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Alphago Zero has beat all previous Go engines without examining any external games. I don't see any reason to believe Chess is a special game where humans under

Alphago Zero has beat all previous Go engines without examining any external games. I don't see any reason to believe Chess is a special game where humans understand it better (to write AIs) than computers can do "on their own". They are both turn-based deterministic games where the only asymmetry is which player gets the first turn.

Recursing 9y ago
Lots of the parameters of the evaluation function are (at least in part) automatically tuned. Here https://chessprogramming.wikispaces.com/Automated+Tuning https://chessprogramming.wikispaces.com/Automated+Tuning you can fnd some of the most successful methods
dragontamer 9y ago
> without examining any external games That's only a big deal in Go circles, where Go computers have only been super-human for less than 2 years. Chess AIs are well known to have been super-human for decades now. Chess AIs are able to build the Chess game database backwards (ie: Tablebases) AND forwards (Opening Databases). There's very little human knowledge involved in a 6-move Chess Tablebase: https://en.wikipedia.org/wiki/Endgame_tablebase https://en.wikipedia.org/wiki/Endgame_tablebase . Indeed, computer-generation of Tablebases have REFUTED human knowledge, and modern Tablebases are way stronger than the entirety of chess knowledge that existed before computer analysis. Building Chess AIs "without human knowledge" has been going on for a long time now. The thing is, its very difficult to beat the SPEED of an assembly-language tuned count of bishop or rook moves. IE: The Magic Multiply / Bitboard method: https://chessprogramming.wikispaces.com/Magic+Bitboards https://chessprogramming.wikispaces.com/Magic+Bitboards ---------- The Chess AI game is played very differently from the Go AI game. In Go AI, "superhuman" programming techniques are still in its infancy. In Chess AIs, the game is mostly settling down upon speed, speed, SPEED. In many cases, a weaker heuristic that sees +1 or +2 moves into the future (due to being a little bit faster) is far better than a stronger heuristic that takes a bit longer to calculate. As such, a huge amount of Chess AI effort is in optimizing routines at the assembly level. Or perhaps data-structures that allow Opening Databases and Endgame Tablebases to be more efficiently utilized. In contrast, a lot of Go AI effort is in representing search trees... but not so much in speed or optimization right now.

Recursing 9y ago

Lots of the parameters of the evaluation function are (at least in part) automatically tuned. Here https://chessprogramming.wikispaces.com/Automated+Tuning https://chessprogramming.wikispaces.com/Automated+Tuning you can fnd some of the most successful methods

dragontamer 9y ago

> without examining any external games That's only a big deal in Go circles, where Go computers have only been super-human for less than 2 years. Chess AIs are well known to have been super-human for decades now. Chess AIs are able to build the Chess game database backwards (ie: Tablebases) AND forwards (Opening Databases). There's very little human knowledge involved in a 6-move Chess Tablebase: https://en.wikipedia.org/wiki/Endgame_tablebase https://en.wikipedia.org/wiki/Endgame_tablebase . Indeed, computer-generation of Tablebases have REFUTED human knowledge, and modern Tablebases are way stronger than the entirety of chess knowledge that existed before computer analysis. Building Chess AIs "without human knowledge" has been going on for a long time now. The thing is, its very difficult to beat the SPEED of an assembly-language tuned count of bishop or rook moves. IE: The Magic Multiply / Bitboard method: https://chessprogramming.wikispaces.com/Magic+Bitboards https://chessprogramming.wikispaces.com/Magic+Bitboards ---------- The Chess AI game is played very differently from the Go AI game. In Go AI, "superhuman" programming techniques are still in its infancy. In Chess AIs, the game is mostly settling down upon speed, speed, SPEED. In many cases, a weaker heuristic that sees +1 or +2 moves into the future (due to being a little bit faster) is far better than a stronger heuristic that takes a bit longer to calculate. As such, a huge amount of Chess AI effort is in optimizing routines at the assembly level. Or perhaps data-structures that allow Opening Databases and Endgame Tablebases to be more efficiently utilized. In contrast, a lot of Go AI effort is in representing search trees... but not so much in speed or optimization right now.