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diff --git a/doc/tex/planification.tex b/doc/tex/planification.tex index 00695a1..f0f7535 100644 --- a/doc/tex/planification.tex +++ b/doc/tex/planification.tex @@ -1,6 +1,6 @@ \section{Planning} -\subsection{Driving needs} +\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 @@ -29,18 +29,18 @@ Presented here are the ideal targets of the project. recorded in the SGF format. \end{itemize} -\subsection{Project stages} +\subsection{Project Stages} The project will be organized in several stages based on the different components and needs. -\subsubsection{Game implementation} +\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} +\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 @@ -50,7 +50,7 @@ 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} +\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 @@ -66,7 +66,7 @@ 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} +\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 @@ -92,9 +92,9 @@ Friday. Gantt diagrams for the planned working schedule are shown as \end{center} \end{figure} -\subsection{Previous works} +\subsection{Previous Works} -\subsubsection{Existing engines} +\subsubsection{Existing Engines} \paragraph{AlphaGo} @@ -103,28 +103,28 @@ 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 +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}} +\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}} +\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} +\subsubsection{Existing Standards} -\paragraph{GTP~\cite{gtp}} +\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 @@ -132,20 +132,23 @@ 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}} +\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. +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}} +\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}} +\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 @@ -153,7 +156,7 @@ layouts. \subsection{Technological Infrastructure} -\subsubsection{Programming language}\label{sec:programmingLanguage} +\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 |