, anticipating a payoff, one of you is wrong (kind of. It’s hard to put in words.) It is always worth making double sure that it isn’t you.
Maybe these obvious moves weren’t so obvious at that level.
Sure. And I’m pretty low as amateurs go—what I found surprising was that there were ~6 moves where I thought “obviously play X,” and 이 immediately played X in half of them and spent 2 minutes to play X in the other half of them. It wasn’t clear to me if 이 was precomputing something he would need later, or was worried about something I wasn’t, or so on.
Most of the time I was thinking something like “well, I would play Y, but I’m pretty unconfident that’s the right move” and then 이 or AlphaGo play something that are retrospectively superior to Y, or I was thinking something like “I have only the vaguest sense of what to do in this situation.” So I guess I’m pretty well-calibrated, even if my skill isn’t that great.
If I understand correctly, at least according to the Nature paper, it doesn’t explicitly optimize for this. Game-playing software is often perceived as playing “conservatively”, this is a general property of minimax search, and in the limit the Nash equilibrium consists of maximally conservative strategies.
Maybe these obvious moves weren’t so obvious at that level.
I don’t know about that level, but I can think of at least one circumstance where I think far longer than would be expected over a forced move. If I’ve worked out the forced sequence in my head and determined that the opponent doesn’t gain anything by it, but they play it anyway, I start thinking “Danger, Danger, they’ve seen something I haven’t and I’d better re-evaluate.”
Most of the time it’s nothing and they just decided to play out the position earlier than I would have. But every so often I discover a flaw in the “forced” defense and have to start scrabbling for an alternative.
This is very true in Go. If you are both playing down a sequence of moves without hesitation, anticipating a payoff, one of you is wrong (kind of. It’s hard to put in words.) It is always worth making double sure that it isn’t you.
Sure. And I’m pretty low as amateurs go—what I found surprising was that there were ~6 moves where I thought “obviously play X,” and 이 immediately played X in half of them and spent 2 minutes to play X in the other half of them. It wasn’t clear to me if 이 was precomputing something he would need later, or was worried about something I wasn’t, or so on.
Most of the time I was thinking something like “well, I would play Y, but I’m pretty unconfident that’s the right move” and then 이 or AlphaGo play something that are retrospectively superior to Y, or I was thinking something like “I have only the vaguest sense of what to do in this situation.” So I guess I’m pretty well-calibrated, even if my skill isn’t that great.