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ROB311H1S_2019_ARTIFICIALINTELLIGENCE_E.pdf
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ROB311H1S 2019 ARTIFICIALINTELLIGENCE E.pdfUNIVER...
ROB311H1S_2019_ARTIFICIALINTELLIGENCE_E.pdfUNIVERSITY OF TORONTO Faculty of Applied
ROB311H1S 2019 ARTIFICIALINTELLIGEN...
ROB311H1S_2019_ARTIFICIALINTELLIGENCE_E.pdfUNIVERSITY OF TORONTO Faculty of Applied
Page 1
UNIVERSITY OF TORONTO
Faculty of Applied Science and Engineering
R0B3 1 1H1 S

Artificial Intelligence
Final Exam
Instructor: Prof. Jonathan Kelly
April 18, 2019
Name:
Student Number:
Time allowed: 2 hours 30 minutes
This exam contains 14 pages (including this cover page) and 5 questions. Answer each question
in the spaces provided on the exam paper. You may use the back of the page for scratch work or
additional space, provided you indicate
clearly
whether it is part of your final solution. You are
permitted one doublesided 8.5" x 11" aid sheet only. Good luck!
Distribution of Marks
Question
Points
Score
True or False
10
Short Answers
20
Long Answer 1

TwoPlayer Games and ce0 Pruning
25
Long Answer 2

Decision Tree Learning
20
Long Answer 3

Markov Decision Processes
25
Total:
100
1
Page 2
ROB311H1 S
Artificial Intelligence
April 18, 2019
QUESTION
1:
TRUE OR FALSE
(10 points)
Clearly select either True or False only. Each question is worth 2 points.
The first AT winter was caused by the failure of expert systems to deliver on their promises.
0
True
0
False
PSPACE
c
PTIME
0
True
0
False
Classical planning (e.g., PDDL) relies on a representation lifted from propositional logic.
0
True
0
False
Object search should not be solved using active perception.
0
True
0
False
Applying policy search to a problem that involves discrete actions may cause policy 'jumps'
(discontinuities).
0
True
0
False
Page 2 of 14
Page 3
R0B311H1 S
Artificial Intelligence
April 18, 2019
QUESTION
2:
SHORT ANSWERS
(20 points)
Answer each question briefly but with
sufficient
detail.
(4 points) Define the concept of rationality. What is a rational agent?
(5 points) In the context of search, what is meant by the relaxation of a problem? Why would
one want to relax a problem? Give a very simple example.
(3 points) State the deduction theorem (n.b., a mathematical expression is acceptable).
(4 points) What is the purpose of bootstrap aggregation (bagging) and how does the process
operate (i.e., what is involved)?
(4 points) Briefly describe what is meant by
inverse reinforcement learning
(i.e., what are the
domain and the goal of IRL)? How does this differ from the problem that is solved by standard
reinforcement learning?
Page 3 of 14
Page 4
ROB311111 S
Artificial Intelligence
April 18, 2019
QUESTION
3:
LONG ANSWER
1

TWOPLAYER GAMES AND
a/3
PRUNING
(25 points)
Consider a twoplayer game between 'Maxi' (who moves first) and 'Mini'. We will analyze the
game tree using the minimax algorithm; however, Maxi happens to have the capability to use
special 'dark magic' in certain cases, as described below.
(a) (3 points) For the game tree below, fill in the values of each of the nodes in the minimax
tree. The upward pointing triangles correspond to maximizer nodes (Layer 1 and 3, moves
made by Maxi), and the downward pointing triangles correspond to minimizer nodes (Layer
2, move made by Mini). Each node has just two actions available, Left and Right.
Clearly
circle the sequence of actions (branches) that correspond to minimax play.
3
6
2
3
7
1
2
0
Page 4 of 14
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