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UNIVERSITY OF ABERDEEN
SESSION 2009-2010
Examination in CS1013 (Grand Challenges of Artificial Intelligence)
25 January, 2010
(12:00 – 14:00)
Answer Question 1 and TWO other questions. Question 1 is compulsory.
Each question is worth 25 marks; the marks for each part of a question are shown in brackets.
1.
Answer all ten parts of this question. Note that brief answers are expected for these questions.
(a)
Give an example of an application where natural language processing techniques are used with
reasonable success, and another application where natural language processing techniques are less
successful.
(2)
(b) Give an example of two sentences which could be confusing for a speech recognition system, and
explain why it could be difficult for the computer to pick the intended one. Note that the problem here is
to convert a speech signal to a text, not necessarily to understand the meaning.
(2)
(c)
(2)
Describe two major problems for robotics research within Artificial Intelligence.
(d) Describe a possible application of a Bayesian belief network, and describe what aspects of the problem
make it particularly suitable for a Bayesian belief network.
(2)
(e)
Roughly how many synapses are in the human brain?
(2)
(f)
Briefly describe the essential components of a “Turing Machine”?
(3)
(g) Artificial Intelligence systems which play chess search through the possible actions that can be taken.
Explain why AI planning systems do not search through the possible actions in the same way.
(3)
(h) Sketch a diagram of an artificial neural network which has two inputs and one output, and which could
recognise the exclusive-or function. You do not need to provide the weights on the connections.
(3)
(i)
Give an example of how the discipline of Neuroscience can contribute to Artificial Intelligence.
(3)
(j)
What is meant by the “Credit Assignment Problem” in reinforcement learning?
(3)
(25)
2.
(a)
Describe two example applications of computer vision technology, and for each one describe what is the
input and what is the output.
(4)
(b) How does the commonsense knowledge problem cause difficulties in the field of computer vision?
Explain your answer with an example scenario.
(3)
(c)
There are a number of levels of processing in computer vision. Explain these levels, by describing the
input of each level and the process carried out.
(10)
(d) Humans can use commonsense knowledge to aid top down processing in both natural language
processing and computer vision. Briefly explain the similarities. Explain your answer with example
scenarios.
(3)
(e)
(5)
Explain the need for interaction between top down and bottom up processing in vision.
(25)
PLEASE TURN OVER
CS1013 (Grand Challenges of Artificial Intelligence)
3.
25 January, 2010
You have a five litre jug and a three litre jug. You have six possible actions: fill the five litre; fill the
three litre; empty the five litre; empty the three litre; pour from the five litre into the three litre; pour
from the three litre into the five litre. A tap provides an unlimited supply of water. You start with both
jugs empty. The goal is to have exactly four litres in the five litre jug.
(a)
Show the entire search tree for this problem (by depth first search) and calculate how many steps it takes
to get four litres in the five litre jug. Do not include any repeated states in your search tree.
(9)
(b) Assuming that jugs are constrained to have whole numbers of litres, and without using the results of the
search tree, calculate an upper bound on the maximum number of possible states in the system, and list
all these states.
(3)
(c)
(2)
In your answer to part (b), underline the states which are never reached in the search tree.
(d) You should see from your answer above that there is a pattern to the states which can never be achieved
in all problems with two jugs (of any capacity). Describe the characteristics of states which cannot be
reached, and explain why this must be so.
(6)
(e)
(5)
What are the characteristics of problems which computers can solve very well by using search?
(25)
4.
(a)
Marvin Minsky has noted the success of Artificial Intelligence (AI) in areas requiring specialist
expertise, but the failures in areas requiring general abilities. Describe some of the things which AI
cannot do, and explain Minsky’s ideas about why this is so, and in particular what is missing from AI
systems.
(6)
(b) David Marr warned against mimicry in AI systems, both the mimicry of behaviour and of structure.
Explain, with examples, what he specifically warned against.
(6)
(c)
In order to apply some AI techniques to solve real-world problems, it is often necessary for a human to
“abstract” the problem, identifying the salient features and representing the problem in a form which
allows the computer to use a the AI technique directly. Some AI researchers such as Rodney Brooks
have criticised this use of abstraction on a number of grounds. Describe some of these criticisms.
(7)
(d) Some AI researchers have criticised the Turing test because it does not motivate AI researchers to try to
pass it. Explain why this is so, and what alternative tests have been proposed.
(6)
(25)
END OF PAPER
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