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UNIVERSITI TENAGA NASIONAL

College of Computer Science and Information Technology

BACHELOR OF INFORMATION TECHNOLOGY (HONS.)

BACHELOR OF COMPUTER SCIENCE (HONS.)

FINAL EXAMINATION

SEMESTER I 2018/2019

ARTIFICIAL INTELLIGENCE

(CSNB234)

Solution September 2018

Time allowed: 2.5 hours + 10 minutes for reading

INSTRUCTIONS TO CANDIDATES

1. The total marks for this exam is 100.

2. There are THREE (3) SECTIONS to this paper: Sections A, B and C.

3. Answer ALL questions in the answer booklet provided.

DO NOT OPEN THIS QUESTION PAPER UNTIL YOU ARE INSTRUCTED TO DO SO

THIS QUESTION PAPER CONSISTS OF 12 PRINTED PAGES INCLUDING THIS PAGE

of 12 Semester I 2018/2019 Artificial Intelligence

SECTION A: MULTIPLE CHOICE (20 QUESTIONS, 30 MARKS)

Instruction: Please select the BEST answer from the given choices.

1. “Given that, P implies Q; If Q is proven true, we may conclude P.” The above statement

describes which of the following reasoning methods?

(A) Deductive Reasoning

(B) Abductive Reasoning

(C) Inductive Reasoning

(D) Common-sense Reasoning

2. “Given that, P implies Q; If P is proven true, we may conclude Q.” The above statement

describes which of the following inference rules?

(A) Modus Ponendo Ponens

(B) Modus Ponendo Tollens

(C) Modus Tollendo Ponens

(D) Modus Tollendo Tollens

3. “Given that, P implies Q; If Q is not true, we may conclude P is also not true.” The

above statement describes which of the following inference rules?

(A) Modus Ponendo Ponens

(B) Modus Ponendo Tollens

(C) Modus Tollendo Ponens

(D) Modus Tollendo Tollens

4. Which of the following is NOT a term associated with heuristics approach of problem

solving?

(A) Non-algorithm

(B) Symbolic

(C) Accuracy

(D) Rule of thumb


5. Which of the following well-formed formulas are horn clauses?

(I) p

(II) p q

(III) p q

(IV) p q r

(A) II only

(B) III only

(C) II and IV

(D) I and II

6. Which of the following is NOT an essential feature of logic programming languages

(such as Prolog)?

(A) Support fast calculation

(B) Ability to backtrack

(C) Ability to propagate constraints

(D) Support symbolic computation

7. All of the following are true about the roles of an inference engine, EXCEPT

(A) It perform reasoning

(B) It controls the searching process

(C) It supports explanation and result justification

(D) It supports database query

8. Which of the following predicate logic is the correct answer for the below English

statement:

“Nobody likes taxes”

(A) X people(X) likes(X, taxes)

(B) X people(X) likes(X, taxes)

(C) X people(X) likes(X, taxes)

(D) X people(X) likes(X, taxes)


9. Which of the following predicate logic is the correct answer for the below English

statement:

“Suresh passed examination but he is not happy.”

(A) pass_exam(suresh) happy(suresh)

(B) pass_exam(suresh) happy(suresh)

(C) pass_exam(suresh) happy(suresh)

(D) X pass_exam(X) happy(X)

10. Which of the following English statement represents the answer for the below predicate

logic:

“X music(X) listen_to(azizah, X) listen_to(azimah, X)”

(A) Azimah and Azizah listen to some music

(B) Azizah listens to all types of music that Azimah listens

(C) Azimah listens to all types of music that Azizah listens

(D) Azizah and Azimah likes all music

11. Which of the following English statement represents the answer for the below predicate

logic:

“X student(X) smoke(X)”

(A) Not all students smoke

(B) All students smoke

(C) Some students smoke

(D) No student smokes

12. A semantic network stores knowledge as

(A) Records

(B) If-then rules

(C) Scenes

(D) Nodes and links


13. Which of the following is TRUE about production rules?

(A) It has episodes and entry-conditions

(B) It contains links between nodes to represent the relationships

(C) It consists of conditions and conclusion

(D) It is similar to predicate calculus

14. A story-based Script contains the following components, EXCEPT

(A) Episodes

(B) Linked nodes

(C) Entry conditions

(D) Propositions and roles

15. In a production rule system, which of the following are the characteristics of backward

chaining?

(I) It is data-driven

(II) It is goal-driven

(III) It is used to predict an outcome

(IV) It is used to determine the cause once something has occurred

(A) I only

(B) II only

(C) I and III

(D) II and IV

16. Which of the following is NOT a term associated with fuzzy logic?

(A) Chromosome

(B) Defuzzification

(C) Linguistic variables

(D) Membership function


17. Genetic algorithms perform ___________________.

(A) Theorem proving

(B) Symbolic computation

(C) Backward chaining

(D) Optimization

18. A multi-layer neural network has ______________.

(A) Maximum one hidden layer

(B) One or more hidden layers

(C) Just one input layer, no others

(D) No hidden layer

19. Which of the following is NOT associated with a sigmoid function in artificial neural

networks.

(A) It gives neurons real-valued outputs

(B) It is a type of activation function

(C) The function has a characteristic of U-shape curve

(D) It is a smooth function

20. In supervised learning:

(A) The algorithms are known but not the inputs

(B) The network is being control by the user

(C) Both the inputs and the desired outputs are known

(D) None of the above


SECTION B: SHORT ANSWER QUESTIONS (8 QUESTIONS, 30 MARKS)

Instruction: Answer ALL questions.

Question 1

Write meaningful sentences that relate these THREE (3) words: chromosome, genes, and

allele.

[3 marks]

Chromosomes are paired long chains within a cell nucleus that are composed of genes. Allele is the

value. Genes mutate and can take two or more alternative forms. An allele is one of these forms of a

gene.

Question 2

In Genetic Algorithm implementation, describe Roulette Wheel Selection. Use a diagram

to illustrate its use.

[5 marks]

It is one of the chromosome selection techniques. Each chromosome is given a slice of the circular

roulette wheel. The area of the slice within the wheel is equal to the chromosome fitness ratio. To select

a chromosome for mating, a random number is generated in the interval [0, 100], and the chromosome

whose segment spans the random number is selected. [4 marks]

Diagram of the wheel – [1 mark]

Question 3

Summation function plays an important role in machine learning algorithms such as the

perceptron. Briefly explain the role of a summation function in perceptron.

[4 marks]

An artificial neuron is a mathematical function conceived as a model of biological neurons, a neural

network. Usually each input is separately weighted, and the sum is passed through a non-linear

function known as a summation or transfer function.

Question 4

What are hidden nodes in a neural network?


[3 marks]

Hidden nodes are found in hidden layers which are additional to the input and output nodes. They are

located in between the input and output layers. Hidden nodes are not connected externally.

Question 5

Write brief notes on certainty factor theory.

[4 marks]

In expert systems with certainty factor, the knowledge base consists of a set of rules that have the

following syntax:

If <evidence>

Then <hypothesis> (c.f. = n)

Where, c.f. represents belief in hypothesis H given that evidence E has occurred. The certainty factor

is based on two functions: measure of belief (MB) and measure of disbelief (MD).

Question 6

Besides Hill-climbing, Best-first is also a popular heuristic search. How does it differ from

hill-climbing?

[4 marks]

Hill-climbing search focuses on the best child nodes but not the best node in the entire partial search

list. As a result it is “short-sighted” in terms of selecting the best available node. Best-first search will

“re-consider” the parent nodes which were non-promising earlier but if they are found more promising

(nearer to the goal node) than the new children nodes they will be used again. Hence an overall best

node discovered so far will be explored each time. With this measure, the overall best in the partial list

for traversing use is guaranteed.

Question 7

Give any TWO (2) primary objective of Heuristic search.

[3 marks]

Provide good enough solution, fast search.

Based on rule-of-thumb such that non-promising search space (branches) can be reduced.


Question 8

Write brief notes on A* search. Give the top level formula for computing the strength of a node

in the * search.

[4 marks]

A* Search algorithm is one of the best and popular technique used in path-finding and graph

traversals. A* Search algorithms, has “brains”. This is achieved by calculating the strength of

h(n)and g(n). g(n) represents the remaining/estimated cost and h(n) represents the cost-so-far, in

the top level formula: f(n) = h(n) + g(n). This is a typical generate and test search, till an optimal

answer is found.


SECTION C: PROBLEM SOLVING (2 QUESTIONS, 40 MARKS)

Instruction: Answer ALL questions.

Question 1

Automated reasoning and modus systems of inference rules formed the basis for artificial

intelligence research. Parts (a) – (d) are questions related to problem solving using a variety of

established reasoning and theorem proving methods.

(a) Suppose you are given the following knowledge-base [(1) – (4)]. Prove that r t is true.

Show each application of inference rules clearly.

(1) (r (s t)) ((r s) (r t))

(2) (s t) (r (s t))

(3) s t

(4) r s

[8 marks]

From premises (2) & (3), we get r (s t) ……. (5)

From (1) & (5), we get (r s) (r t) …….. (6)

From (6) & (4), we get (r t) …………. (7)

Therefore, the Goal r t is proven true. [2 marks each step = 8 marks]

(b) Suppose you are given the rule hot_weather drink_water, explain how Modus

Tollendo Tollens is used to draw a conclusion (i.e. to return an answer).

[4 marks]

Given the rule: hot_weather drink_water [1 mark]

In Modus Tollendo Tollens, whenever we see people NOT drinking water, we will conclude that

the weather is NOT hot. [3 marks]

(c) Describe Inductive Reasoning with an example. Is this reasoning method reliable?

Explain your answer.

[4 marks]

Inductive reasoning involves the learners detecting, or noticing, patterns and working out a

'rule' for themselves, and use the pattern to make generalization. [2 marks]

Not producing reliable answer all the time. [1 mark]

Since the logic used is inductive, not by deduction. [1 mark]


(d) Give an example to illustrate the usage of Abductive Reasoning in a rule-based system.

Will abductive reasoning guarantee a correct conclusion?

[4 marks]

It is a form of logical inference which starts with an observation or set of observations then

seeks to find the simplest and most likely explanation. Also called inverse application of a rule

(when Q true, we conclude P). [3 marks]

No, the answer is not always reliable. [1 mark]

Question 2

An expert system is a computerized clone of a human expert. It is domain-specific and can

explain its reasoning. Provide adequate discussion and judgement to the following parts related

to various components and functionality in an expert system.

a) Explanation module (or facility) and inference engine are two important components

of a typical Expert System. Briefly describe the roles of these two components in the

context of an Expert System.

[6 marks]

Explanation module – provide justification towards a solution. It handles “How” and “Why”

questions of the users. This module uses run-time paraphrasing approach to generate an

explanation note. [3 marks]

Inference engine – it determines the reasoning approach, searching methods, decides how

inferencing can be conducted, and what conclusion to return. [3 marks]

b) List any TWO (2) generic categories of expert systems. Provide TWO (2) common

characteristics in the categories you named above.

[6 marks]

Diagnosis, Repair, Design and Diagnosis. Any two answers. [2 marks]

Generic categories are those domains that its problems are solved with no general rules or

equation. [2 marks]

For each problem, it tends to generate multiple solutions, i.e. no absolute answer. Based on

rule-of-thumb. [2 marks]

c) R1 – R5 are production rules of an expert system that uses Forward Chaining method.

Suppose “a low inflation rate” and “develop saving habits” are given as the starting facts,


apply forward chaining to determine whether to buy or not to buy a house. Show clearly

the sequence of rules fired, and briefly explain the match & fired process.

R1: IF inflation is low

THEN interest rates are low

ELSE interest rates are high

R2: IF interest rates are high

THEN housing prices are high

R3: IF interest rates are low

AND some savings in bank account

THEN buy it

R4: IF housing prices are high

THEN do not buy a house

R5: IF develop savings habits

THEN some savings in bank account

[8 marks]

Rule fired: R1, R5, R3.

The forward chaining engine will first fires R1 (since the condition is true). This deduces “low interest

rate”. Forward chaining continues to R5 since the rest of the rules are not satisfied of their

conditions/premises. This action asserted the fact “some savings in bank account”. Then it backtracks

to R3, and confirms that the potential buyer should get a house. The search/match/fire cycle stopped

and the system makes decision about “to buy a house”.

Correct sequence [6 marks]

Explanation [2 marks]

---End of Questions---

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