|
|
|
|
|
|
by V-2
4213 days ago
|
|
|
My thoughts exactly. As an exercise it's surely interesting, but neural network approach is inherently unsuitable for chess. Chess requires 100% accuracy and in which just because positions are similar, doesn't mean that best moves in these positions have to be in any way similar too. On the other hand, it sort of mimics the way human player thinks, in terms of recognizing certain patterns. After all, even grandmasters do not bruteforce their way through all possible combinations. We use a hybrid approach: recognize certain strategic patterns first (to drastically narrow down the search tree), and perform calculations on the top of that. Chess engines can wipe the floor with any player where tactics is involved; the trick of beating a computer is to close the game and take advantage of the fact that it's not able to formulate a long-term PLAN (whose consequences are beyond its horizon). See how Nakamura repeatedly beat Rybka in blitz games a few years ago, eg.: http://www.chessgames.com/perl/chessgame?gid=1497429 - very instructive :) |
|
|
To alleviate this, one can add more abstract/heuristic information about the position to the input (indicators for complex relations between several pieces). This kind of high-dimensional vector would be more robust to small changes, and make the objective function more smooth. Perhaps the non-linearities introduced by the three layers cannot do this as effectively.