I think the problem is that what you meant is very different from what you actually wrote.
Practical implementations that need to achieve good but not perfect play are content to stop at a point before that.
Indeed, but that's not the basic minimax algorithm (which is what I was discussing) and the branching factor of go makes classic minimax infeasible even if you modify it to be forward-looking. Basic minimax is not good for Go. At all. You have to combine it with other techniques for it to be even vaguely effective. You know this, but didn't mention it. So how is anyone supposed to infer that you know it?
I can see now that you have a decent understanding of the actual solution, but that's not at all what you wrote above.
For example, you said to evaluate a game board you need to 'play the game through to the end' -- which hides all that complexity of monte-carlo tree evaluation, etc. Read what you wrote again from the point of view of someone who isn't yourself.
Simply saying 'Play the game through to the end' by itself is neither correct nor accurate. Because that implies evaluating the rest of the game to its conclusion. i.e. every board state. You omitted any other detail or reference. So I descibed how and why it's not feasible (which you now discount as going off at a tangent). You gave no prior indication of understanding this.
Another example: in a previous comment I described how backward looking techniques like the minimax algorithm are not feasible for go, and you said:
"That's exactly how AlphaGo works though".
But it isn't at all:
Classic minimax is backward-looking (starts at end states and works backwards -- this is what I was talking about, and what I described);
AlphaGo (and the paper you linked to) is forward-looking (starts at current root and works forwards).
Classic minimax is full-tree evaluation; AlphaGo is partially-evaluating.
Minimax is deterministic; AlphaGo is stochastic/random sampling.
Minimax uses a simple single-value single-direction evaluation function; AlphaGo uses a complex bi-directional multi-value evaluation.
I could go on.
Read any treatise or any introduction to AI for Go and pretty much the first chapter will be describing how classic minimax is not suitable without heavy modification.
AlphaGo uses some of the same concepts, but applied very differently, as you appear to already know. Yet after I mentioned minimax (which is well-known as an infeasible algorithm for go), you responded:
"That's exactly how AlphaGo works though".
Which is not at all true.
And then you link to a paper that describes precisely how it's not how it works, and why minimax-related techniques alone don't work, in stark contradiction to yourself.
I'm not intentionally misinterpreting what you wrote: I'm taking a reasonable interpretation of what you actually wrote, which appears to show a lack of understanding on your part. I don't think there is a lack of understanding, now, but it's hard to come to a different conclusion based on what you wrote before.
Practical implementations that need to achieve good but not perfect play are content to stop at a point before that.
Indeed, but that's not the basic minimax algorithm (which is what I was discussing) and the branching factor of go makes classic minimax infeasible even if you modify it to be forward-looking. Basic minimax is not good for Go. At all. You have to combine it with other techniques for it to be even vaguely effective. You know this, but didn't mention it. So how is anyone supposed to infer that you know it?
I can see now that you have a decent understanding of the actual solution, but that's not at all what you wrote above.
For example, you said to evaluate a game board you need to 'play the game through to the end' -- which hides all that complexity of monte-carlo tree evaluation, etc. Read what you wrote again from the point of view of someone who isn't yourself.
Simply saying 'Play the game through to the end' by itself is neither correct nor accurate. Because that implies evaluating the rest of the game to its conclusion. i.e. every board state. You omitted any other detail or reference. So I descibed how and why it's not feasible (which you now discount as going off at a tangent). You gave no prior indication of understanding this.
Another example: in a previous comment I described how backward looking techniques like the minimax algorithm are not feasible for go, and you said:
"That's exactly how AlphaGo works though".
But it isn't at all:
Classic minimax is backward-looking (starts at end states and works backwards -- this is what I was talking about, and what I described); AlphaGo (and the paper you linked to) is forward-looking (starts at current root and works forwards).
Classic minimax is full-tree evaluation; AlphaGo is partially-evaluating.
Minimax is deterministic; AlphaGo is stochastic/random sampling.
Minimax uses a simple single-value single-direction evaluation function; AlphaGo uses a complex bi-directional multi-value evaluation.
I could go on.
Read any treatise or any introduction to AI for Go and pretty much the first chapter will be describing how classic minimax is not suitable without heavy modification.
e.g. https://www.youtube.com/watch?v=5oXyibEgJr0
AlphaGo uses some of the same concepts, but applied very differently, as you appear to already know. Yet after I mentioned minimax (which is well-known as an infeasible algorithm for go), you responded:
"That's exactly how AlphaGo works though".
Which is not at all true.
And then you link to a paper that describes precisely how it's not how it works, and why minimax-related techniques alone don't work, in stark contradiction to yourself.
I'm not intentionally misinterpreting what you wrote: I'm taking a reasonable interpretation of what you actually wrote, which appears to show a lack of understanding on your part. I don't think there is a lack of understanding, now, but it's hard to come to a different conclusion based on what you wrote before.