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0 pointscupofpython3y ago0 comments

you're missing the point of my post. yes the computer prunes, but fundamentally it is attempting to review all possible scenarios indiscriminately as opposed to a human who is trying to make some sort of sense of the position. without work, as in developing an intuition for making sense of the game, a human looking ahead doesnt provide value.

so im not sure why you think i was trying to say humans cant move pieces around in their mind.

you also completely ignored that i was mainly addressing the scope difference of positions analyzed by a human player vs a computer, and that the talking point was someone looking "64 moves ahead" and trying to explain that no this guy did not literally see all variations 64 moves out - but that through (arguably more impressive) reasonable human ability he was able to to be accurate and confident in a decision that he had won while there was still 64 moves of depth left in the game

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thaumasiotes3y ago

> yes the computer prunes, but fundamentally it is attempting to review all possible scenarios indiscriminately as opposed to a human who is trying to make some sort of sense of the position.

No, pruning is by far the majority of the work the computer does. It is very much not trying to review all possible scenarios indiscriminately, because that can't be done, because there are too many possible scenarios.

And "pruning" and "making sense of the position" refer to the same thing. Interpreting the position is how you prune.

kcexn3y ago

I think what many people in this thread are trying to stumble over is that the way a computer prunes moves and the way humans prune moves is qualitatively different.

A computer prunes its moves from either an explicit or implicit (implicit when it's say a trained neural net) database of known positions, with some quantitative sense of strength (usually a probability to win or something like that).

A human needs to assign a narrative to particular branching pathways. These are qualitative instead of quantitative assessments.

A human isn't saying, if I make a certain move there is an 85% chance of winning, and so that makes it my best bet. They're assigning arbitrary structures and narratives to positions, hence why many positions, tactics, and strategies in chess and other games are given colourful names.

The two approaches are very different and have different strengths and weaknesses. Which is why the best play outcome is to combine the computer generated moves with the human generated moves.

The human approach is very good at generalising new information very quickly. Assigning unusual or unfamiliar information in a broader qualitative framework about what good play looks like, think about players who are trying to create certain structures, shapes and patterns on the board.

The computer is very good at applying knowledge about individual moves at great depths. But cannot combine it with any external information. All information about the success rates of moves are determined from the database of all past moves. The computer can't condition those probabilities on things like, does my opponent need to win, or only draw. Do they have a tendency to be aggressive or defensive. Probabilities of success only make sense when taking a population view of the computers input data (a literally impossible task if your talking about the kinds of neural nets used in chess).

So a hybrid approach lets good players condition computer generated moves based on external information. Maybe the computer generates a line of play with 80% confidence of winning, but the human can see that because of certain qualitative structures on the board, the opposing player is more likely to see the solution than the computers population, and so can recondition the lines of play on this new information, even if the human has no idea why the line of play should work 80% of the time. Lines of play that would otherwise have very similar success rates (differing by only a few percentage points say) can be re-ordered based on human judgement.

Both the computer and the human can tell obviously bad from obviously good moves. But their approach is very different when nuance is required.

cupofpythonOP3y ago

thank you, this is pretty much what I was trying to say. Especially the narratives part. there are plenty of instances of humans finding winning moves in positions that the computer did not 'see' the advantage of until after it was played. That doesnt happen because the human saw more moves ahead than the computer. It happens based on work done building a generalized intuition about the game itself which i would argue extends beyond what would be expected from "pruning" algorithms.

If a computer could assess every single move, it would. A human would still prefer to rely on narratives and game sense if it's good enough

kcexn3y ago

I'd argue that if the human sees a line that the computer didn't see any advantage of at all, then the engine is just wrong.

The more likely outcome if the engine is 'correct' is that it sees the line but thinks an alternative one offers a much greater advantage.

The computer can't condition this information on what you or the opponent is likely to do though. For example, there are lines of play that an engine can generate where you can checkmate in 60+ (even examples where the number goes into the hundreds) moves but only if you play every move absolutely perfectly, this kind of strategy is very brittle, a human might make adjustments to preserve the general idea of the line of play but make it more robust to error. The engine might also generate lines of play that have one or two flaws, but the engine thinks it's very unlikely that the opponent will find those flaws, because the population of games in it's database tells it that very few opponents see them. As the human you might see that your opponent is taking a certain line of play to try and get some sort of positional advantage, and that they are more likely to see the flaw in the engines line of play because the goals are in direct opposition to each other, in this case you would not choose this line because the computer is unable to condition its lines of play on the quality of your opponent.

IMO this is the fundamental reason chess masters around the world don't feel threatened by the computers yet. The way computers play chess relies on past information, often this past information is generated by humans. Humans are also able to generalise the insights that engines can find creating more robust strategies that are hard for engines to beat, until the engine adds it to the database.

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thaumasiotes3y ago

> yes the computer prunes, but fundamentally it is attempting to review all possible scenarios indiscriminately as opposed to a human who is trying to make some sort of sense of the position.

And "pruning" and "making sense of the position" refer to the same thing. Interpreting the position is how you prune.

kcexn3y ago

I think what many people in this thread are trying to stumble over is that the way a computer prunes moves and the way humans prune moves is qualitatively different.

A human needs to assign a narrative to particular branching pathways. These are qualitative instead of quantitative assessments.

The two approaches are very different and have different strengths and weaknesses. Which is why the best play outcome is to combine the computer generated moves with the human generated moves.

Both the computer and the human can tell obviously bad from obviously good moves. But their approach is very different when nuance is required.

cupofpythonOP3y ago

If a computer could assess every single move, it would. A human would still prefer to rely on narratives and game sense if it's good enough

kcexn3y ago

I'd argue that if the human sees a line that the computer didn't see any advantage of at all, then the engine is just wrong.

The more likely outcome if the engine is 'correct' is that it sees the line but thinks an alternative one offers a much greater advantage.

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