-
7/30/2019 AI-SHEET
1/4
Course : Artificial Intelligence
Semester : 1st semester 2005/2006
Course Instructor : Dr. Nabil Hewahi
Exercise Sheet
1. Define the following terms
AI Expert System Knowledge Based System Machine Learning
Heuristic Search
2. Distinguish between
Hill climbing and Generate and test search techniques
OR graph and AND-OR graph
3. What is the difference between A* and AO*
4. Propose a heuristic function for the water jug problem.
5. Given the following figure
Assume the starting node is R and the goal is F apply A*
algorithm
6. Given the following figure
7.8.
1
R5 2
P Q
3 6 6 159 7
A B C D E3
3 3 8 1
F
R
P Q
A B C D E
5
F 4 G 2 H 6 I 1
Assume the starting node is R. Using AO* show the solution
path
direction
-
7/30/2019 AI-SHEET
2/4
9. What we mean when we say Some solution steps can be ignored
or undone
10. What is the minimax algorithm and when should we use it.
11. Propose a heuristic function for the nim game.
12. For the TIC-TAC-TO, given the heuristic function E(n) =
m(n)-O(n), where
M(n) is the total number of possible winning for me and O(n) is
the total of wins
to the opponent. E(n) is the total evaluation for state n. If we
explore
a) 5 levels for the node
b) 7 levels for the node
Show the solution path applying minimax algorithm.
13. Consider the following tree
a) Suppose A is the minimum player, what is the first move that
should be
chosen by him.
b) What nodes would not need to be examined using Alpha-Beta
pruning
procedure.
14. Is the minimax algoritm depth first search or breadth first
search.
15. What is the difference between propositional calculus and
predicate calculus.
16. Consider the following sentences
John likes all kinds of food
Apples are food
Chicken is food
Anything anyone eats and it is not killed by is food
Bill eats peanuts and is still alive
Sue eats everything Bill eatsa. Translate these sentences into
formulas in predicate logic
2
Min A
B C D
E F G H I J5 2 9 6
K L
4 M N
1 5
O P
8 7
-
7/30/2019 AI-SHEET
3/4
b. Convert the formulas of steps a into clause form.
c. Prove that John likes peanuts using resolution
d. Use resolution to answer the question What food does Sue
eat?
17. What is the difference between monotonic and non-monotonic
reasoning.
18. In what applications TMS is considered to be a useful
tool.
19. Using MYCIN s rules for inexact reasoning, compute CF, MB,
and MD of
h1 given three observations where
MB(h1,O1) = 0.5 MB(h2,O2) = 0.3 MB(h1,O3)=0.2
20. What are the requirements to use Bayes theorem .
21. What is the difference between semantic nets and frames
(explain with
examples).
22. What is expert system tool.
23. Support facilities is one of the key features of ES tools
discuss this
statement.
24. Can we design ES using a certain tool that supports both
forward and
backward chaining of inference.
25. a)What is fuzzy logic and what is the advantage of
membership functions.
b)Can you state some applications where fuzzy logic can be
used.
c) What is the difference between KAS and Prospector.
d) Apply specific to general search of the version space
learning the concept
table. The provided examples are :
+ : obj (small, brown,table)
+ : obj(large,brown,table)
+: obj(large,white,table)-:obj(small,green,table)
e) Apply the candidate elimination algorithm learning concept
brown table.
The provided examples are
+ : obj(small,brown,table)
+ : obj(large,brown,table)
-: obj(small,yellow,chair)
-:obj(large,brown,cube)
f) What are the alternate terms for neural networks
3
-
7/30/2019 AI-SHEET
4/4
g) Given the following data
X1 x2 output
1.0 1.0 1
7.2 3.5 15.2 2.1 -1
3.3 3.4 -1
8.1 2.3 1
2.1 1.3 -1
Apply few steps to modify the weights using the perceptron to
classify the output.
29. Can we solve XOR problem using interpolation net, if yes
apply it ( construct the
net), if no why ?
30. Can we use backpropagation algorithm in two layers neural
network.
31. Apply Entropy measures homogenetiy of examples to construct
the decision tree,
then extract the rules for the following database.
Day Outlook Temp. Hum. Wind PlayTennis
D1 Sunny Hot High Weak No
D2 Sunny Hot High Strong No
D3 Overcast Hot High Weak Yes
D4 Rain Mild High Weak Yes
D5 Rain Cool Normal Weak YesD6 Rain Cool Normal Strong No
D7 Overcast Cool Normal Strong Yes
D8 Sunny Mild High Weak No
D9 Sunny Cool Normal Weak Yes
D10 Rain Mild Normal Weak Yes
D11 Sunny Mild Normal Strong Yes
D12 Overcast Mild High Strong Yes
D13 Overcast Hot Normal Weak Yes
D14 Rain Mild High Strong No
32. What is the GA and define the necessary genetic
operators.
33. What is the classifier system.
35. Using TMS, if a group of people is planning to make a trip,
the system is going to
make a compromise between all the participants to choose the
free day for all. The
chosen day has to be warm and either Monday, Tuesday or
Wednesday. Show some
of the nodes that might be produced by the system in case of
contradiction and how
the system is going to resolve the problem. (assume some
starting nodes)
4
