Seems like we need a short introduction too: "Go is a board game played on a rectangular grid, usually 19x19. Pieces (or stones) are placed alternately by the black and white players. Pieces are played onto empty vertexes with the aim of surrounding and capturing the opponents pieces. The game continues until both players pass. Go is scored on territory---essentially whoever has the most territory wins. See http://senseis.xmp.net/?BasicRulesOfGo for a more complete but informal introduction.
This task aims to solve the game of go using Monte Carlo simulation, playing many random games to determine the best next move. For an introduction to Monte Carlo simulation See http://senseis.xmp.net/?MonteCarloTreeSearch or http://en.wikipedia.org/wiki/Monte_Carlo_method This task uses a simple simulation and somewhat simplified interpretation of the rules for the sake of ease of implementation." You also need to explain the following terms you use without explanation: komi, play-outs, gtp, genmove, ko, superko. explanation by reference to a good beginner page on http://senseis.xmp.net/ or http://en.wikipedia.org/wiki/ should do cheers stuart On Tue, Oct 14, 2008 at 12:14 PM, Don Dailey <[EMAIL PROTECTED]> wrote: > I made a reference bot and I want someone(s) to help me check it out > with equivalent data from their own program. There are no guarantees > that I have this correct of course. > > Doing 1 million play-outs from the opening position I get the following > numbers for various komi: > > playouts: 1,000,000 > komi: 5.5 > moves: 111,030,705 > score: 0.445677 > > playouts: 1,000,000 > komi: 6.0 > moves: 111,066,273 > score: 0.446729 > > playouts: 1,000,000 > komi: 6.5 > moves: 111,040,546 > score: 0.447138 > > playouts: 1,000,000 > komi: 7.0 > moves: 111,029,204 > score: 0.4333795 > > playouts: 1,000,000 > komi: 7.5 > moves: 111,047,843 > score: 0.421281 > > (I also get a score of 0.524478 for 0.0 komi) > > Score is from blacks point of view. Score is not the score of the > best move of course but the combined average score of all 1 million > play-outs using the stated komi and ranges from zero to one. > > I am going to build a test harness to compare multiple bots side by > side using gtp commands. I made up two private gtp commands to > facilitate this: > > ref-nodes -> return total moves executed in play-outs > (including both pass moves at end of each > play-out.) > > ref-score -> return total win fraction for black. > > NOTE: both commands report stats from last given genmove search. > > > > I hope to get peoples opinion on the following implementation > specification. I'm definitely not a writer, so I need to know if this > very informal spec is enough at least for experienced MC bot authors > or where there are still some ambiguous points. > > > I'm using the following implementation specification: > > ----[ bot implementation specification ]---- > > This is an informal implementation specification document for > writing a simple Monte Carlo Bot program. The idea is to build a bot > like this in ANY language and test it for performance (and > conformity.) Can be used as a general language benchmark but is as much > about the implementation as the language. This specification assumes > some knowledge of go and Monte Carlo go programs. (If you don't like > it, please write a better one for me!) > > > > 1. Must be able to play complete games for comprehensive conformity > testing. > > 2. In the play-out phase, the moves must be chosen in a "uniformly > random" way between legal moves that do not fill 1 point eyes and > obey the simple-ko restriction. > > When a move in the play-out is not possible, a pass is given. > > 3. Play-outs stop after 2 consecutive pass moves, OR when N*N*3 > moves have been completed, except that at least 1 move gets tried > where N is the size of the board. So if the board is 9x9 then > the game is stopped after 9*9*3 = 81*3 = 243 move assuming at > least one move has been tried in the play-outs. > > 4. A 1 point eye is an empty point surrounded by friendly stones > for the side to move. Additionally, we have 2 cases. If the > stone is NOT on any edge (where the corner is an edge) there > must be no more than one diagonal enemy stone. If the point in > question is on the edge, there must be NO diagonal enemy stones. > > 5. Scoring is Chinese scoring. When a play-out completes, the > score is taken accounting for komi and statistics are kept. > > 6. Scoring for game play uses AMAF - all moves as first. In the > play-outs, statistics are taken on moves played during the > play-outs. Statistics are taken only on moves that are played by > the side to move, and only if the move in question is being > played for the first time in the play-out (by either side.) A > win/loss record is kept for these moves. > > 7. The move with the highest statistical win rate is the one > selected for move in the actual game. In the case of moves with > even scores the choice is randomly made between them. > > 8. Pass move are never selected as the final move to play unless no > other non-eye filling move is possible. > > 9. Random number generator is unspecified - your program should > simply pass the "black box" test and possible an optional > additional test which consists of long matches between other > known conforming bots. Your program should score close to 50% > against other "properly implemented" programs. > > 10. Suicide not allowed in the play-outs or in games it plays. > > 11. When selecting moves to play in the actual game (not play-outs) > positional superko is checked and forbidden. > > 12. If stats for a move was never seen in the play-outs, (has a count > of zero) it is ignored for move selection. > > > _______________________________________________ > computer-go mailing list > computer-go@computer-go.org > http://www.computer-go.org/mailman/listinfo/computer-go/ > _______________________________________________ computer-go mailing list computer-go@computer-go.org http://www.computer-go.org/mailman/listinfo/computer-go/
