4 ms·

Remarkable achievement, much more impressive than Chess, imo.

• BookmarkSaver 11y ago
  Not just in your opinion, Go is objectively much more difficult for computers than Chess.
  • _yosefk 11y ago
    Until it isn't. Once humans are handily beaten in both games, it's kinda hard to tell which game is harder (I guess you could compare the amount of computations needed to beat the top human player, but that does not let you distinguish if it's harder for computers or conversely easier for humans.)
  • taneq 11y ago
    I think being difficult "for computers" is a misleading concept. Go is more difficult for our current algorithms than chess, at least in part because we've focused on chess for much longer. Sooner or later we may discover some new algorithm (this deep-neural-net-based approach may be it) which will reduce Go to be as tractable as chess, or even more so.
    • xcombelle 11y ago
      It was more difficult for computer programmers to program an ai which reach a human top level, and for this level you have to throw much more hardware in it.
• gnarbarian 11y ago
  Not just in your opinion. The search space is exponentially larger in go because you can place a piece on any unoccupied square. "The search space for Go's game tree is both wider and deeper than that of chess. It has been estimated to be as big as 10^170 compared to 10^50 for chess, making the normal brute-force game tree search algorithms much less effective. " http://ai-depot.com/LogicGames/Go-Complexity.html http://ai-depot.com/LogicGames/Go-Complexity.html To provide a little more perspective. The estimated number of atoms in the observable universe is about 4*10^80. So we have no hope of ever being capable of "solving" go by mapping out every possible game state AKA "brute forcing" the game. https://en.m.wikipedia.org/wiki/Shannon_number https://en.m.wikipedia.org/wiki/Shannon_number
  • peter_plassi 11y ago
    I understand that the search space is larger, but how have you determined that it is exponentially larger than in chess?
    • simcop2387 11y ago
      The exponent is larger. That's what that means. It's not a simple doubling (quadratic) or linear increase in search space.
    • gnarbarian 11y ago
      From the linked article above "Some of the factors responsible for the game tree's enormity are shown below. (A comparison to chess is shown in brackets) the size of the board : 19x19 (8x8) the average number of moves per game is around 300 (80) the average branching factor at each turn is around 235 (35) players can pass" It's been 10 years since I did the math for this in my AI class but a simplified example would be to compare the start of each game. a go player has 19^2 or 361 possible moves. then on the second turn there are 360 possible moves (since one space has been taken up by the first move) so after two moves there are 361*360 or 129,960 possible go board configurations. a chess player is more restricted by the smaller size of the board and the way pieces are permitted to move. so there are a total of 16 possible pawn moves on the first turn plus 4 possible knight moves leaving 20 total moves available. On the second turn the second player is similarly resticted to 20 possible moves causing the total number of chess board configurations after two moves to be 400. Once more room opens up on the chess board the branching factor does increase a bit but it never comes close to the hundreds of potential moves at any step during a go game. this "branching factor" is the key to the size of a search space. in chess it gets messy to calculate due to the variety of piece types and their allowable movements but it's easy to see that at any given board configuration there are fewer possible moves. to calculate the search space you multiply the number of potential moves at each step until every board configuration has been accounted for. https://en.wikipedia.org/wiki/Branching_factor https://en.wikipedia.org/wiki/Branching_factor
      • NolF 11y ago