added conclusions and future work in spanish but not in the final pdf

Author: LauraWangQiu

docs/AlphaDeepChess/Capitulos/ConclusionesTrabajoFuturo.tex

@@ -1,38 +1,36 @@
-\chapter{Conclusions and future work}
+\chapter{Conclusiones y trabajo futuro}
 
-We have successfully developed a competitive chess engine based on an enhanced minimax search with alpha-beta pruning, a straightforward materialistic evaluation, and a Most Valuable Victim-Least Valuable Aggressor (MVV-LVA) move-ordering heuristic.
+Hemos desarrollado con éxito un motor de ajedrez competitivo basado en una búsqueda minimax mejorada con poda alfa-beta, una evaluación materialista directa y una heurística de ordenación de jugadas MVV-LVA (Most Valuable Victim-Least Valuable Aggressor).
 
 \vspace{1em}
 
-\noindent Building on this basic implementation, we have researched and analyzed the following algorithmic techniques to further improve the bot's playing strength:
+\noindent Partiendo de esta implementación básica, hemos investigado y analizado las siguientes técnicas algorítmicas para mejorar aún más la fuerza de juego del bot:
 
 \begin{itemize}[itemsep=1pt]
-    \item \textit{Transposition tables with Zobrist hashing}: provided an increase in performance by avoiding redundant position evaluations.
-    \item \textit{Move generator with magic bitboards and PEXT instructions}: substantial improvement in move generation.
-    
-    \item \textit{Evaluation with king safety and piece mobility parameters}: showed no clear improvement, likely due to the added computational cost and the difficulty of correlating these factors directly with positional strength.
-    \item \textit{Multithreaded search}: requires further optimization of data structures and algorithms for concurrent access to yield tangible gains.
-    \item \textit{Search with Late Move Reductions}: did not improve performance, as our current move-ordering heuristic is not strong enough to support such aggressive pruning.
+    \item \textit{Tablas de transposición con Zobrist hashing}: proporcionó un aumento en el rendimiento al evitar evaluaciones de posición redundantes.
+    \item \textit{Generador de jugadas con bitboards mágicos e instrucciones PEXT}: mejora sustancial en la generación de jugadas.
+    \item \textit{Evaluación con parámetros de seguridad de rey y movilidad de pieza}: no mostró una mejora clara, probablemente debido al coste computacional añadido y a la dificultad de correlacionar estos factores directamente con la fuerza posicional.
+    \item \textit{Búsqueda multihilo}: requiere una mayor optimización de las estructuras de datos y algoritmos de acceso concurrente para producir ganancias tangibles.
+    \item \textit{Búsqueda con reducciones de movimientos tardíos}: no mejoró el rendimiento, ya que nuestra heurística actual de ordenación de movimientos no es lo suficientemente fuerte como para soportar una poda tan agresiva.
 \end{itemize}
 
-\noindent The engine achieved an ELO rating of 1900 on \textit{Lichess} while running on a Raspberry Pi 5 with a 2GB transposition table, demonstrating its efficiency even on resource-constrained hardware.
+\noindent El motor alcanzó una puntuación ELO de 1900 en \textit{Lichess} mientras se ejecutaba en una Raspberry Pi 5 con una tabla de transposición de 2GB, lo que demuestra su eficiencia incluso en hardware con recursos limitados.
 
 \vspace{1em}
 
-\noindent The engine's \textit{Lichess} profile can be found at:\\
+\noindent El perfil del motor de ajedrez puede encontrarse en: \\
 \url{https://lichess.org/@/AlphaDeepChess}
 
-
 \newpage
-\section{Future work}
+\section{Trabajo futuro}
 
-\noindent Potential avenues for further improvement include:
+\noindent Entre las posibles vías de mejora figuran las siguientes:
 
 \begin{itemize}[itemsep=1pt]
-    \item \textit{Neural-network evaluation and move ordering}:  
-    Application of neural networks for the evaluation function and the move ordering heuristic, following the steps of \textit{Stockfish}, the actual best chess engine. This could unlock more aggressive pruning strategies.
-    \item \textit{Advanced pruning techniques}:  
-    With a stronger evaluation and ordering heuristic, revisit and tune the actual Late Move Reductions, and implement null move pruning technique.
-    \item \textit{Multithreading optimizations}:  
-    Refine concurrent search algorithm and redesign shared data structures like transposition tables to support concurrent access.
+    \item \textit{Evaluación por redes neuronales y ordenación de jugadas}:
+    Aplicación de redes neuronales para la función de evaluación y la heurística de ordenación de jugadas, siguiendo los pasos de \textit{Stockfish}, el mejor motor de ajedrez actual. Esto podría desbloquear estrategias de poda más agresivas.
+    \item \textit{Técnicas avanzadas de poda}:  
+    Con una evaluación más sólida y una heurística de ordenación, revisar y ajustar las reducciones de movimientos tardíos e implementar la técnica de poda de movimientos nulos.
+    \item \textit{Optimizaciones multihilo}:  
+    Perfeccionar el algoritmo de búsqueda concurrente y rediseñar las estructuras de datos compartidos como las tablas de transposición para soportar el acceso concurrente.
 \end{itemize}

docs/AlphaDeepChess/Capitulos/ConclusionsFutureWork.tex

@@ -1,7 +1,37 @@
-\chapter*{Conclusions and future work}
-\label{cap:conclusions}
-\addcontentsline{toc}{chapter}{Conclusions and Future Work}
+\chapter{Conclusions and future work}
 
-Conclusions and future lines of work. This chapter contains the translation of Chapter \ref{cap:conclusiones}.
+We have successfully developed a competitive chess engine based on an enhanced minimax search with alpha-beta pruning, a straightforward materialistic evaluation, and a Most Valuable Victim-Least Valuable Aggressor (MVV-LVA) move-ordering heuristic.
 
-TODO
+\vspace{1em}
+
+\noindent Building on this basic implementation, we have researched and analyzed the following algorithmic techniques to further improve the bot's playing strength:
+
+\begin{itemize}[itemsep=1pt]
+    \item \textit{Transposition tables with Zobrist hashing}: provided an increase in performance by avoiding redundant position evaluations.
+    \item \textit{Move generator with magic bitboards and PEXT instructions}: substantial improvement in move generation.
+    \item \textit{Evaluation with king safety and piece mobility parameters}: showed no clear improvement, likely due to the added computational cost and the difficulty of correlating these factors directly with positional strength.
+    \item \textit{Multithreaded search}: requires further optimization of data structures and algorithms for concurrent access to yield tangible gains.
+    \item \textit{Search with Late Move Reductions}: did not improve performance, as our current move-ordering heuristic is not strong enough to support such aggressive pruning.
+\end{itemize}
+
+\noindent The engine achieved an ELO rating of 1900 on \textit{Lichess} while running on a Raspberry Pi 5 with a 2GB transposition table, demonstrating its efficiency even on resource-constrained hardware.
+
+\vspace{1em}
+
+\noindent The engine's \textit{Lichess} profile can be found at:\\
+\url{https://lichess.org/@/AlphaDeepChess}
+
+
+\newpage
+\section{Future work}
+
+\noindent Potential avenues for further improvement include:
+
+\begin{itemize}[itemsep=1pt]
+    \item \textit{Neural-network evaluation and move ordering}:  
+    Application of neural networks for the evaluation function and the move ordering heuristic, following the steps of \textit{Stockfish}, the actual best chess engine. This could unlock more aggressive pruning strategies.
+    \item \textit{Advanced pruning techniques}:  
+    With a stronger evaluation and ordering heuristic, revisit and tune the actual Late Move Reductions, and implement null move pruning technique.
+    \item \textit{Multithreading optimizations}:  
+    Refine concurrent search algorithm and redesign shared data structures like transposition tables to support concurrent access.
+\end{itemize}

docs/AlphaDeepChess/Capitulos/EstadoDeLaCuestion.tex

@@ -450,7 +450,7 @@ \section{UCI}\label{sec:uci}
 
 \section{\textit{Stockfish}}\label{sec:stockfish}
 
-\noindent \textit{Stockfish} is an open-source chess engine that has consistently ranked among the strongest in the world~\cite{Stockfish}. It is written in C++ and released under the GNU General Public License (GPL). \textit{Stockfish} has been developed and refined by a large community of contributors.
+\noindent \textit{Stockfish} is an open source chess engine that has consistently ranked among the strongest in the world~\cite{Stockfish}. It is written in C++ and released under the GNU General Public License (GPL). \textit{Stockfish} has been developed and refined by a large community of contributors.
 
 \vspace{1em}
 
@@ -470,7 +470,7 @@ \section{\textit{Stockfish}}\label{sec:stockfish}
 
 \section{\textit{Lichess}}\label{sec:lichess}
 
-\textit{Lichess} is a free and open-source online chess platform that offers a wide range of features for both casual and competitive players. It was created by Thibault Duplessis in 2010 and has since grown into one of the most popular chess websites in the world, hosting millions of games each day~\cite{Lichess}.
+\textit{Lichess} is a free and open source online chess platform that offers a wide range of features for both casual and competitive players. It was created by Thibault Duplessis in 2010 and has since grown into one of the most popular chess websites in the world, hosting millions of games each day~\cite{Lichess}.
 
 \vspace{1em}
 
@@ -490,7 +490,7 @@ \section{\textit{Lichess}}\label{sec:lichess}
 
 \section{\textit{Cutechess}}\label{sec:cutechess}
 
-\noindent \textit{Cutechess} is an open-source tool widely adopted in the chess programming community. It allows users to run automated matches between engines, compare their performance, and analyze gameplay~\cite{CuteChess}.
+\noindent \textit{Cutechess} is an open source tool widely adopted in the chess programming community. It allows users to run automated matches between engines, compare their performance, and analyze gameplay~\cite{CuteChess}.
 
 \vspace{1em}
 

docs/AlphaDeepChess/Cascaras/abstract.tex

@@ -2,7 +2,7 @@ \chapter*{Abstract}
 
 \section*{\tituloPortadaEngVal}
 
-Chess engines have played a fundamental role in the advancement of artificial intelligence applied to the game since the mid-20th century. Today, \textit{Stockfish}, the most powerful open source chess engine, still relies on alpha-beta pruning, but also incorporates machine learning techniques.
+Chess engines have played a fundamental role in the advancement of artificial intelligence applied to the game since the mid-20th century. Today, \textit{Stockfish}, the most powerful and open source chess engine, still relies on alpha-beta pruning, but also incorporates machine learning techniques.
 
 \vspace{1em}
 

docs/AlphaDeepChess/TFGTeXiS.pdf

@@ -186,7 +186,8 @@
 \include{Capitulos/DescripcionTrabajo}
 \include{Capitulos/ImprovementTechniques}
 \include{Capitulos/AnalysisOfImprovements}
-\include{Capitulos/ConclusionesTrabajoFuturo}
+\include{Capitulos/ConclusionsFutureWork}
+% \include{Capitulos/ConclusionesTrabajoFuturo}
 
 %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
 % Si el TFG se escribe en inglés, comentar las siguientes líneas