[SkiFire13]

> One other thing to take into consideration, is that to play the game of Go you can't just think of the next move. You have to think far forward in the game -- even though technically all it's doing is picking the next move, it is doing so using a model that has obviously looked forward more than just one move.

It doesn't necessarily have to look ahead. Since Go is a deterministic game there is always a best move (or moves that are better than others) and hence a function that goes from the state of the game to the best move. We just don't have a way to compute this function, but it exists. And that function doesn't need the concept of lookahead, that's just an intuitive way of how could find some of its values. Likewise ML algorithms don't necessarily need lookahead, they can just try to approximate that function with enough precision by exploiting patterns in it. And that's why we can still craft puzzles that some AIs can't solve but humans can, by exploiting edge cases in that function that the ML algorithm didn't notice but are solvable with understanding of the game.

The thing is though, does this really matter if eventually we won't be able to notice the difference?

[bytefactory]

> It doesn't necessarily have to look ahead. Since Go is a deterministic game there is always a best move

Is there really a difference between the two? If a certain move shapes the opponent's remaining possible moves into a smaller subset, hasn't AlphaGo "looked ahead"? In other words, when humans strategize and predict what happens in the real world, aren't they doing the same thing?

I suppose you could argue that humans also include additional world models in their planning, but it's not clear to me that these models are missing and impossible for machine learning models to generate during training.

[SkiFire13]

> If a certain move shapes the opponent's remaining possible moves into a smaller subset, hasn't AlphaGo "looked ahead"?

You're confusing the reason why a move is good with how you can find that move. Yeah, a move is good due to how it shapes the opponent remaining moves, and this is also the reasoning we make in order to find that move, but it doesn't mean you can only find that move by doing that reasoning. You could have found that move just by randomly picking one, it's not very probably but it's possible. AIs just try to maximize such probability of picking a good move, meanwhile we try to find a reason a move is good. IMO it doesn't make sense to try to fit the way AI do this into our mental model, since the middle goal is fundamentally different.

[xcv123]

> Since Go is a deterministic game there is always a best move

The rules of the game are deterministic, but you may be going a step too far with that claim.

Is the game deterministic when your opponent is non-deterministic?

Is there an optimal move for any board state given that various opponents have varying strategies? What may be the best move against one opponent may not be the best move against another opponent.

[SkiFire13]

Maybe "deterministic" is not the correct term here. What I meant is that there's no probability or unknown in the game, so you can always know what are the possible moves and the relative new state.

The opponent's moves may be considered non-deterministic, but you can just assume the worst case for you, that is the best case for the opponent, which is the opponent will always play the best move too.

[user_named]

At every point in time there are a range of moves with different levels of optimality. That range changes at the next point in time following the opponent's move.

[xcv123]

The opponents strategy is an unknown variable not determined by the current board state.

Therefore the best move cannot be determined by the current board state, as it cannot be determined in isolation from the opponents strategy.

[janalsncm]

The optimal strategy can be determined from the current state. This is the principle behind minimax.

In a perfect information zero sum game, we can theoretically draw a complete game tree, each terminal node ending with a win, loss, or draw. With a full understanding of the game tree we can make moves to minimize our opponent’s best move.

[xcv123]

I stand corrected. Thanks for that explanation.