From 820fd9329b04cf9b93728f63eda105b40b564a7a Mon Sep 17 00:00:00 2001 From: Petr Baudis Date: Fri, 12 Mar 2010 02:21:41 +0100 Subject: [PATCH] tex: Bibliography --- tex/gostyle.bib | 125 +++++++++++++++++++++++++++++++++++++++++++++++++++++++- tex/gostyle.tex | 12 +++--- 2 files changed, 129 insertions(+), 8 deletions(-) diff --git a/tex/gostyle.bib b/tex/gostyle.bib index 8d82614..55018e7 100644 --- a/tex/gostyle.bib +++ b/tex/gostyle.bib @@ -1,7 +1,7 @@ % This file was created with JabRef 2.6b2. % Encoding: UTF-8 -@MISC{GoR, +@ELECTRONIC{GoR, author = {Ales Cieply and others}, title = {EGF ratings system -- System description}, owner = {pasky}, @@ -9,6 +9,119 @@ url = {http://www.europeangodatabase.eu/EGD/EGF_rating_system.php} } +@ELECTRONIC{ProGoR, + author = {Ales Cieply and others}, + title = {Go Ratings of Professional and Strong Amateur Players}, + owner = {pasky}, + url = {http://www.goweb.cz/progor} +} + +@ELECTRONIC{KGSAnalytics, + author = {Kazuhiro}, + title = {KGS Analytics}, + owner = {pasky}, + url = {http://kgs.gosquares.net/} +} + +@ELECTRONIC{Kombilo, + author = {Ulrich G\"ortz}, + title = {Kombilo --- a go database program}, + owner = {pasky}, + url = {http://www.u-go.net/kombilo/} +} + +@ELECTRONIC{MoyoGo, + author = {Frank de Groot}, + title = {Moyo Go Studio}, + owner = {pasky}, + url = {http://www.moyogo.com/} +} + +@ELECTRONIC{SGF, + author = {Arno Hollosi}, + title = {SGF File Format}, + owner = {pasky}, + url = {http://www.red-bean.com/sgf/} +} + +@ELECTRONIC{GTP, + author = {Gunnar Farneb\"{a}ck}, + title = {GTP --- Go Text Protocol}, + owner = {pasky}, + url = {http://www.lysator.liu.se/~gunnar/gtp/} +} + +@ELECTRONIC{RankComparison, + author = {Sensei's Library}, + title = {Rank --- worldwide comparison}, + owner = {pasky}, + url = {http://senseis.xmp.net/?RankWorldwideComparison} +} + +@ELECTRONIC{GTL, + author = {Jean-loup Gailly, Bill Hosken and others}, + title = {The Go Teaching Ladder}, + owner = {pasky}, + url = {http://gtl.xmp.net/} +} + +@ELECTRONIC{GoGoD, + author = {T. Mark Hall, John Fairbairn}, + title = {Games of Go on Disk --- GoGoD Encyclopaedia and Database}, + owner = {pasky}, + url = {http://www.gogod.co.uk/} +} + +@ELECTRONIC{GoDiscThread, + author = {GoDiscussions}, + title = {Pro Styles (discussion thread)}, + owner = {pasky}, + url = {http://www.godiscussions.com/forum/showthread.php?t=10980} +} + +@ELECTRONIC{Pachi, + author = {Petr Baudi\v{s} and others}, + title = {Pachi --- Simple Go/Baduk/Weiqi Bot}, + owner = {pasky}, + url = {http://repo.or.cz/w/pachi.git} +} + +@ELECTRONIC{GoStyle, + author = {Josef Moud\v{r}\'{i}k, Petr Baudi\v{s} and others}, + title = {GoStyle --- Determine playing style in the game of Go}, + owner = {pasky}, + url = {http://repo.or.cz/w/gostyle.git} +} + +@inproceedings{SpatPat, + author = {Stern, David and Herbrich, Ralf and Graepel, Thore}, + title = {Bayesian pattern ranking for move prediction in the game of Go}, + booktitle = {ICML '06: Proceedings of the 23rd international conference on Machine learning}, + year = {2006}, + isbn = {1-59593-383-2}, + pages = {873--880}, + location = {Pittsburgh, Pennsylvania}, + doi = {http://doi.acm.org/10.1145/1143844.1143954}, + publisher = {ACM}, + address = {New York, NY, USA}, +} + +@inproceedings{PatElo, + title = { {C}omputing {E}lo {R}atings of {M}ove {P}atterns in the {G}ame of {G}o}, + author = {{C}oulom, {R}{\'e}mi}, + abstract = {{M}ove patterns are an essential method to incorporate domain knowledge into {G}o-playing programs. {T}his paper presents a new {B}ayesian technique for supervised learning of such patterns from game records, based on a generalization of {E}lo ratings. {E}ach sample move in the training data is considered as a victory of a team of pattern features. {E}lo ratings of individual pattern features are computed from these victories, and can be used in previously unseen positions to compute a probability distribution over legal moves. {I}n this approach, several pattern features may be combined, without an exponential cost in the number of features. {D}espite a very small number of training games (652), this algorithm outperforms most previous pattern-learning algorithms, both in terms of mean log-evidence (−2.69), and prediction rate (34.9%). {A} 19x19 {M}onte-{C}arlo program improved with these patterns reached the level of the strongest classical programs.}, + language = {{A}nglais}, + affiliation = {{SEQUEL} - {INRIA} {F}uturs - {INRIA} - {CNRS} : {UMR}8022 - {CNRS} : {UMR}8146 - {U}niversit{\'e} des {S}ciences et {T}echnologies de {L}ille - {L}ille {I} - {U}niversit{\'e} {C}harles de {G}aulle - {L}ille {III} - {E}cole {C}entrale de {L}ille }, + booktitle = {{C}omputer {G}ames {W}orkshop }, + address = {{A}msterdam {P}ays-{B}as }, + editor = {van den {H}erik, {H}. {J}aap and {M}ark {W}inands and {J}os {U}iterwijk and {M}aarten {S}chadd }, + audience = {non sp{\'e}cifi{\'e}e }, + year = {2007}, + URL = {http://hal.inria.fr/inria-00149859/en/}, + URL = {http://hal.inria.fr/inria-00149859/PDF/MMGoPatterns.pdf}, +} + note = {{I}.: {C}omputing {M}ethodologies/{I}.2: {ARTIFICIAL} {INTELLIGENCE}/{I}.2.6: {L}earning, {I}.: {C}omputing {M}ethodologies/{I}.2: {ARTIFICIAL} {INTELLIGENCE}/{I}.2.8: {P}roblem {S}olving, {C}ontrol {M}ethods, and {S}earch/{I}.2.8.3: {G}raph and tree search strategies }, + @ARTICLE{CoverHart1967, author = {Thomas M. Cover and Peter E. Hart}, title = {Nearest neighbor pattern classification}, @@ -54,6 +167,15 @@ owner = {hellboy} } +@BOOK{Elo, + title = {The rating of chessplayers, past and present}, + publisher = {Arco, New York}, + year = {1978}, + author = {Arpad E. Elo}, + isbn = {0668047216}, + owner = {pasky} +} + @ELECTRONIC{KohonenPy, author = {lmjohns3}, title = {python-kohonen, {A} library of {Kohonen} maps}, @@ -82,7 +204,6 @@ volume = {42}, number = {1}, pages = {59--66}, - issn = {}, owner = {pasky} } diff --git a/tex/gostyle.tex b/tex/gostyle.tex index fd6f0a4..4b1c753 100644 --- a/tex/gostyle.tex +++ b/tex/gostyle.tex @@ -354,7 +354,7 @@ the internet) and review games played by others on computers as well. This means that large amounts of game records are collected and digitally stored, enabling easy processing of such collections. However, so far only little has been done with the available data --- we are aware -only of uses for simple win/loss statistics \cite{KGSStats} \cite{KGSAnalytics} \cite{ProGoR} +only of uses for simple win/loss statistics \cite{KGSAnalytics} \cite{ProGoR} and ``next move'' statistics on a~specific position \cite{Kombilo} \cite{MoyoGo}. We present a~more in-depth approach --- from all played moves, we devise @@ -402,7 +402,7 @@ not statistically significant. We have chosen an intuitive and simple approach inspired by pattern features used when computing Elo ratings for candidate patterns in Computer Go play. -\cite{Elo} Each pattern is a~combination of several {\em pattern features} +\cite{PatElo} Each pattern is a~combination of several {\em pattern features} (name--value pairs) matched at the position of the played move. We use these features: @@ -420,7 +420,7 @@ The spatial patterns are normalized (using a dictionary) to be always black-to-play and maintain translational and rotational symmetry. Configurations of radius between 2 and 9 in the gridcular metric% \footnote{The {\em gridcular} metric -$d(x,y) = |\delta x| + |\delta y| + \max(|\delta x|, |\delta y|)$ defines +$d(x,y) = |\delta x| + |\delta y| + \max(|\delta x|, |\delta y|)$ produces a circle-like structure on the Go board square grid. \cite{SpatPat} } are matched. @@ -440,7 +440,7 @@ further local and global development. We have implemented the data extraction by making use of the pattern features matching implementation% \footnote{The pattern features matching was developed according -to the Elo-rating playing scheme. \cite{Elo}} +to the Elo-rating playing scheme. \cite{PatElo}} within the Pachi go-playing program \cite{Pachi}. We extract information on players by converting the SGF game records to GTP stream \cite{GTP} that feeds Pachi's {\tt patternscan} @@ -722,7 +722,7 @@ We have implemented the data mining methods as the made available under the GNU GPL licence. The majority of our basic processing and the analysis parts -are implemented in the Python \cite{Python2005} programming language. +are implemented in the Python \cite{Python25} programming language. We use several external libraries, most notably the MDP library \cite{MDP} (used for PCA analysis) and Kohonen library \cite{KohonenPy}. The neural network part of the project is written using the libfann C library\cite{Nissen2003}. @@ -741,7 +741,7 @@ First, we have used our framework to analyse correlations of pattern vectors and playing strength. Like in other competitively played board games, Go players receive real-world {\em rating number} based on tournament games, and {\em rank} based on their rating.% -\footnote{Elo-like rating system \cite{GoR} is usually used, +\footnote{Elo-type rating system \cite{GoR} is usually used, corresponding to even win chances for game of two players with the same rank, and about 2:3 win chance for stronger in case of one rank difference.}% \footnote{Professional ranks and dan ranks in some Asia countries may -- 2.11.4.GIT