3 ms·

I encountered this many times over the past couple of days haha. I then have to explain that previous AIs, like those in chess largely used brute force computat

I encountered this many times over the past couple of days haha. I then have to explain that previous AIs, like those in chess largely used brute force computation to simulate each move, while these AIs "actually learn, similar to our brain". Probably not the most scientific explanation, but I feel your sentiment.

Scea91 11y ago
There is still tree search going on. However I don't know the details about it. Is it that the deep net works mainly as an evaluation function of the current position? I guess it does more than that right?
- umanwizard 11y ago
  There's also tree search in my brain when I play chess or go. (Consciously!)
- Terribledactyl 11y ago
  They are using Monte Carlo Methods for looking around, more like a tiny sampling, it's an incomprehensibly large space. They have both value and policy (deep) neural nets. Train one to get the sense of good/bad individual board states (value) and one to get the sense of good/bad trajectories (policy, evaluates chains of moves). Chess has a fairly straight forward ranking of pieces, fairly well established ranking of piece power. A knight is more or less always a knight. Go, you kinda make up your "pieces" from scratch and there isn't any single common ranking. This makes the problem of even evaluating the board difficult (value), which is a sub step of making your higher plans (policy). The output of the two dNN help cut off, or prevent the sampling of the bad game states and encourage looking into the fruitful areas. Here is a (edit: not old) new paper, https://gogameguru.com/i/2016/03/deepmind-mastering-go.pdf https://gogameguru.com/i/2016/03/deepmind-mastering-go.pdf
sobellian 11y ago
Well, it's a bit more complicated than that. Neither chess engines nor go engines use brute-force (that is, exhaustive search). Although go does have a much higher branching factor and that does affect the search algorithm used, the biggest challenge is being able to write good evaluation and move-guessing heuristics.

Scea91 11y ago

There is still tree search going on. However I don't know the details about it. Is it that the deep net works mainly as an evaluation function of the current position? I guess it does more than that right?

umanwizard 11y ago
There's also tree search in my brain when I play chess or go. (Consciously!)
Terribledactyl 11y ago
They are using Monte Carlo Methods for looking around, more like a tiny sampling, it's an incomprehensibly large space. They have both value and policy (deep) neural nets. Train one to get the sense of good/bad individual board states (value) and one to get the sense of good/bad trajectories (policy, evaluates chains of moves). Chess has a fairly straight forward ranking of pieces, fairly well established ranking of piece power. A knight is more or less always a knight. Go, you kinda make up your "pieces" from scratch and there isn't any single common ranking. This makes the problem of even evaluating the board difficult (value), which is a sub step of making your higher plans (policy). The output of the two dNN help cut off, or prevent the sampling of the bad game states and encourage looking into the fruitful areas. Here is a (edit: not old) new paper, https://gogameguru.com/i/2016/03/deepmind-mastering-go.pdf https://gogameguru.com/i/2016/03/deepmind-mastering-go.pdf

sobellian 11y ago

Well, it's a bit more complicated than that. Neither chess engines nor go engines use brute-force (that is, exhaustive search). Although go does have a much higher branching factor and that does affect the search algorithm used, the biggest challenge is being able to write good evaluation and move-guessing heuristics.