International Higher Diploma in Computer Studies
Artificial Intelligence
The marks given in brackets are indicative of the weight given to each part of the question.
Answer FOUR questions out of SIX.
Time: TWO hours and 10 minutes reading time
Reference materials are NOT allowed.
Question 1
a)
Describe the main applications of Artificial Intelligence.
[10 Marks]
Game playing
Artificial Intelligence in games is slowly getting better. With the advent of games like
HalfLife and Unreal, even the notoriously dumb AI-engines in first-person shooters are
gradually getting more and more intelligent! Is it due to neglect that games have taken so
long to get half-intelligent enemies?
Speech recognition
In the 1990s, computer speech recognition reached a practical level for limited purposes.
Thus United Airlines has replaced its keyboard tree for flight information by a system using
speech recognition of flight numbers and city names.
Understanding natural language
Just getting a sequence of words into a computer is not enough. Parsing sentences is not
enough either. The computer has to be provided with an understanding of the domain the text
is about, and this is presently possible only for very limited domains.
Computer vision
The world is composed of three-dimensional objects, but the inputs to the human eye and
computers' TV cameras are two dimensional. Some useful programs can work solely in two
dimensions, but full computer vision requires partial three-dimensional information that is not
just a set of two-dimensional views.
Expert systems
A “knowledge engineer” interviews experts in a certain domain and tries to embody their
knowledge in a computer program for carrying out some task.
Heuristic classification
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One of the most feasible kinds of expert system given the present knowledge of AI is to put
some information in one of a fixed set of categories using several sources of information. An
example is advising whether to accept a proposed credit card purchase.
Minimax Trees and Alpha-Beta Pruning
Moving on to another genre of games completely - board games. Board gaming AI has
received a huge amount of publicity since the famous chess match between Deep Blue (IBM's
master chess computer) and Kasparov - the first time a chess world champion has been
beaten by a machine.
b)
What is the Turing’s Test? How does it work?
[10 Marks]
The famous mathematician and co-founder of computer science Alan Turing has proposed a
test for deciding if a given computer program can “think” or not. In his test, a human
interrogator communicates with two subjects (a human and a computer) via a Teletype in
order to decide which is which. Both subjects try to convince him that they are the humans.
The computer program passes the test if at the end the interrogator fails to make a decision or
decides wrongly. Turing's test can be seen as two things: As a philosophical comment on
“other minds problem” or as a practical test for attributing intelligence of computer programs.
Turing starts by moving the argument onto his terrain. Whether a machine can think clearly
depends on what we mean by “thinking”, and he lays out his criterion: “thinking” means
playing the imitation game as effectively as a human. It is a behaviorist criterion, which rules
out any sort of consciousness as a condition of thinking.
Turing next makes clear what he means by “machine”, essentially introducing readers to the
digital computer and its capabilities.
c)
Explain about Heuristic Classification.
[5 Marks]
Heuristic classification
One of the most feasible kinds of expert system given the present knowledge of AI is to put
some information in one of a fixed set of categories using several sources of information. An
example is advising whether to accept a proposed credit card purchase.
Question 2
a)
What are intelligent computers? Explain the different characteristics of
intelligent computers.
[10 Marks]
Intelligent computers must be able to reason; however, to be effective, reason may require broad
knowledge about the real world. Humans know a great deal about the world, and they take this
knowledge for granted when they think and communicate. Ideally, we want computers not only to
mirror our extensive contextual knowledge of the real world, but also to have much more in-depth
information at their disposal about virtually any subject. Using this knowledge, intelligent computers
could answer our questions, rapidly solve complex and specialized problems, and create new
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knowledge. Intelligent computers should be linked to a worldwide computer network so that they can
instantly access remote databases and other sources of knowledge.
The ability to understand written and verbal communication is another necessary skill.
As long as intelligent computers lack certain essential human characteristics, they represent a
very powerful extension of current computing technology, but they remain soulless machines.
They are likely to have a strong, transformational impact on human society, but they are
unlikely to raise fundamental moral questions related to their very existence.
b)
Briefly discuss the social impact that artificial intelligence may have on society.
[10 Marks]
Artificial intelligence (AI) will be a transforming technology because it will allow old things
to be done in a dramatically different way-whether cheaper, faster, or simply better. There are
a lot of social impacts AI like computerization and natural language processing, machine
translation, expert systems and the overall effect of AI applications on employment. It is
concluded that AI applications are likely to develop in an evolutionary sequence rather than
through one or more sudden breakthroughs. However, the sum of the changes which will
result from the sequence of these suboptimal systems will almost certainly transform a wide
range of human activities.
c)
How do you understand “parsing” in Artificial Intelligence
[5 Marks]
Having a grammar isn't enough to parse natural language - you need a parser. The parser should
search for possible ways the rules of the grammar can be used to parse the sentence - so parsing can
be viewed as a kind of search. In general there may be many different rules that can be used to
“expand” or rewrite a given syntactic category and the parser must check through them all, to see if
the sentence can be parsed using them. For example, in our mini-grammar above there were two rules
for noun_phrases: a parse of the sentence may use either one or the other.
To parse a sentence we need to search through all these possibilities, effectively going
through all possible syntactic structures to find one that fits the sentence. There are good
ways and bad ways of doing this, just as there are good and bad ways of parsing
programming languages. One way is basically to do a depth first search through the parse
tree.
Question 3
a)
What is meant by “Rules”? Discuss the limitations of rules as a form of
knowledge representation.
[10 Marks]
Of all possible kinds of knowledge systems, rule-based systems are the most popular. They
are appealing because rules are the simplest of all forms of knowledge representation to
understand and to use.
However, rules are not perfect. They lack variation and they are unstructured. The format is
inadequate or inconvenient to represent many types of knowledge, or to model the structure
of a system. Their lack of variability in expressing knowledge also limits the representation
of causal knowledge, partly because too many rules and too much effort is required to get all
the effects of a causal model.
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b)
What are the major advantages of a human over a robot? What are the
managerial implications of robotics' limitations?
[10 Marks]
The major advantage of human over robot is simple “Humans can do everything and
anything, human’s can think and feel but robots performs only specific action of a human not
all the things human can do.
Limitations:
There are numerous technical hurdles encountered when implementing AI in a
robot, many of which are being researched today.
The ability to see, hear, and touch can be implemented through cameras,
infrared and ultrasound sensors, collision sensors, and other devices. While
implementing these physical sensors is relatively simple, making the robot
make sense of this information can be quite difficult.
c)
Define the term ‘neural networks’.
[5 Marks]
Neural networks are an entirely different paradigm in computing. They are based on
replicated the functions and structure of the human/animal brain. The term 'Neural network'
in fact refers to a biological term, and the correct computer term is Artificial Neural Network
(or ANN). ANNs attempt to model the functions of the brain - thus its only natural to start off
looking at the brain! Biological neurons receive input, perform some operation on them, and
output them to the tens, hundreds, thousands, perhaps millions of connecting neurons. The
neurons receive their input through the dendrites from the synapses of other connected
neurons. This information is passed to the soma, which processes the information. It then
passes it to the axon and synapses, and the process starts again.
Question 4
a)
Differentiate between the three main types of reasoning that exist.
[15 Marks]
 Deduction
A, A=>B conclude B
(This is read as: A is true. If A is true then B is true. Therefore conclude that B is true)
For example:
I hit the glass with a hammer
Hitting a glass with a hammer => glass breaks
Conclude: The glass is broken
This is a sound form of reasoning. Given that proposition A is true and that the truth of
proposition B is based only on whether A is true or not, B can be very reliably inferred
from the truth or otherwise of A.
 Abduction
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B, A=>B conclude A
(This is read as: B is true. If A is true then B is true. Therefore conclude that A is true)
Taking the previous example this states:
The glass is broken
Hitting a glass with a hammer => glass breaks
Conclude: I hit the glass with a hammer
This is a reasonable deduction but not necessarily true. Someone else may have hit the
glass with a hammer or maybe I threw the glass onto the floor. This is “jumping to
conclusions” which is not sound though we do it all the time. There are many situations
where more than one reason for something happening could be true and we have to
choose the most likely one. In other words, we perform “informed guesswork”!
 Induction
A E S ^ red(A)
D E S ^ red(D)
F E S ^ red(F)
Conclude: For all x where x is an element of set S x is red
(These read as: A is an element of the set S and A is red, D is an element of the set S and
D is red, F is an element of the set S and F is red.)
Example:
(Fred, Derek and Charles are all men.)
Fred is a man and Fred is TALL.
Derek is a man and Derek is TALL.
Charles is a man and Charles is TALL.
Conclude from the three examples: All men are TALL.
This is not sound. Making such a deduction from so small a sample is statistically
unsound yet we do this all the time too! Induction is only safe if it is statistically sound,
that is, there is a sufficiently large sample and that set of samples is truly randomly
selected. Theories must be based on statistically sound samples but they are nevertheless
induced.
b)
List the different methods of knowledge representation.
[5 Marks]
Rules
Frames
Semantic Nets
Symbolic
Uniform Representation
c)
List and describe briefly the basic needs of knowledge representation methods.
[5 Marks]
More representational power
We need knowledge representation systems that are better at conveying the temporal and spatial
knowledge.
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Focus of attention
We need to get a right focus of attention. The structure and context side of knowledge are always a
problem - how to store it and in what size chunks?
Fast associative search
As knowledge bases get larger and more complex we need to develop more powerful search
algorithms that will allow the thinking part of the system to keep up with the amount of knowledge it
has.
Better procedural attachment
Many knowledge representation systems have no ability to do particular actions as response to
changes in the knowledge base. They have no temporal knowledge or at least no ability to act on it if
they have it. Frames begin to address this problem but need more work.
Powerful inference strategies
These need developing to be able to create more knowledge from that already available to the
system.
So KR systems are reasonably good today but there is plenty of room for improvement.
Question 5
a)
What does the term ‘Expert System’ mean? What are the components of expert
system?
[10 Marks]
Expert Systems are computer programs that are derived from a branch of computer science
research called Artificial Intelligence (AI). AI's scientific goal is to understand intelligence by
building computer programs that exhibit intelligent behavior. It is concerned with the
concepts and methods of symbolic inference, or reasoning, by a computer, and how the
knowledge used to make those inferences will be represented inside the machine.
Components:
The knowledge base of expert systems contains both factual and heuristic knowledge. Factual
knowledge is that knowledge of the task domain that is widely shared, typically found in textbooks or
journals, and commonly agreed upon by those knowledgeable in the particular field.
Knowledge representation formalizes and organizes the knowledge. One widely used representation is
the production rule, or simply rule. A rule consists of an IF part and a THEN part (also called a
condition and an action).
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b)
Describe four characteristics of an intelligent agent.
[7 Marks]
 Autonomous: an agent is able to take initiative and exercise a non-trivial degree of
control over its own actions. The agent can act without direct intervention by humans or
other agents and that it has control over its own actions and internal state.
 Goal-Oriented: an agent accepts high-level requests indicating what a human wants and
is responsible for deciding how and where to satisfy the request.
 Collaborative: an agent does not blindly obey commands, but has the ability to modify
requests, ask clarification questions, or even refuse to satisfy certain requests.
 Flexible: the agents actions are not scripted; it is able to dynamically choose which
actions to invoke, and in what sequence, in response to the state of its external
environment.
c)





Describe the benefits of expert systems to end users.
[8 Marks]
A speed-up of human professional or semi-professional work -- typically by a factor of
ten and sometimes by a factor of a hundred or more.
Within companies, major internal cost savings. For small systems, savings are sometimes
in the tens or hundreds of thousands of dollars; but for large systems, often in the tens of
millions of dollars and as high as hundreds of millions of dollars. These cost savings are a
result of quality improvement, a major motivation for employing expert system
technology.
Improved quality of decision making. In some cases, the quality or correctness of
decisions evaluated after the fact show a ten-fold improvement.
Preservation of scarce expertise. ESs are used to preserve scarce know-how in
organizations, to capture the expertise of individuals who are retiring, and to preserve
corporate know-how so that it can be widely distributed to other factories, offices or
plants of the company.
Introduction of new products. A good example of a new product is a pathology advisor
sold to clinical pathologists in hospitals to assist in the diagnosis of diseased tissue.
Question 6
a) Explain the term ‘machine learning’ used in AI.
[4 Marks]
Learning covers a wide range of phenomena and is characterised by improvement in behavior or
ability. It can be seen as skill refinement where people get better at many tasks simply by practising. It
can also be a knowledge acquisition process. Knowledge can be acquired in different ways, i.e. by
remembering, by taking advice, by experiencing, by observation, by discovery, and so on.
Machine learning is the ability of a computer to learn from experience. It is essential in situations
where the environment changes, standards of expertise changes and in situations where there is no
case history or historical data of any kind and learning takes place as a task is performed. Machine
learning is very important in the area of knowledge acquisition.
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b) With the help of diagram(s), explain the architecture of an expert system.[10 Marks]
The user interacts with the system through a user interface, which may use menus, natural language
or any other style of interaction). Then an inference engine is used to reason with both the expert
knowledge (extracted from our friendly expert) and data specific to the particular problem being
solved. The expert knowledge will typically be in the form of a set of IF-THEN rules. The case
specific data includes both data provided by the user and partial conclusions (along with certainty
measures) based on this data. In a simple forward chaining rule-based system the case specific data
will be the elements in working memory.
Almost all expert systems also have an explanation subsystem, which allows the program to
explain its reasoning to the user. Some systems also have a knowledge base editor, which
help the expert or knowledge engineer to easily update and check the knowledge base.
c) What do you understand by ‘pattern recognition’?
[6 Marks]
Pattern Recognition
“Pattern recognition is the research area that studies the operation and design of systems that
recognize patterns in data. It encloses sub-disciplines like discriminant analysis, feature
extraction, error estimation, cluster analysis (together sometimes called statistical pattern
recognition), grammatical inference and parsing (sometimes called syntactical pattern
recognition). Important application areas are image analysis, character recognition, speech
analysis, man and machine diagnostics, person identification and industrial inspection.”
d) What is the difference between syntax and semantics?
[5 Marks]
Syntax
These words group themselves together into phrases, in these phrases in turn combine into sentences.
This is the level of syntax.
Syntax helps us understand how words are grouped together to make complex sentences, and gives us
a starting point for working out the meaning of the whole sentence.
Semantics
The problem of how to represent the meaning of sentences is undertaken in the level of semantics.
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In general, the input to the semantic stage of analysis may be viewed as being a set of
possible parses of the sentence, and information about the possible word meanings. The aim
is to combine the word meanings, given knowledge of the sentence structure, to obtain an
initial representation of the meaning of the whole sentence.
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