Download ICT619-06-PoolOfExamQuestions

ICT619 Intelligent Systems
Final examination S2, 2006
Some of the questions for the exam have been selected from the following list (Note: there may be
some differences in the actual wordings)

How does soft computing differ from traditional computing?

Describe the soft computing paradigm in terms of its defining characteristics. Give examples of
soft computing methodologies possessing these characteristics.

What is the goal of traditional artificial intelligence (AI), and how successful has it been?

State the defining characteristics of human intelligence along with examples of soft computing
methodologies that attempt to exhibit these.

Define an intelligent system in the context of business applications.

Suppose you have decided to solve a business problem in your organisation by developing an
artificial neural network. Write a brief report stating all possible justifications for this decision.

Draw a diagram to show the structure of a multilayer perceptron. Describe how learning takes
place in this type of artificial neural network with references to the concept of an error landscape.

Describe the difference between the multiplayer perceptron and the Kohonen network in terms of
their structure and operation.

Both expert systems and artificial neural networks attempt to mimic humans to solve problems.
Discuss in detail the fundamental differences between these two methodologies.

Discuss the concept of a solution surface. Why are artificial neural networks called universal
approximators?

Discuss the two main types of learning in artificial neural networks.

Discuss the advantages of artificial neural networks.

How does a rule-based expert system differ from a conventional computer program? What are the
advantages offered by an expert system?

Describe the two approaches used by the inference engine in an expert system for reasoning with
rules.

Describe the role of the knowledge engineer in the development of an expert system. What are the
prerequisites, if any, for being a knowledge engineer?

Is an expert system adaptive? Discuss this issue and any difficulties with making it so.

Suppose you have decided to solve a business problem in your organisation by developing an
expert system. Write a brief report stating possible justifications for this decision.

Draw a diagram to show the structure of an expert system.

Describe how the rule-base is used in an expert system’s reasoning process. Mention the forward
and backward chaining strategies in your description.

Mention three limitations of expert systems.

Define a linguistic variable with an example. Write an example fuzzy IF-THEN rule and explain
how it is interpreted.

.What is fuzzification? Use a diagram to explain how a crisp variable may be fuzzified.

What is defuzzification? Describe a popular defuzzification method.

Describe a fuzzy model for problem solving and how fuzzy reasoning works to infer the value of
an output variable.

What advantages do fuzzy rule-based systems offer over conventional rule-based systems?

What is the difference between a fuzzy control system and a fuzzy decision support system?

Explain with examples, the min-max rules used in fuzzy inferencing.

Describe how fuzzy sets are designed for the implementation of a fuzzy model.

What are the main steps in the development of a fuzzy expert system?

What are the main advantages of genetic algorithms (GA)?

Describe the problem solving strategy used in genetic algorithms. Explain the terms fitness
function, crossover and mutation in your description.

What are the important factors that determine the suitability of a problem for a genetic algorithmbased solution?

How do the crossover and mutation operators work in a GA?

Explain the roulette wheel selection technique used in GA.

Suppose the solution to a given problem is represented by 3 variables – of which, the first one, X,
is binary, the second one, Y, is a numerical value in the range 0 to 10, and the third one, Z, is a
symbolic value representing any one of three possible categories a, b or c. Assuming solutions are
coded as bit strings, show an example of a candidate solution.

Describe how genetic algorithms can be used in hybrid intelligent systems involving artificial
neural networks and fuzzy systems.

In what way(s) is case-based reasoning similar to problem solving by humans?

Describe the operation of a case-based reasoning (CBR) system.

The development of a CBR system involves reduced knowledge acquisition effort. Explain why.

Distinguish between the transformational analogy and derivational analogy approaches to case
reuse in a CBR system.

What are the applications of a decision tree?

Describe, with the aid of a diagram, the structure and operation of a decision tree used for
classification.

When is the application of a decision tree appropriate for a data mining application?

When and why is it necessary to prune decision trees, and how can pruning be done?

Briefly describe four ways in which data mining expertise may be utilised in business.

What is the significance of clustering in data mining?
Describe the k-means clustering algorithm used in data mining.

What is clustering? Explain, with an example, how it can be useful to businesses.
How does clustering differ from classification?

Under what circumstances would it be appropriate for a company to hire outside experts for data
mining projects?

Describe how an artificial neural network can be used for clustering data records for data mining
applications. What are the advantages of such clustering compared with k-means clustering?

Describe the defining characteristics of intelligent agents. Discuss the possible areas of
application for them, including e-commerce.

Describe an approach to natural language processing (NLP) based on pattern matching. How does
it compare with other more recent approaches?

Define the field of language technology in terms of its application, and the nature of inputs and
outputs of a language technology system.

Identify the current five main approaches to natural language processing and briefly describe the
principles of each.

What is an anaphoric reference and why is it a problem for natural language processing?
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