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scientificComputing/projects/project_random_walk/random_walk.tex

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\documentclass[a4paper,12pt,pdftex]{exam}
\newcommand{\ptitle}{Random walk}
\input{../header.tex}
\firstpagefooter{Supervisor: Jan Grewe}{phone: 29 74588}%
{email: jan.grewe@uni-tuebingen.de}
\begin{document}
\input{../instructions.tex}
%%%%%%%%%%%%%% Questions %%%%%%%%%%%%%%%%%%%%%%%%%
\section*{Random walk with memory.}
The movement pattern of some animals can be described as a random walk when
searching for food. In some cases this random walk is not completely
random. In fact, sometimes there is some memory involved. Whenever
there is a positive gradient in food gain between successive steps the
animal will continue in the very same direction as in the step before. When the
next step leads to a decrease in food gain the animal switches back to
a random walk and changes directions randomly.
\begin{questions}
\question{} The accompanying dataset (random\_world.mat) contains a
single variable. This is the world (10000\,m$^2$ area with
10\,cm spatial resolution) in which there are randomly distributed
food sources (Gaussian blotches of food).
\begin{parts}
\part{} Create a plot of the world using \code{imshow}.\\[0.5ex]
\part{} Create a model animal (agent) that performs a pure random walk. The
agent can walk in eight different directions (the cardinal and
diagonal directions) with a stepsize of 10\,cm
(approximately). Let the agent start at a random location in the
world and count how much food it eats in 10000 steps (eaten food
disappears from the world, of course). If the agent bumps into the
borders of the world choose a different direction.\\[0.5ex]
\part{} Plot a typical example walk. (You can also make an animation
with MATLAB, see plotting chapter in the script).\\[0.5ex]
\part{} Same as above, but create a model animal that has some memory,
i.e. the direction is kept constant as long as there is a positive
gradient in the food gain. Otherwise, a random walk is performed.\\[0.5ex]
\part{} Plot a typical example walk also for this agent.\\[0.5ex]
\part{} Compare the performance of the two agents. Create
appropriate plots and apply statistical methods. You will need to
run the simulations several times to get a good estimate of the
neumbers.
\part{} Can you think about better search strategies?
\end{parts}
\end{questions}
\end{document}