\section{Former Project: \textit{Neural Networks playing GO}  \cite{98nnpgo}}
We want to take some conlusions over the given work. During preparation for our
work, we took a look on given papers and programs. During this collection of facts, we
found some points we have to criticize and others we want to take over.\\
A first paper was already summarized in the introduction, so we will give only some short
statements for the next one.
\subsection{Discussing the paper}
This group used for their work (see \cite{98nnpgo}), SNNS and one-hidden layer feed forward nets.
One the one side, they only used standard \ANN s ( in further shorted with ANN), so they did
not implemented an evolution, which improves, in general, the output of the program
(see \ref{ch:intro:sane}).

\renewcommand{\ANN}{\textit{ANN}}	

\begin{itemize}
  \item The number of input units was far too large for the given situation.
  \item The number of neurons in the hidden layer seemed to be less than necessary.
  \item The hidden layer was fully connected to both the input and the output layer.
  \item The use of only one hidden layer complicates implementations of feedback algorithms.
  \item A variable system, which can be evolved, is more able to adapt the complex strategies of GO.
  \item Go is far too complex. So you should use more than the given training patterns.
\end{itemize}
\section{Improvements over the given papers}
We also found some things, we would improve in further work. This relates not only to \cite{97paper} but
also to the named paper \cite{98nnpgo} above.
\begin{description}
  \item{\textbf{The number of input units}}
  In \cite{98nnpgo} the number of input units was far too large, but this problem occured also
  in \cite{97paper}. With a better coding of the given board situation, the number of input
  nodes could be reduced, and so the complexity of the net.
  \item{\textbf{Hidden Layers}}
  Improvements can be made by using more layers in combination with more neurons. Also a feedback
  between neurons in hidden layers, could result in better performance. Both dicussed papers
  used only on hidden layer, with many (\cite{97paper}) or, relied to the complexity of the subject
  too few (\cite{98nnpgo}) nodes in the hidden layer.
  \item{\textbf{The hidden layer was fully connected to both the input and the output layer.}}
  One paper (\cite{97paper}) used SANE for keeping the number of connections low. The other
  (\cite{98nnpgo}) connected every node in the hidden layer with each of both the input and the
  output layer. Using a evolution algorithm such as SANE (see \ref{ch:intro:sane}), the number
  of connections between I/O Layers and the hidden layer structure can be optimized.
  \item{\textbf{Number of training patterns}}
  Strategics used for GO are, on the one side pattern oriented, on the other side tricky. Therefore to
  train a \ANN s with games, you should use a large number of training patterns. But you have to keep
  in mind, that training costs time. Therefore you will have to evaluate the best combination of
  training patterns and computation time.
\end{description}


