\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
Fig.~\ref{fig:planificationWorkPlanGame} and
Fig.~\ref{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 \parencite{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 Game Format}) is a text format widely used for storing
records of Go matches which allows for variants, comments and other metadata.
Many popular playing tools use it. By supporting SGF vast existing collections
of games 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.

\subsection{Technological Infrastructure}

\subsubsection{Programming language}

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, 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 Fig.~\ref{fig:interfaces}.

\begin{figure}[h]
	\begin{center}
		\includegraphics[width=\textwidth]{diagrams/interfaces.png}
		\caption{Interfaces of the three components of the project.}
		The Engine and Trainer components are shown to share the Neural network
		model interface because they will interact with the same files (the
		files generated by the Trainer will be used by the Engine).
		\label{fig:interfaces}
	\end{center}
\end{figure}