work on summary

tex/.vscode/ltex.dictionary.en-US.txt

@@ -1 +1,2 @@
 Vatthauer
+Minimax

tex/sections/01_prolog.tex

@@ -1 +1,5 @@
-\chapter{Prolog}
+\chapter{Prolog}
+
+\section{Introduction to Logic Programming}
+
+\section{Usage of Prolog}

tex/sections/02_agents.tex

@@ -1 +1,6 @@
-\chapter{Rational Agents}
+\chapter{Rational Agents}
+
+
+\section{Classifying Environments}
+
+\section{Types of Agents}

tex/sections/03_problemsolving.tex

@@ -1 +1,42 @@
-\chapter{General Problem Solving}
+\chapter{General Problem-Solving}
+
+\section{Uninformed Search}
+An \textbf{uninformed} search algorithm only uses the information available in the problem definition.
+\subsection{Breadth-First Search}
+In breadth first search the fringe of the graph is treated as a FIFO queue.
+
+\begin{center}
+  \includegraphics[scale=0.5]{ressources/bfs.png}
+\end{center}
+\subsection{Uniform-Cost Search}
+For Graphs with step costs the BFS strategy might choose a suboptimal path.
+Instead, one should use uniform cost search, where the fringe is ordered by increasing step cost.
+\begin{center}
+  \includegraphics[scale=1.5]{ressources/ucs.pdf}
+\end{center}
+\subsection{Depth-First Search}
+In depth first search the fringe of the graph is organized as a LIFO stack.
+
+\begin{center}
+  \includegraphics[scale=0.5]{ressources/dfs.png}
+\end{center}
+
+\subsection{Iterative Deepening Search}
+Depth first search may diverge when considering infinite trees,
+instead one can use iterative deepening search, where the search depth is iteratively increased,
+thus yielding a mix of the BFS and DFS search strategies.
+
+\section{Informed Search}
+\textbf{Informed} search strategies try to guide the search by using heuristics.
+
+\subsection{Heuristics}
+
+\subsection{Greedy Search}
+\subsection{A-Star Search}
+
+\section{Adversarial Search}
+\subsection{Minimax Search}
+\subsection{Alpha-Beta Search}
+\subsection{Monte-Carlo Tree Search}
+
+\section{Constraint Satisfaction Problems}