Adding examples of protocols.

1 files changed, 69 insertions, 9 deletions

diff --git a/doc/tex/planning.tex b/doc/tex/planning.tex
index 269645a..1450e8a 100644
--- a/doc/tex/planning.tex
+++ b/doc/tex/planning.tex
@@ -48,7 +48,9 @@ responsibilities other than this project. Taking this into account, a sensible
 initial assumption is that he will be able to work 3 hours a day, Monday to
 Friday. Gantt diagrams for the planned working schedule are shown as
 \fref{fig:planningWorkPlanGame} and
-\fref{fig:planningWorkPlanEngine}.
+\fref{fig:planningWorkPlanEngine}. This planning predicts 6 months of
+development, from November 2020 to April 2021. With the planned schedule of 3
+hours a day on weekdays this amounts to 375 hours.
 
 \begin{figure}[h]
 	\begin{center}
@@ -70,30 +72,61 @@ Friday. Gantt diagrams for the planned working schedule are shown as
 
 \subsubsection{Existing Engines}
 
-\paragraph{AlphaGo}
+\begin{figure}[h]
+	\begin{center}
+		\includegraphics[width=0.5\textwidth]{img/Alphago_logo_Reversed.jpg}
+		\caption{AlphaGo logo. By Google DeepMind - Google DeepMind AlphaGo
+		Logo, Public Domain,
+		https://commons.wikimedia.org/w/index.php?curid=47169369
+		}\label{fig:alphaGoLogo}
+	\end{center}
+\end{figure}
+
+\paragraph{AlphaGo \cite{alphaGo}}
 
 A Go play and analysis engine developed by DeepMind Technologies, a company
 owned by Google. It revolutionized the world of Go in 2015 and 2016 when it
 respectively became the first AI to win against a professional Go player and
 then won against Lee Sedol, a Korean player of the highest professional rank and
 one of the strongest players in the world at the time. Its source code is
-closed, but a paper written by the team has been
-published on Nature \cite{natureAlphaGo2016}.
+closed, but a paper written by the team has been published on Nature
+\cite{natureAlphaGo2016}. The logo of the project is shown on
+\fref{fig:alphaGoLogo}.
 
 The unprecedented success of AlphaGo served as inspiration for many AI projects,
 including this one.
 
+\begin{figure}[h]
+	\begin{center}
+		\includegraphics[width=0.5\textwidth]{img/katago.png}
+		\caption{KataGo logo.
+		https://katagotraining.org/static/images/katago.png
+		}\label{fig:kataGoLogo}
+	\end{center}
+\end{figure}
+
 \paragraph{KataGo \cite{katago}}
 
 An open source project based on the AlphaGo paper that also achieved superhuman
 strength of play. The availability of its implementation and documentation
-presents a great resource for this project.
+presents a great resource for this project. The logo of the project is shown on
+\fref{fig:kataGoLogo}.
+
+\begin{figure}[h]
+	\begin{center}
+		\includegraphics[width=0.5\textwidth]{img/gnuGoLogo.jpg}
+		\caption{GnuGo logo.
+		https://www.gnu.org/software/gnugo/logo-36.jpg
+		}\label{fig:gnuGoLogo}
+	\end{center}
+\end{figure}
 
 \paragraph{GnuGo \cite{gnugo}}
 
 A software capable of playing Go part of the GNU project. Although not a strong
 engine anymore, it is interesting for historic reasons as the free software
-engine for which the GTP protocol was first defined.
+engine for which the GTP protocol was first defined. The logo of the project is shown on
+\fref{fig:gnuGoLogo}.
 
 \subsubsection{Existing Standards}
 
@@ -113,19 +146,46 @@ allows for variants, comments and other metadata. It was devised for Go but it
 supports other games with similar turn-based structure. Many popular playing
 tools use it. By supporting SGF vast existing collections of games, such as
 those played on online Go servers, can be used to train the decision algorithms
-based on neural networks.
+based on neural networks. An example of a SGF file can be seen on
+\lref{lst:sgfExample}.
+
+\begin{listing}[h]
+	\inputminted[breakafter=\]]{text}{listings/sgfExample.sgf}
+	\caption{SGF example. Describes a tsumego (Go problem) setup and two
+	variants, one commented as "Correct" and other commented as "Incorrect".}
+	\label{lst:sgfExample}
+\end{listing}
+
+\begin{figure}[h]
+	\begin{center}
+		\includegraphics[width=0.5\textwidth]{img/sabaki.jpg}
+		\caption{Sabaki screenshot.
+		https://sabaki.yichuanshen.de/img/screenshot.png
+		}\label{fig:sabaki}
+	\end{center}
+\end{figure}
 
 \subsubsection{Sabaki \cite{sabaki}}
 
 Sabaki is a Go board software compatible with GTP engines. It can serve as a GUI
 for the engine developed in this project and as an example of the advantages of
-following a standardized protocol.
+following a standardized protocol. Part of its graphical interface is shown on
+\fref{fig:sabaki}.
+
+\begin{figure}[h]
+	\begin{center}
+		\includegraphics[width=0.5\textwidth]{img/kerasLogo.jpg}
+		\caption{Keras logo.
+		https://keras.io/img/logo.png
+		}\label{fig:kerasLogo}
+	\end{center}
+\end{figure}
 
 \subsubsection{Keras \cite{keras}}
 
 Keras is a deep learning API for Python allowing for the high-level definition
 of neural networks. This permits easily testing and comparing different network
-layouts.
+layouts. The logo of the project is shown on \fref{fig:kerasLogo}.
 
 \subsection{Technological Infrastructure}