UNIVERSITY EXAMINATION 2021/2022
YEAR IV SEMESTER I EXAMINATION FOR THE DEGREE OF BACHELOR
OF TECHNOLOGY IN ELECTRONIC AND COMPUTER ENGINEERING
ECE 2420: Artificial Intelligence and Logic Year IV Semester I
Date: 2022
Time: 11.00am – 1.00pm
INSTRUCTIONS
-
Answer Question one (compulsory) and any other two questions
Question One (30 marks)
a) Explain the meaning of expert system and outline its three components.
(5 marks)
b) Discuss the two main approaches used in designing a knowledge-based agent
(4 marks).
c) Differentiate between an expert system and a conventional computer system.
(4 marks)
d) Describe your understanding of Rough set theory in regard to knowledge
discovery
e) Define and explain how a perceptron works.
(3 marks).
(4 marks)
f) State the purpose of defuzzyfication? Name at least one method used for
defuzzyfication.
(3 marks)
g) A 4-input neuron has weights 1, 2, 3 and 4. The transfer function is linear with a
constant of proportionality equal to 2. The inputs are 4, 10, 5 and 20 respectively.
Find its output
(3 marks).
h) What is the reason that logic function has rapidly become one of the most
successful technology for developing sophisticated control systems?
(4 marks).
Question Two (20 marks)
Use Table 1 and Figure 1 to answer questions 2a, 2b and 2c.
Consider the following fuzzy expert system for weather forecast:
Page 1 of 3
Table 1: Fuzzy expert system for weather forecast
The following two plots represent the membership functions of two fuzzy variables
describing the position of the arrow of barometer (left) and the direction of its
movement (right):
Figure 1: Plots for membership functions
The air pressure is measured in millibars, and the speed of its change in millibars per
hour. Answer the following questions:
a) How much is the arrow Down, Up or in the Middle if it indicates that the pressure
is 1020 millibars? Use membership functions on the graphs.
(3 marks)
b) How much is the arrow moving Down or Up if the pressure changes —2 millibars
every hour?
(2 marks)
c) Using the membership values found above and confidences of the rules in the
table calculate the degree of confidence when the sky is clear or cloudy.
(9 marks)
d) A knowledge-based agent can be viewed at different levels. Explain the three
levels of viewing knowledge-based agents
(6 marks).
Question Three (20 marks)
Use Figure 2 below of a single artificial neuron (unit) to answers questions 3(a), 3(b),
3(c), and 3(d):
Page 2 of 3
Figure 2: Single unit with tree inputs
The node has three inputs 𝑥 = (𝑥1 , 𝑥2 , 𝑥3 ) that receive only binary signals (either 0 or
1).
a) Compute the number of different input patterns this node can receive?
(3 marks)
b) Compute the number of different input patterns if the neuron had four inputs?
(3 marks)
c) Compute the number of different input patterns if the neuron had four inputs?
(3 marks)
d) Based on the findings from 3(a), 3(b), and 3(c) above, give a formula that
computes the number of binary input patterns for a given number of inputs?
(3 marks)
e) Using a diagram, illustrate the working of a knowledge-based agent
architecture.
(8 marks)
Question Four (20 marks)
a) What are the major tasks executed by a fuzzy rule-based system? Describe these
tasks in your own words.
(12 marks)
b) Consider the following real variables from everyday life:
(i) Income measured in Kenya shillings (Ksh).
(ii) Speed measured in meters per second.
(iii) A TV show measured in how much you are interested watching it.
(iv) A meal measured in how much you like to eat it.
(v) A traffic light measured in what color is on.
In each case, suggest a fuzzy variable corresponding to these real variables.
For which of these five variables the use of a fuzzy variable is not really necessary?
Why?
(8 Marks)
Question Five
a) Describe the working of Mamdani fuzzy rule systems.
(8 marks)
b) Discuss the two rules used by inference system to generate new facts, so that the
agents can update the knowledge base (KB).
(12 marks)
Page 3 of 3