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diff --git a/assignments/assignment2/assignment2.tex b/assignments/assignment2/assignment2.tex index 26e964e..655179b 100644 --- a/assignments/assignment2/assignment2.tex +++ b/assignments/assignment2/assignment2.tex @@ -7,6 +7,7 @@ \usepackage{graphicx} \usepackage{caption} \usepackage{cleveref} +\usepackage{subfig} \let\oldurl\url \def\url#1{{\small\oldurl{#1}}} @@ -44,23 +45,22 @@ \item 10ct 10ct 10ct 10ct 10ct snickers snickers \item 10ct 10ct 10ct snickers 10ct twix \item 10ct 10ct 10ct 5ct snickers twix + \item 10ct 10ct 5ct 10ct snickers mars + \item 10ct 10ct 5ct 10ct 10ct mars mars snickers \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. + States are in principle uniquely identified by the amount injected in the machine. + The machine does not accept more than 45ct, and the granularity is 5ct. + Hence, there are $\frac{45}{5}+1=10$ states. + However, there are two states in which we have 35ct, namely one where we can still insert money + (accessed through \enquote{10ct 10ct 5ct 10ct}) + and one where we cannot (e.g. \enquote{10ct 10ct 10ct 5ct}). + Hence, the complete model has 11 states. + + The learned model also contains 11 states, therefore they must be equivalent. \item A counter-example $\pi$ consists of a prefix, an input where an inconsistency appears and a suffix showing the inconsistency: @@ -77,8 +77,34 @@ \item \item + The method with user queries worked very efficiently in combination with L*. + It allows the user to insert cleverly chosen counter-examples, + but does not ask him as often as other learning methods would do. + However, we realise that for more complex systems it may be difficult for the user to provide counter-examples. + + As for the testing method, + on our small example a random walk appears to be the fasted. + However, as noted in the source code (\texttt{BasicLearner.java}), + it may perform worse than the others on large models. + \end{enumerate} +\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} + \includegraphics[width=.5\textwidth]{LStar_hypothesis4} + \includegraphics[width=.5\textwidth]{LStar_hypothesis5} + \caption{Learning the chocolate bar machine with L* (top to bottom, left to right).\label{fig:lstar-run}} +\end{figure} +\begin{figure}[p] + \ContinuedFloat + \includegraphics[width=.5\textwidth]{LStar_hypothesis6} + \caption[]{Learning the chocolate bar machine with L* (top to bottom, left to right).\label{fig:lstar-run}} +\end{figure} +\thispagestyle{empty} + \section{Bounded Retransmission Protocol} \end{document} |