Why don't use the same approach for chess?
It's very interesting to see if it is able to handle much more advanced and tuned engines that exist for chess, game with considerable much more complicated rules?
It's very interesting to see if it is able to handle much more advanced and tuned engines that exist for chess, game with considerable much more complicated rules?
And chess, while it does have more complex base rules, has a much lower combinatorial complexity than Go.
The problem with the current chess bots is that they play badly, badly. They choose a terrible random mistake to make every few moves, while some of their other moves are brilliant. They cannot accurately mimic beginner or intermediate level players.
I wouldn't bet on it though. SMP is notoriously hard to work with alpha-beta search and there are a lot of clever tricks (which is probably still not perfect). Maybe with ASICs, you could make it stronger, but then it wouldn't be as fair a comparison.
I'm talking about something similar to the described in the paper, 100% self-learned solution without using human heuristics, based on NNs. That could bring a totally new ideas into chess.
But shogi is much more obscure outside of Japan than go or chess, so it gets less interest, especially in the large-board variants.
But maybe not sexy enough, or we just don't hear about it as much.
Giraffe attempted this (with more standard tree search than MCTS and with only a value function rather than a combined policy/value network), but only reached IM level -- certainly impressive, but nowhere close to Stockfish.
Minimax with Alpha Beta pruning works in Chess because the search tree is way smaller. The reason why all this "Monte-Carlo Tree Search + Neural Nets" are being used in Go because Minimax + Alpha Beta pruning DOESN'T work in Go.