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diff --git a/doc/diagrams/planificationWorkPlanEngine.puml b/doc/diagrams/planificationWorkPlanEngine.puml
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+@startgantt
+
+!include skinparams.puml
+!include skinparamsGantt.puml
+
+printscale weekly
+Saturday are closed
+Sunday are closed
+
+Project starts 2021-01-11
+
+-- Preliminary research --
+[Previous works research] as [PWR] lasts 1 week
+[Algorithms research] as [AR] lasts 2 weeks
+
+-- Engine Implementation --
+[Engine modelling] as [EM] lasts 1 week
+[Engine implementation] as [EI] lasts 4 weeks
+
+-- Algorithms Implementations --
+[Monte Carlo implementation] as [MCI] lasts 4 weeks
+[Neural networks research] as [NNR] lasts 2 weeks
+[Neural networks implementation] as [NNI] lasts 3 weeks
+
+-- Testing --
+[Engine unit testing] as [EUT] lasts 4 weeks
+[System testing] as [ST] lasts 1 week
+
+-- Analysis --
+[Algorithms comparison] as [AC] lasts 2 weeks
+
+[PWR] -> [AR]
+[AR] -> [EM]
+
+[EM] -> [MCI]
+[EM] -> [EI]
+[EM] -> [EUT]
+
+[MCI] -> [NNR]
+[NNR] -> [NNI]
+
+[NNI] -> [ST]
+
+[ST] -> [AC]
+
+@endgantt
diff --git a/doc/diagrams/planificationWorkPlanGame.puml b/doc/diagrams/planificationWorkPlanGame.puml
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index 0000000..ffaf72c
--- /dev/null
+++ b/doc/diagrams/planificationWorkPlanGame.puml
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+@startgantt
+
+!include skinparams.puml
+!include skinparamsGantt.puml
+
+printscale weekly zoom 2
+Saturday are closed
+Sunday are closed
+
+Project starts 2020-11-02
+
+-- Preliminary research --
+[Previous works research] as [PWR] lasts 1 week
+
+-- Game Implementation --
+[Domain modelling] as [DM] lasts 1 week
+[Domain implementation] as [DI] lasts 6 weeks
+
+-- Testing --
+[Unit testing] as [UT] lasts 6 weeks
+[System testing] as [ST] lasts 1 week
+
+[PWR] -> [DM]
+
+[DM] -> [DI]
+[DM] -> [UT]
+
+[DI] -> [ST]
+
+@endgantt
diff --git a/doc/tex/planification.tex b/doc/tex/planification.tex
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+\section{Planning}
+
+\subsection{Driving Needs}
+
+As one of the deepest and most studied games in the world, Go presents a very
+interesting problem for artificial intelligence. Implementing not only the
+game's simple but subtle rules, but a system capable of playing it with a
+satisfying level of skill, is a task worth of pursuing as an exercise on
+software design, algorithmics and AI research.
+
+On the practical level, this project can be a foundation for the development of
+different Go analysis algorithms by providing an existing engine to house them,
+which can be of interest to Go players and software scientists alike.
+
+\subsection{Reach}
+
+Presented here are the ideal targets of the project.
+
+\begin{itemize}
+	\item An implementation of the game of Go, that is, a system for holding the
+		moves and variants of a match (a tree of moves) and the logic for the
+		game's rules.
+	\item An engine capable of analyzing board positions and generating strong
+		moves via various decision algorithms.
+	\item Either a GUI specifically developed for the project or an
+		implementation of an existing protocol so the engine can be used with
+		existing tools and GUIs.
+	\item A way for processing existing records of games, which are usually
+		recorded in the SGF format.
+\end{itemize}
+
+\subsection{Project Stages}
+
+The project will be organized in several stages based on the different
+components and needs.
+
+\subsubsection{Game Implementation}
+
+The rules of the game must be implemented, ideally in a way they can be tested
+by direct human play. This system will at its bare minimum represent the
+Japanese Go rules (area scoring, no superko rule, no suicide moves).
+
+\subsubsection{Engine Implementation}
+
+The key of this project is to create some kind of system able to generate strong
+moves based on any given board configuration: this will be such system. It will
+implement an existing protocol so it can be used with other compatible tools. It
+has to be able to receive game updates and configuration and to output moves for
+either player. It should also be modular enough so different algorithms can be
+selected and tested against each other as an experimental search for the best of
+them.
+
+\subsubsection{Artificial Intelligence Algorithms}
+
+Different algorithms for the engine to use should be implemented and tested. The
+results of this development and testing process should be presented as part of
+the final version of the project.
+
+\subsection{Logistics}
+
+The project will be developed by Íñigo Gutiérrez Fernández, student of the
+Computer Software Engineering Degree at the University of Oviedo, with
+supervision from Vicente García Díaz, Associate Professor in the Department of
+Computer Science at the University of Oviedo.
+
+The used material consists of a development and testing machine owned by the
+student with specifications stated later on the project plan.
+
+\subsection{Work Plan}
+
+The sole developer will be the student, who is currently working as a Junior
+Software Engineer on a 35 hour per week schedule and with no university
+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:planificationWorkPlanGame} and
+\fref{fig:planificationWorkPlanEngine}.
+
+\begin{figure}[h]
+	\begin{center}
+		\includegraphics[width=\textwidth]{diagrams/planificationWorkPlanGame.png}
+		\caption{Initial work plan for implementing the game.
+		}\label{fig:planificationWorkPlanGame}
+	\end{center}
+\end{figure}
+
+\begin{figure}[h]
+	\begin{center}
+		\includegraphics[width=\textwidth]{diagrams/planificationWorkPlanEngine.png}
+		\caption{Initial work plan for implementing the engine and algorithms.
+		}\label{fig:planificationWorkPlanEngine}
+	\end{center}
+\end{figure}
+
+\subsection{Previous Works}
+
+\subsubsection{Existing Engines}
+
+\paragraph{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\cite{natureAlphaGo2016} written by the team and
+published on Nature is available on
+https://storage.googleapis.com/deepmind-media/alphago/AlphaGoNaturePaper.pdf.
+
+The unprecedented success of AlphaGo served as inspiration for many AI projects,
+including this one.
+
+\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.
+
+\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.
+
+\subsubsection{Existing Standards}
+
+\paragraph{GTP\cite{gtp}}
+
+GTP (\textit{Go Text Protocol}) is a text based protocol for
+communication with computer go programs. It is the protocol used by GNU Go and
+the more modern and powerful KataGo. By supporting GTP the engine developed for
+this project can be used with existing GUIs and other programs, making it easier
+to use it with the tools users are already familiar with.
+
+\paragraph{SGF\cite{sgf}}
+
+SGF (\textit{Smart Go Format} or, in a more general context, \textit{Smart Game
+Format}) is a text format widely used for storing records of Go matches which
+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.
+
+\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.
+
+\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.
+
+\subsection{Technological Infrastructure}
+
+\subsubsection{Programming Language}\label{sec:programmingLanguage}
+
+The resulting product of this project will be one or more pieces of software
+able to be run locally on a personal computer. The programming language of
+choice is Python\cite{python}, for various reasons:
+
+\begin{itemize}
+
+	\item It has established a reputation on scientific fields and more
+		specifically on AI research and development.
+	\item Interpreters are available for many platforms, which allows the most
+		people possible to access the product.
+	\item Although not very deeply, it has been used by the developer student
+		during its degree including in AI and game theory contexts.
+
+\end{itemize}
+
+\subsubsection{Interface}
+
+Both the game and the engine will offer a text interface. For the game this
+allows for quick human testing. For the engine it is mandated by the protocol,
+since GTP is a text based protocol for programs using text interfaces.
+Independent programs compatible with this interface can be used as a GUI.
+
+There is also the need of an interface with SGF files so existing games can be
+processed by the trainer.
+
+Both the engine and the trainer will need to interface with the files storing
+the neural network models.
+
+The systems' interfaces are shown in \fref{fig:interfaces}.
+
+\begin{figure}[h]
+	\begin{center}
+		\includegraphics[width=\textwidth]{diagrams/interfaces.png}
+		\caption{Interfaces of the three components of the project.}
+		\label{fig:interfaces}
+	\end{center}
+\end{figure}