UNIVERSITI TENAGA NASIONAL
College of Computer Science and Information Technology
BACHELOR OF INFORMATION TECHNOLOGY (HONS.)
BACHELOR OF COMPUTER SCIENCE (HONS.)
FINAL EXAMINATION
SEMESTER I 2017/2018
ARTIFICIAL INTELLIGENCE
(CSNB234)
Answer Schemes
September 2017
Time allowed: 3 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: Section 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 14 PRINTED PAGES INCLUDING THIS PAGE
Page 1 of 13
Semester I 2017/2018
Artificial Intelligence
SECTION A: MULTIPLE CHOICE (20 QUESTIONS, 30 MARKS)
Instruction: Please select the BEST answer from the given choices.
1.
Which of the following is the correct answer for the below statement:
“All people like some sports”
2.
(a)
X Y people(X)  sport(Y)  likes(Y, X)
(b)
X Y people(X)  sport(Y)  likes(X, Y)
(c)
Y X people(X)  sport(Y)  likes(X, Y)
(d)
Y X people(X)  sport(Y)  likes(Y, X)
CO1
Which of the following predicate calculus is the correct answer for the below English
statement:
“X people(X)  eat_durian(X)”
3.
(a)
All people eat durian
(b)
Somebody eat durian
(c)
Not all people eat durian
(d)
Nobody eat durian
CO1
Which of the following is NOT the main concern in Artificial Intelligence (AI) problem
solving?
4.
(a)
Theorem proving
(b)
Algorithmic approach
(c)
Use of rule-of-thumb
(d)
Symbolic computation
CO1
A horn clause contains at most ______________.
(a)
One conjunctive operator
(b)
One implication sign
(c)
One negative literal
(d)
One positive literal
CO1
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Semester I 2017/2018
Artificial Intelligence
5.
“Given that, P implies Q; If Q is proven true, we may conclude P.”
The above statement describes which of the following reasoning techniques?
6.
7.
8.
9.
(a)
Abductive Reasoning
(b)
Inductive Reasoning
(c)
Default Reasoning
(d)
Deductive Reasoning
CO1
The main purpose of Alan Turing test is to find out whether
(a)
The human interrogator is intelligent
(b)
The human interrogator is able to answer questions
(c)
The machine is intelligent
(d)
The Turing test is doable
CO1
Which of the following is a characteristic of Hill-climbing search?
(a)
It can guarantee the best solution
(b)
It performs faster than A* search
(c)
It is less efficient than breadth-first search
(d)
It is similar to depth-first search
CO3
Forward chaining (reasoning) method is used in ___________________.
(a)
Rule-based expert systems
(b)
Prolog inference engine
(c)
Conceptual graph representation
(d)
Database systems
CO2
Which of the following is TRUE about Production Rules?
(a)
It contains links between nodes to represent the relationships
(b)
It consists of premises and conclusion
(c)
It is similar to logical scheme of knowledge representation
(d)
It has episodes and entry-conditions
CO2
Page 3 of 13
Semester I 2017/2018
Artificial Intelligence
10.
Paraphrasing is a technique used in modern expert system for which of the following
functions?
11.
12.
13.
(a)
Computation
(b)
Reasoning
(c)
Explanation
(d)
Planning
CO4
A Semantic Network stores knowledge as
(a)
Frames
(b)
Scripts
(c)
Multiple if-then statements
(d)
Nodes and links
CO2
Which of the following are characteristics of an Expert System?
(I)
Program calculates large amount of numbers
(II)
The system development methodology is rapid prototyping
(III)
Execution is done on a step-by-step basis
(IV)
Heuristic approach is used
(a)
II only
(b)
II and IV
(c)
II, III and IV
(d)
None of the above
CO4
Which of the following is NOT the so-called “well-defined” tasks in the context of
building an expert system.
(a)
Car engine repairing
(b)
Sickness Diagnosing
(c)
PC Troubleshooting
(d)
Calculation based on equations
CO4
Page 4 of 13
Semester I 2017/2018
Artificial Intelligence
14.
Backward reasoning provides alternate solutions through a technique called
____________.
15.
16.
17.
18.
(a)
Planning
(b)
Proofing
(c)
Backtracking
(d)
Traversing
CO4
Which of the following is NOT a term associated with Genetic Algorithms?
(a)
Backpropagation
(b)
Mutation
(c)
Genes
(d)
Crossover
CO5
Genetic algorithms perform ________________.
(a)
Machine learning
(b)
Optimization
(c)
Backward chaining
(d)
Symbolic reasoning
CO5
Which of the following is NOT an uncertainty handling methods?
(a)
Neural network
(b)
Bayes theorem
(c)
Certainty factor
(d)
Fuzzy logic
CO5
Roulette wheel technique is used in ________________ to select a chromosome for
matting.
(a)
Fuzzy systems
(b)
Neural network
(c)
Genetic algorithms
(d)
Automated reasoning
CO5
Page 5 of 13
Semester I 2017/2018
Artificial Intelligence
19.
20.
In a Neural Network, the major purpose of the summation function is:
(a)
To compute the inputs of various neurons
(b)
To compute the internal simulation level of a neuron
(c)
To adjust the value of the weights
(d)
To transform the internal stimulation level to an output of a neuron
CO5
A multi-layer Neural Network has ____________.
(a)
No hidden layer
(b)
Only one hidden layer
(c)
One or more hidden layers
(d)
Just one output layer, no others
CO5
Page 6 of 13
Semester I 2017/2018
Artificial Intelligence
SECTION B: SHORT ANSWER QUESTIONS (8 QUESTIONS, 30 MARKS)
Instruction: Answer ALL questions.
Question 1
Provide an example that shows how a Semantic Network can depict inheritance.
[3 marks]
A semantic network of 2-3 levels is needed. Show the properties can be inherited via link
association. [3 marks]
Example:
Animal
can
Breathe
can Move
can
has
Bird
Fly
Wings
has
Feathers
Canary can
is
Sing
Yellow
Question 2
Distinguish between Monotonic and Non-monotonic Reasoning.
[4 marks]
Monotonic – traditional logic is used. Assertions cannot be disapproved. Considered as
axioms. Contradict with human reasoning. [2m]
Non-monotonic – compatible with our reasoning. Any axioms may be removed/updated if a
newly arrived axiom or fact assertion form a contradiction to the existing ones. [2m]
Question 3
List any TWO (2) functions of an Inference Engine.
[3 marks]
Determine of forward or backward chaining is used in a rule-based system.
Perform searching
Draw conclusion
Provide justification
[any two, 1.5m each]
Question 4
Name ONE (1) limitation of expert systems. In what way can it be a critical limitation?
[3 marks]
Page 7 of 13
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Artificial Intelligence
Rapid degradation due to inability to acquire knowledge automatically. [1.5m]
Critical in a way that, it may cause Knowledge Acquisition difficulty. Since machine learning
through induction is still a challenge,
[1.5m]
Question 5
(a)
In Neural Network computing, describe the role of the sigmoid function.
[2 marks]
A smooth function used in NN to improve the learning process. A sigmoid function having a
characteristic "S"-shaped curve or sigmoid curve, and defined by the formula:
[2m]
(b)
Explain how neural networks learn in a supervised and in an unsupervised mode.
[4 marks]
Supervised – Input and expected outputs are available, with s Feedback loop. E.g. perceptron
and Hopfield,
[2m]
Unsupervised – Expected output not provided. The learning programs will adjust themselves
to figure out what could be the output. There is no targets to match, whatsoever. E.g. SOM and
Hebbian networks.
[2m]
Question 6
Provide your views for the future of Artificial Intelligence in Computer Science.
[3 marks]
Open-ended question. Invite students for opinions.
[3m]
Expect to read about: which research areas or techniques are particularly received attention
or have the market. And, to what domains AI may help human in the next10 years, for example.
Question 7
Give the formula for computing Certainty Factor (CF). Briefly explain the components in the
equation.
[4 marks]
The accumulation of the CF for a conclusion can be calculated, using a formula, as more
evidence. CF = MB - MD. CF is the measure of the confidence placed on a conclusion derived
from a chaining system (e.g. rule-based expert system).
Page 8 of 13
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Artificial Intelligence
MB – measure of belief (in the goal)
MD – measure of disbelief (in the goal)
[4m]
Question 8
List and explain TWO (2) application areas of Genetic Algorithms.
[4 marks]
GAs are used for optimization problem such as maximizing profits or minimizing costs/time,
etc. Some application areas are: Production Scheduling, design, financial management, etc.
Accept any correct answers other than the above. Expect to read answers that state briefly the
optimization part of the named area, such as to maximize or to minimize and upon what
variables.
[4m]
Page 9 of 13
Semester I 2017/2018
Artificial Intelligence
SECTION C: ESSAY QUESTIONS (2 QUESTIONS, 40 MARKS)
Instruction: Answer ALL questions.
Question 1
(a)
Describe briefly how a fuzzy intelligent system works in a washing machine.
[3 marks]
If a washing machine uses a fuzzy system in its operation that means the input it receives such
as the load of the laundry will be used to determine how much water is needed for the wash. A
non-fuzzy system can have a small load and large load, that will dispense let say 10 liters and
20 liters water respectively for the load, but a fuzzy system can dispense for small load a range
of 10, 11, 12 to 19 liters of water, depending on how small is the load (i.e. the CF attached to
it.) and so on for large load. Therefore the output has a range of value rather than just one
value for small and one value for big. Some show of understanding is enough, doesn't have to
be exact. [3m]
(b)
Define FOUR (4) linguistic variables for operating a washing machine, then design a
set of THREE (3) fuzzy rules for a fully automatic washing machine. All the 4 linguistic
variables must be used in each fuzzy rule.
[7 marks]
Examples of the linguistic variables needed: water_level, was_load, wash_type,
time_duration, etc. Two set of values are given here:
[1m]
Example of Linguistic values for “water_level”: [quarter, half, three_quarter, full]
Example of Linguistic values for “wash_load”: [light, medium, heavy]
[Note that there are other possible sets]
Students are expected to know which one is condition and which one to be used as the conclusion. There
are multiple solutions to the same set of linguistic variables/values defined above.
One possible set of 3 rules:
[6m]
IF wash_load IS high AND water_amount IS full THEN wash_duration IS long AND spin_speed IS fast
IF wash_load IS medium AND water_amount IS medium THEN wash_duration IS long AND spin_speed IS slow
IF wash_load IS light AND water_amount IS light THEN wash_duration IS short AND spin_speed IS medium
(c)
List TWO (2) potential benefits of expert systems. Of the benefits you listed, which
one do you consider the most important? Why?
[4 marks]
Page 10 of 13
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Artificial Intelligence
Increased availability: Expertise in a field is made available to many more people (even when
human experts are not around in the company). Top experts’ knowledge gets saved rather than
being lost, when they retire or should they have resigned.
[1.5m]
Reduced cost: The cost of providing expertise per use is greatly reduced.
[1.5m]
It is up to the students to defend their arguments
[1m]
(d)
Based on the following knowledge base (facts and rules) in Figure 2, apply forward
chaining to determine the final goal (conclusion). Provide the rule firing sequence as
well.
FACT 1:
FACT 2:
RULE 1
RULE 2:
RULE 3
RULE 4
RULE 5
RULE 6
A
C
IF A AND B THEN E
IF F THEN D
IF H THEN J
IF C AND E THEN F
IF D AND A THEN H
IF C THEN B
Figure 1
[6 marks]
Sequence of rules applied:
R6, R1, R4, R2, R5, R3, STOPPED.
[5m]
[-1 mark per wrong rule used]
Final goal:
It is “J”
[1m]
Question 2
State space search is a process used in artificial intelligence to generate a plan or solution path
to achieve a desired goal. In producing a solution path, the search strategy will explore
alternatives in the state space (normally represented as a tree) and find the sequence of steps
leading to the desired goal.
(a) Explain how a Heuristic Search can reduce the size of a search tree.
[3 marks]
Page 11 of 13
Semester I 2017/2018
Artificial Intelligence
By:
- measuring the quality of each node (which one nearer to the goal) via nodes sorting (less
nodes are considered)
- pruning inferior nodes (size reduced, thus speed up the search)
[1.5m each]
(b)
What is a Hill-climbing search? This method has three well-known drawbacks, name
and briefly describe them.
[5 marks]
Hill-climbing search – students can either give the algorithm in pseudocode, English
statements or brief description about the search, such as what’s so special or unique.
[2m]
Foothill (Local maxima) – is a peak that is lower than the highest peak in the state space. Once
on local maximum, the algorithm will halt even though the solution may be far from
satisfactory.
[1m]
Plateau – a plateau is an area of the state space where the evaluation function is essentially
flat.
[1m]
Ridges – a ridge may have steeply sloping sides, so that the search reaches the top of the ridge
with ease, but the top may slope only very gently & slowly toward a peak.
[1m]
(c)
Besides Hill-climbing, Best-first is also a popular heuristic search. How does it differ
from hill-climbing?
[3 marks]
Hill-climbing considers the best child node of the parent but not the best node in the entire
partial list. Therefore it is “short-sighted”. While in best-first search the children nodes are
arranged in such a way that they are compared to the previous parents nodes as well. Previous
parents are those less promising in the previous selection but they may be better than the newly
expanded children nodes.
[3m]
(d)
Conduct Best-first search for the following search tree in Figure 2. Your goal is 1 defect
(node K). Write down the traversing order.
[6 marks]
Page 12 of 13
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Artificial Intelligence
A
8
4
E
13
B
2
F
11
C
3
5
G
H
G
D
7
1
J
K
Figure 2
[A]
[B D C]
[F E D C]
[E D C]
[D C]
[H J C]
[J C]
Solution path: A B F E D H J
(e)
[6m]
[-1 mark per incorrect node selection]
Write brief notes on A* search. Give the top level formula for computing the strength of
a node in the * search.
[3 marks]
A* is admissible, i.e. it could guaranty the best answer if an optimal answer is available for
the searching problem. The cost function for A* is f(n) = g(n) + h(n). Where, g(n) is the costso-far (branch-and-bound); h(n) computes the estimation of the partial path from an arbitrary
node “n” leading to the goal (best-first).
[3m]
---End of Answer Schemes---
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Semester I 2017/2018
Artificial Intelligence