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ARI711S - ARTIFICIAL INTELLIGENCE - 1ST OPP - JUNE 2024 |
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nAmlBIA
unlVERSITY
oFsmnce
Ano TECHnOLOGY
FACULTY OF COMPUTING AND INFORMATICS
DEPARTMENT OF SOFTWARE ENGINEERING
QUALIFICATION: BACHELOROF COMPUTER SCIENCE(SOFTWARE DEVELOPMENT)
QUALIFICATION CODE: 07BACS
LEVEL: 7
COURSE:ARTIFICIAL INTELLIGENCE
COURSECODE: ARl711S
DATE: JUNE 2024
SESSION:THEORY
DURATION: 3 HOURS
MARKS: 100
EXAMINER:
MODERATOR
FIRSTOPPORTUNITYEXAMINATION QUESTIONPAPER
MR ISAAC MAKOSA & MR IMMANUEL KANDJABANGA
MS PAULINA SHIFUGULA
THIS QUESTION PAPERCONSISTSOF 6 PAGES
(Including this front page)
INSTRUCTIONSTO STUDENTS:
1. Read all the questions, passages, scenarios, etc., carefully before answering.
2. All questions must be answered in the Answer Booklet. Clearly indicate the question number
for each answer.
3. Please, ensure that your writing is legible, neat and presentable.
4. There are no books, notes or any other additional aids allowed in the examination.
5. Use the allocated marks as a guideline when answering questions.
6. Looking at other students' work is strictly prohibited.
PERMISSIBLEMATERIALS
1. Calculator
2. Ruler
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Question 1
Select whether the following statements are either TRUE or FALSE.
1.1 Greedy search can take longer to terminate than uniform-cost search.
(10 marks)
(2 marks)
1.2 Backtracking algorithms are guaranteed to find a solution for any CSPwith a solution.
(2 marks)
1.3 Doubling your computer's speed allows you to double the depth of a tree search given
the same amount of time.
(2 marks)
1.4 The Bellman equation is a key formula used in solving MDPs to determine the optimal
value function and policy.
(2 marks)
1.5. Arc consistency is a stronger form of consistency checking compared to forward
checking.
(2 marks)
Question 2
(6 marks)
Explain the difference between planning problems and identification problems?
Question 3
(6 marks)
Backtracking algorithms explore a search space by making choices and potentially
backtracking from them. How can filtering be used to improve the efficiency of backtracking
in general?
Question 4
(10 marks)
Explain the key concepts related to solving Markov Decision Processes (MDPs)?
Question 5 - Machine Learning
(15 marks)
Machine learning algorithms can be categorised based on the type of data they use and the
learning approach they employ. Explain the fundamental differences between supervised
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learning, unsupervised learning, and reinforcement learning and provide examples of
common algorithms or applications associated with this learning paradigm.
Supervision:
(3 marks)
Learning Goal:
(3 marks)
Data Interaction:
{3 marks)
Examples (Provide 2 example for each in the table below)
Supervised Learning
Unsupervised Learning Reinforcement Learning
Question 6 - Model-based learning
(5 marks)
Model-based learning offers a powerful approach for decision-making in complex
environments. However, achieving optimal performance often requires balancing
exploration and exploitation.
Explain the following concepts in the context of model-based learning?
Exploration vs. Exploitation Dilemma:
Question 7 - Game Search
{10 marks)
Consider the game tree shown below. Assume the top node is a max node. The labels on the
arcs are the moves. The numbers in the bottom layer are the values of the different
outcomes of the game to the max player.
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Max
Min
Max
7.1. What is the value of the game to the max player?
(2 marks)
7.2. What first move should the max player make?
(2 marks)
7.3. Assuming the max player makes that move, what is the best next move for the min
player, assuming that this is the entire game tree?
(2 mark)
7.4. Using alpha-beta pruning, consider the nodes from right to left, which nodes are cut
off? Circle the nodes that are not examined on the game tree below
(4 marks)
Max
Min
Max
Question 8 - Tree Search
(15 marks)
Consider the tree shown helow. The numbers on the arcs are the arc lengths; the numbers
near states B, C, and D are the heuristic estimates; all other states have a heuristic estimate
of 0.
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Assume that the children of a node are expanded in alphabetical order when no other order
is specified by the search, and that the goal is state J. No visited or expanded lists are used.
What order would the states be expanded by each type of search. Write only the sequence
of states expanded by each search.
Search Type
Breadth First
Depth First
Uniform Cost Search
Greedy Best-First Search
A* Search
List of States
Question 9 - Constraint Satisfaction Problem (CPS)
(15 marks)
Show the sequence of variable assignments during backtracking with forward checking (BT
+ FC), assume that the variables are examined in numerical order and the values are
assigned in the order shown next to each node. Show assignments by writing the variable
number and the value, e.g. 1R. Show each step to the solution.
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Question 10 - Perceptron
(8 marks)
The following table shows a data set and the number of times each point is misclassified
during a run of the perceptron algorithm, starting with zero weights. What is the equation
of the separating line found by the algorithm, as a function of x1, x2, and x3? Assume that
the learning rate is 1 and the initial weights are all zero.
Use the equation below
X1 X2 X:l y ti1m..'Smisclm;sificd
2 3 1 ·I 1
12
2 4 0 f1
0
:~ 1 1 -1
3
1 I 0 -1
6
1 2 I -1
11
- END OF QUESTION PAPER -
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