From c4a4c22db0cb8d2009f39158960651ae88495da2 Mon Sep 17 00:00:00 2001 From: Petr Baudis Date: Thu, 11 Mar 2010 21:50:47 +0100 Subject: [PATCH] tex: Style Estimator cleanup I --- tex/gostyle.tex | 63 ++++++++++++++++++++++++++++++++------------------------- 1 file changed, 36 insertions(+), 27 deletions(-) diff --git a/tex/gostyle.tex b/tex/gostyle.tex index 2632628..1a9692f 100644 --- a/tex/gostyle.tex +++ b/tex/gostyle.tex @@ -242,8 +242,8 @@ % The paper headers -\markboth{Transactions on Computational Intelligence and AI in Games}% -{On Pattern Feature Trends in Large Go Game Corpus} +\markboth{Transactions on Computational Intelligence and AI in Games --- DRAFT}% +{On Pattern Feature Trends in Large Go Game Corpus --- DRAFT} % The only time the second header will appear is for the odd numbered pages % after the title page when using the twoside option. % @@ -473,7 +473,7 @@ allowing for visualization of clusters of players with similar properties. Furthermore, we use two \emph{classification} methods that assign -each pattern vector $\vec P$ an \emph{output vector $\vec O$, +each pattern vector $\vec P$ an \emph{output vector} $\vec O$, representing e.g.~information about styles, player's strength or even meta-information like the player's era or a country of origin. Initially, the methods must be calibrated (trained) on some prior knowledge, @@ -790,16 +790,18 @@ on average. \section{Style Estimator} -As a~second case study for our pattern analysis, we investigate pattern vectors $\vec p$ -of various well-known players, their relationships and correlations to prior -knowledge to explore its correlaction with extracted patterns. We look for -relationships between pattern vectors and perceived ``playing style'' and -attempt to use our classifiers to transform pattern vector $\vec p$ to style vector $\vec s$. +As a~second case study for our pattern analysis, +we investigate pattern vectors $\vec p$ of various well-known players, +their relationships in-between and to prior knowledge +in order to explore the correlation of prior knowledge with extracted patterns. +We look for relationships between pattern vectors and perceived +``playing style'' and attempt to use our classifiers to transform +pattern vector $\vec p$ to style vector $\vec s$. The source game collection is GoGoD Winter 2008 \cite{GoGoD} containing 55000 professional games, dating from the early Go history 1500 years ago to the present. We consider only games of a small subset of players (fig. \ref{fig:style_marks}); -we have chosen these for being well-known within the players community, +we have chosen them for being well-known within the players community, having large number of played games in our collection and not playing too long ago.\footnote{Over time, many commonly used sequences get altered, adopted and dismissed; usual playing conditions can also differ significantly.} @@ -808,20 +810,22 @@ dismissed; usual playing conditions can also differ significantly.} \label{style-vectors} In order to provide a reference frame for our style analysis, we have gathered some expert-based information about various -traditionally perceived style aspects. -This expert-based knowledge allows us to predict styles of unknown players based on -the similarity of their pattern vectors, as well as discover correlations between -styles and proportions of played patterns. +traditionally perceived style aspects to use as a prior knowledge. +This expert-based knowledge allows us to predict styles of unknown players +based on the similarity of their pattern vectors, +as well as discover correlations between styles and proportions +of played patterns. Experts were asked to mark each style aspect of the given players on the scale from 1 to 10. The style aspects are defined as shown: -%\vspace{4mm} -%\noindent -\begin{table} +\vspace{4mm} +\noindent +%\begin{table} \begin{center} -\caption{Styles} +%\caption{Styles} \begin{tabular}{|c|c|c|} +\multicolumn{3}{|c|}{Styles} \\ \hline \hline Style & 1 & 10\\ \hline Territoriality $\tau$ & Moyo & Territory \\ @@ -830,13 +834,18 @@ Aggressivity $\alpha$ & Calm & Figting \\ Thickness $\theta$ & Safe & Shinogi \\ \hline \end{tabular} \end{center} -\end{table} -%\vspace{4mm} +%\end{table} +\vspace{4mm} -Averaging this expert based evaluation yields -\emph{reference style vector} $\vec s_r$ (of dimension $4$) for each player $r$ +Averaging this expert based evaluation yields \emph{reference style vector} +$\vec s_r$ (of dimension $4$) for each player $r$ from the set of \emph{reference players} $R$. +Throughout our research, we have experimentally found that playing era +is also a major factor differentiating between patterns. Thus, we have +further extended the $\vec s_r$ by median year over all games played +by the player. + \begin{table}[!t] % increase table row spacing, adjust to taste \renewcommand{\arraystretch}{1.3} @@ -891,7 +900,7 @@ Sakata Eio & $7.6 \pm 1.7$ & $4.6 \pm 0.5$ & $7.3 \pm 0.9$ & $8.0 \pm Fujisawa Hideyuki & $3.5 \pm 0.5$ & $9.0 \pm 1.0$ & $7.0 \pm 0.0$ & $4.0 \pm 0.0$ \\ Otake Hideo & $4.3 \pm 0.5$ & $3.0 \pm 0.0$ & $4.6 \pm 1.2$ & $3.6 \pm 0.9$ \\ Kato Masao & $2.5 \pm 0.5$ & $4.5 \pm 1.5$ & $9.5 \pm 0.5$ & $4.0 \pm 0.0$ \\ -Takemiya Masaki & $1.3 \pm 0.5$ & $6.3 \pm 2.1$ & $7.0 \pm 0.8$ & $1.3 \pm 0.5$ \\ +Takemiya Masaki\tnote{4}&$1.3\pm 0.5$& $6.3 \pm 2.1$ & $7.0 \pm 0.8$ & $1.3 \pm 0.5$ \\ Kobayashi Koichi & $9.0 \pm 1.0$ & $2.5 \pm 0.5$ & $2.5 \pm 0.5$ & $5.5 \pm 0.5$ \\ Cho Chikun & $9.0 \pm 0.8$ & $7.6 \pm 0.9$ & $6.6 \pm 1.2$ & $9.0 \pm 0.8$ \\ Ma Xiaochun & $8.0 \pm 2.2$ & $6.3 \pm 0.5$ & $5.6 \pm 1.9$ & $8.0 \pm 0.8$ \\ @@ -910,11 +919,11 @@ Chen Yaoye & $6.0 \pm 1.0$ & $4.0 \pm 1.0$ & $6.0 \pm 1.0$ & $5.5 \pm \hline \end{tabular} \begin{tablenotes} -\item [1] Including standard deviation. Only players where we got at least two out of tree answers are included. -\item [2] We consider era as one of factors when finding correlations with pattern vectors; we quantify era by taking median year over all games played by the player. Since this quantity does not fit to the table, we at least sort the players ascending by their median year. -\item [3] We do not consider games of Go Seigen due to him playing across several distinct Go-playing eras and thus specifically high diversity of patterns. -\item [4] We do not consider games of Ishida Yoshio and Yamashita Keigo for the PCA analysis since they are significant outliers, making high-order dimensions much like purely ``similarity to this player''. Takemiya Masaki has the similar effect for the first dimension, but this corresponds to common knowledge of him being an extreme proponent of anti-territorial (``moyo'') style. -\item [5] We consider games only up to year 2004, since Yi Ch'ang-ho was prominent representative of a balanced, careful player until then, but is regarded to have altered his style significantly afterwards. +\item [1] Including standard deviation. Only players where we received at least two out of three answers are included. +\item [2] Since the playing era column does not fit into the table, we at least sort the players ascending by their median year. +\item [3] We do not consider games of Go Seigen due to him playing across several distinct eras and also being famous for radical opening experiments throughout the time, and thus featuring especially high diversity in patterns. +\item [4] We do not consider games of Ishida Yoshio and Yamashita Keigo for the PCA analysis since they are significant outliers, making high-order dimensions much like purely ``similarity to this player''. Takemiya Masaki has the similar effect for the first dimension, but that case corresponds to common knowledge of him being an extreme proponent of anti-territorial (``moyo'') style. +\item [5] We consider games only up to year 2004, since Yi Ch'ang-ho was prominent representative of a balanced, careful player until then and still has this reputation in minds of many players, but is regarded to have altered his style significantly afterwards. \end{tablenotes} \end{threeparttable} \end{table} -- 2.11.4.GIT