This question paper consists
of 4 printed pages,
each of which is
identified by the Code
Number COMP232001
UNIVERSITY OF LEEDS
School of Computing
June 2004
AI23: Bio-Inspired Computing
Time allowed: 2 hours
Answer THREE questions.
Question 1
Habituation (or desensitisation) can be described as reduced responsiveness (or disengagement) in the presence of
continued stimulation. A minimal model of a habituation gate is a "pulsifier", which generates a brief pulse in
response to an arbitrarily long but continuous string of non-zero inputs.
a) Give a biological example of habituation.
[2 marks]
b) Label the neurons, and fully specify weights, thresholds and initial conditions, such that the circuit below
functions as a pulsifier. Assume the inputs and outputs are all binary.
[6 marks]
c) Test your circuit and show that:
(i)
the output neuron returns zero in response to a zero input;
(ii)
the output neuron returns a brief positive pulse in response to a continued stimulus, and
(iii)
the network resets itself when the stimulus is stopped.
[4 marks]
d) List two different algorithms for training dynamical neural networks. Which would you choose to train a
habituation circuit? Justify your answer.
[4 marks]
e) Could a feed-forward network be used to design the same habituation gate operation? Explain your answer.
[4 marks]
[Total: 20 marks]
TURN OVER
1
Question 2
The Travelling Salesman Problem (TSP) can be solved by
1)
2)
3)
4)
a)
Hopfield networks (or attractor neural networks)
Kohonen networks (or Self Organising Maps)
Genetic algorithms
Ant algorithms
Describe how each method can solve the TSP problem.
[8 marks]
b)
Write out a pseudo-code implementation of a 5-city TSP for one of the above algorithms. Include the
initial set up of parameters, training and running stages of the code, as needed.
[12 marks]
[Total: 20 marks]
TURN OVER
2
Question 3
Worker ants follow a simple clustering algorithm for cleaning up their nest. Objects to be cleaned emit an
attracting signal which cause ants to pick them up. The more isolated an object, the more likely it is to be picked
up. Clusters of objects emit a stronger signal which cause the ants to deposit objects onto them. The larger the
cluster, the more attractive it is for further depositions.
a) The key principle behind this algorithm is that of stigmergy. Explain what is meant by this term and how
stigmergy facilitates clustering in the example above.
[3 marks]
b) Describe what happens as ants begin to cluster randomly placed objects on a 2-D grid.
(i) How might the first cluster begin to form?
(ii) What will the object distribution on the grid look like after some time?
(iii) In the long run, where on the grid are the clusters most likely to be found?
[4 marks]
c) Clustering algorithms can straightforwardly generalise to sorting algorithms. How would you generalise the
clustering algorithm described above (i.e., what modifications/additions would you make) to obtain a sorting
algorithm that would partition multiple classes of objects into distinct clusters.
[3 marks]
d) Describe a specific real-world application of your sorting algorithm. How does it work?
[3 marks]
e) Suppose the ants in your algorithm were trained using a fitness function. Specify a suitable fitness function.
Justify your answer.
[3 marks]
f) List and explain the advantages of ant algorithms in real-world applications.
[4 marks]
[Total: 20 marks]
TURN OVER
3
Question 4
a) What role or effect does selection play in the following scenarios?
i)
ii)
iii)
iv)
Artificial evolution of mice in the mice-demo of BEAST
Artificial evolution in the absence of mutation (or with a negligibly slow mutation rate)
Biological evolution on a neutral landscape
Co-evolution in a predator-prey situation
[8 marks]
b) List and describe specific biological/natural analogies to three (3) of the above.
[6 marks]
c) List and describe three (3) different rules for selection for evolutionary and co-evolutionary algorithms.
[6 marks]
[Total: 20 marks]
END
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