Course : Artificial Intelligence

```Course : Artificial Intelligence
Semester : 1st semester 2005/2006
Course Instructor : Dr. Nabil Hewahi
Exercise Sheet
1.
Define the following terms
AI
Expert System
Heuristic Search
2.
Knowledge Based System
Machine Learning
Distinguish between
Hill climbing
OR graph
and
and
Generate and test search techniques
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
R
5
2
P
Q
3
6
9
B
A
6
15
7
D
C
E
3
3
3
8
1
F
Assume the starting node is R and the goal is F apply A* algorithm
6.
Given the following figure
R
P
A
Q
B
C
D
E
5
F 4 G2
H6
I1
Assume the starting node is R. Using AO* show the solution path
direction
1
7.
8.
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
Min
A
B
E
5
C
F
2
D
G
H
9
K
I
J
6
L
4
M
1
O
8
N
5
P
7
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
a.
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 eats
Translate these sentences into formulas in predicate logic
2
b.
c.
d.
Convert the formulas of steps a into clause form.
Prove that John likes peanuts using resolution
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.
20.
21.
22.
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
What are the requirements to use Bayes theorem .
What is the difference between semantic nets and frames (explain with
examples).
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)
d)
What is the difference between KAS and Prospector.
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
g)
Given the following data
X1
x2
output
1.0
7.2
5.2
3.3
8.1
2.1
1.0
3.5
2.1
3.4
2.3
1.3
1
1
-1
-1
1
-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
D1
D2
D3
D4
D5
D6
D7
D8
D9
D10
D11
D12
D13
D14
Outlook
Sunny
Sunny
Overcast
Rain
Rain
Rain
Overcast
Sunny
Sunny
Rain
Sunny
Overcast
Overcast
Rain
Temp.
Hot
Hot
Hot
Mild
Cool
Cool
Cool
Mild
Cool
Mild
Mild
Mild
Hot
Mild
Hum.
High
High
High
High
Normal
Normal
Normal
High
Normal
Normal
Normal
High
Normal
High
Wind
Weak
Strong
Weak
Weak
Weak
Strong
Strong
Weak
Weak
Weak
Strong
Strong
Weak
Strong
PlayTennis
No
No
Yes
Yes
Yes
No
Yes
No
Yes
Yes
Yes
Yes
Yes
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
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