\section{Conclusion}
Detailed benchmarking and thorough comparison of the results still has to be
done.

The object-based approach shows to heavily depend on the number of boxes.
Describing the goal state for $n$ boxes needs a formula of $n!$ size. The size
of the encoding also increases with the number of free blocks. When a screen is
circular the parser also parses the blocks on the sides to be free spaces
whereas they can never be reached. By improving the parser to disregard those
positions we can increase the performance too.

The coordinate-based approach revealed that the following factors affect the
feasibility of solving a particular \emph{sokoban} field:
\begin{itemize}
  \item Overall size of a field
  \item Proximity of boxes to the man
  \item Number of boxes (to a lesser extent)
\end{itemize}
A possible improvement for the coordinate-based approach is the elimination of
outer walls, which can reduce overall field sizes.\\
In total, the material obtained by implementing two different ways to encode a
\emph{sokoban} solver might give the us the ability to approach the next
assignment in a more elaborate way.
