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Adversarial? If the model exclusively trains against itself, you can’t really insert anything there. Do you mean, play confusing moves at the beginning of the g

by bertil 8y ago

Adversarial? If the model exclusively trains against itself, you can’t really insert anything there. Do you mean, play confusing moves at the beginning of the game?

CJefferson 8y ago
I mean, if we had the network, it would be easy to beat, the same way you can confuse image recognition systems with very minor changes.
- IanCal 8y ago
  That doesn't follow. For you to confuse it, you need to change the inputs. For images, this is fine, we can smoothly change lots of little things. For chess games or go you don't have that freedom. You can download the weights for LCZero right now though and try out your theory. https://github.com/LeelaChessZero/lc0/wiki/Getting-Started https://github.com/LeelaChessZero/lc0/wiki/Getting-Started
  - CJefferson 8y ago
    You are right, I should try. I'll see if I can find time in the new year. I'd prefer to try with a go player, because as you say, in chess it's hard to exactly control the input to the network, it's easier in Go.
    - IanCal 8y ago
      Here's a go setup https://github.com/gcp/leela-zero https://github.com/gcp/leela-zero There's current best weights available. Not alphazero, but I would expect that issues would be general and so if there are issues with leela zero they may transfer and if you don't see issues with leela zero they're unlikely to exist in alpha zero (at least, if they do they may be very particular to subtle training differences). Would be very interested to see what you find if you get the chance.
  - kzrdude 8y ago
    You can change the inputs: it depends on when (ply) and which move you play. Some moves are uncommon enough to make it possible for you to uncover something?
    - IanCal 8y ago
      You absolutely can change the inputs, but the point I wanted to make is that unlike images where you can make a human-irrelevant changes you can't really do that with chess or go. If you want to construct a particular position on the board, you'd likely need to use multiple steps, require the AI to play very particular moves and then the outcome would be a certain move from the AI. Even then, a simple incorrect classification doesn't help all that much, you need your opponent to make repeated mistakes. I think in reality if you uncovered a type of move it wasn't expecting you are likely to uncover a new strategy in general rather than a trick. Image classification however lets you play uninterrupted with tiny pixel value changes, and you only need a single incorrect output to "win".
- thom 8y ago
  It's suspect it's a bit harder for the network to be overfit like this, but it's probably possible it has some gaps in its evaluation. However, those gaps would have to persist beyond its search horizon and not concretely affect material or mobility and it just seems vanishingly unlikely you'll find any systematic way to exploit anything.
- bko 8y ago
  The way general adversarial networks work on tricking image recognition systems is that they vary pixels of the input image slightly to manipulate the output of the neural network. For alphazero, the input is the board, which you can't manipulate arbitrarily. You can run an evaluation of a board based on a move and see if its significantly different than the evaluation that alphazero comes up with, and maybe try to exploit that. But if you have a better evaluation of some state than that of alphazero, you're likely a stronger player anyway so this extra step is unnecessary. Most of the value of the bot comes from the evaluation function of a board, along with some hyper-parameters. But the evaluation is probably the most important part and the most difficult to replicate.

by bertil 8y ago

Adversarial? If the model exclusively trains against itself, you can’t really insert anything there. Do you mean, play confusing moves at the beginning of the game?

CJefferson 8y ago

I mean, if we had the network, it would be easy to beat, the same way you can confuse image recognition systems with very minor changes.

IanCal 8y ago
That doesn't follow. For you to confuse it, you need to change the inputs. For images, this is fine, we can smoothly change lots of little things. For chess games or go you don't have that freedom. You can download the weights for LCZero right now though and try out your theory. https://github.com/LeelaChessZero/lc0/wiki/Getting-Started https://github.com/LeelaChessZero/lc0/wiki/Getting-Started
- CJefferson 8y ago
  You are right, I should try. I'll see if I can find time in the new year. I'd prefer to try with a go player, because as you say, in chess it's hard to exactly control the input to the network, it's easier in Go.
  - IanCal 8y ago
    Here's a go setup https://github.com/gcp/leela-zero https://github.com/gcp/leela-zero There's current best weights available. Not alphazero, but I would expect that issues would be general and so if there are issues with leela zero they may transfer and if you don't see issues with leela zero they're unlikely to exist in alpha zero (at least, if they do they may be very particular to subtle training differences). Would be very interested to see what you find if you get the chance.
- kzrdude 8y ago
  You can change the inputs: it depends on when (ply) and which move you play. Some moves are uncommon enough to make it possible for you to uncover something?
  - IanCal 8y ago
    You absolutely can change the inputs, but the point I wanted to make is that unlike images where you can make a human-irrelevant changes you can't really do that with chess or go. If you want to construct a particular position on the board, you'd likely need to use multiple steps, require the AI to play very particular moves and then the outcome would be a certain move from the AI. Even then, a simple incorrect classification doesn't help all that much, you need your opponent to make repeated mistakes. I think in reality if you uncovered a type of move it wasn't expecting you are likely to uncover a new strategy in general rather than a trick. Image classification however lets you play uninterrupted with tiny pixel value changes, and you only need a single incorrect output to "win".
thom 8y ago
It's suspect it's a bit harder for the network to be overfit like this, but it's probably possible it has some gaps in its evaluation. However, those gaps would have to persist beyond its search horizon and not concretely affect material or mobility and it just seems vanishingly unlikely you'll find any systematic way to exploit anything.
bko 8y ago
The way general adversarial networks work on tricking image recognition systems is that they vary pixels of the input image slightly to manipulate the output of the neural network. For alphazero, the input is the board, which you can't manipulate arbitrarily. You can run an evaluation of a board based on a move and see if its significantly different than the evaluation that alphazero comes up with, and maybe try to exploit that. But if you have a better evaluation of some state than that of alphazero, you're likely a stronger player anyway so this extra step is unnecessary. Most of the value of the bot comes from the evaluation function of a board, along with some hyper-parameters. But the evaluation is probably the most important part and the most difficult to replicate.