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diff --git a/doc/tex/results.tex b/doc/tex/results.tex index 5433041..fac2df9 100644 --- a/doc/tex/results.tex +++ b/doc/tex/results.tex @@ -72,11 +72,11 @@ A record of the game is shown in \fref{fig:mctsVSmcts}. Since tree exploration on smaller boards or advanced games with little empty spaces should be easier the algorithm has also been tested on some Go problems. -A Go problem or tsumego is a predefined layout of the board, or of a section of +A Go problem or \gls{tsumego} is a predefined layout of the board, or of a section of the board, for which the player must find some beneficial move. Life and death -problems are a subset of tsumegos in which the survival of a group depends on +problems are a subset of \gls{tsumego}s in which the survival of a group depends on finding the correct sequence to save or kill the group. One collection of such -tsumegos is \textit{Cho Chikun's Encyclopedia of Life and Death}, part of which +\gls{tsumego}s is \textit{Cho Chikun's Encyclopedia of Life and Death}, part of which are available on OGS \cite{ogsLifeAndDeath}, an online Go server. The first of these problems and what the algorithm suggested as moves is shown @@ -86,18 +86,18 @@ Black makes the first move, which means the solution is to find some favorable outcome for black, which in this case is killing the white group. The white group has a critical point on B1. If white plays on B1 they make two eyes and live, but if black plays there first white can't make two eyes and dies, so B1 -is the solution to the tsumego. This is one of the easiest Go problems. +is the solution to the \gls{tsumego}. This is one of the easiest Go problems. The algorithm neglects this solution. While asked five times to generate a move for the starting position it suggested B1 only once. But notably, after making another move, it consistently suggested B1 for white, which is the solution now that white has to play. So in the end it was able to -solve the tsumego, probably because after making a move it had already explored +solve the \gls{tsumego}, probably because after making a move it had already explored part of the tree but it was difficult that it explored the solution for the first move. -The engine was tested against other tsumegos but it was not able to solve them, +The engine was tested against other \gls{tsumego}s but it was not able to solve them, so no more are shown here. \subsection{Neural Network Training} |