\documentclass[british]{scrartcl}

\usepackage[british]{babel}
\usepackage{csquotes}
\usepackage{enumerate}
\usepackage[hidelinks]{hyperref}
\usepackage{minted}
\setminted{fontsize=\small,breaklines,breakanywhere,tabsize=4}
\usepackage{caption}
\newenvironment{longlisting}{\captionsetup{type=listing}}{}
\usepackage{skak}
\usepackage{tikz}
\usetikzlibrary{arrows, matrix, positioning}
\usepackage{cleveref}

\let\oldurl\url
\def\url#1{{\small\oldurl{#1}}}

\title{Testing Techniques}
\subtitle{Assignment 2 Automata Learning}
\author{Ren\'e den Hertog \and Camil Staps \and Erin van der Veen}
% Remove the date from '\maketitle'.
\makeatletter
	\patchcmd
		{\@maketitle}
		{{\usekomafont{date}{\@date\par}}\vskip\z@\@plus 1em}
		{}
		{}
		{}
\makeatother

\begin{document}

\maketitle

\section{Chocolate Bar Machine}
\begin{enumerate}
	\item
		L* creates large hypothesis model from the beginning.
		This means that it works faster with inefficient equivalence oracles.
		On the other hand, Rivest-Schapire and TTT create small models which can perform better with a fast equivalence oracles,
			because these algorithms spend less time on creating a well-based hypothesis.

	\item
		We used L* with the following counterexamples:

		\begin{itemize}
			\item 10ct 10ct 5ct snickers
			\item 10ct 10ct 10ct 10ct 10ct snickers snickers
			\item 10ct 10ct 10ct snickers 10ct twix
			\item 10ct 10ct 10ct 5ct snickers twix
		\end{itemize}

		The hypotheses are given in \cref{fig:lstar-run} (p.~\pageref{fig:lstar-run}).

		\begin{figure}[p]
			\includegraphics[width=.5\textwidth]{LStar_hypothesis0}
			\includegraphics[width=.5\textwidth]{LStar_hypothesis1}
			\includegraphics[width=.5\textwidth]{LStar_hypothesis2}
			\includegraphics[width=.5\textwidth]{LStar_hypothesis3}
			\caption{Learning the chocolate bar machine with L* (top to bottom, left to right).\label{fig:lstar-run}}
		\end{figure}

	\item
		States are uniquely identified by the amount injected in the machine.
		The machine does not accept more than 40ct, and the granularity is 5ct.
		Hence, there are $\frac{40}{5}+1=9$ states.
		The learned model also contains states, therefore they must be equivalent.

	\item

	\item

	\item

	\item
\end{enumerate}

\section{Bounded Retransmission Protocol}

\appendix
\section{Listings}


\end{document}