CENTRAL COLLEGE OF ENGINEERING AND MANAGEMENT

Question Bank
Artificial Intelligence & Expert systems
CSE VIII Semester
Unit-I
2 Marks:
1. Define AI.
2. What is an AI technique?
3. What is state space search?
4. Write any two differences between informed search and blind search.
5. Define heuristic search.
6. What is production system?
7. Write down the major components of AI?
8. What is conflict resolution?
9. Define game of chance.
10. Write down the complexity of breadth first search.
7 Marks questions:
1. What is Artificial Intelligence and Artificial Technique? Briefly explain how A1
Technique can be represented.
2. Enumerate Classical Water jug Problem. Describe the state space for this problem.
3. What is production system? Explain it with an example. Discuss the Characteristics of a
production system.
4. How to define a problem as state space search? Discuss it with the help of an example.
5. Define the following problems. What types of control strategy is used in the following
problem.
I.
The lower of Hanoi
II.
Crypt arithmetic
III.
The Missionaries and cannibals problems
IV.
8-puzzle problem
6. Discuss the following search Technique with the help of an example. Also discuss the
benefits and shortcoming of each.
I.
Breadth First Search.
II.
Depth First Search.
7. Explain Turing Test with suitable diagram.
8. What are task domains of artificial intelligence? Explain with example.
9. What is knowledge? Give the difference between data and knowledge. With the help of
block diagram. Explain the components of knowledge based system.
10. Find a good heuristic function for following.
a. Monkey and Banana problem.
b. Travelling Salesman problem.
11. Explain the difference between Data, knowledge, belief and hypothesis.
12. Define the following problems. What types of control strategy is used in the following
problem.

a.
b.
c.
d.
e.

The lower of Hanoi

Cryptarithmetic
The Missionaries and cannibals problems
The Missionaries and cannibals problems
8-puzzle problem

UNIT-II
2 Marks:
1. Give examples for heuristic search
2. Define knowledge
3. Mention the types of knowledge.
4. What is monotonic reasoning?
5. What is predicate logic?
6. What is mean-end analysis?
7. Define And-OR graph.
8. What are the various types of informed search?
9. When A* is optimal?
10. Define branch and bound algorithm.
11. What is A* search?
12. What is non-monotonic reasoning?
13. Name any two AI languages.
7 Marks questions:
1. Define the heuristic search. Discuss benefits and short comings.
2. Discuss any four from the following heuristic search techniques. Explain the algorithm
with the help of an example.
a. Hill Climbing
b. Best First Search
c. The A* Algorithms.
d. Problem Reduction
e. The AO* Algorithms.
f. Constraints Satisfaction.
g. Means End Analysis.
3. Solve the following Crypt arithmetic problem using constraints satisfaction search
procedure.
a. CROSS + ROADS = DANGER
b. SEND + MORE = MONEY
4. Explain briefly the difference between procedural and declaration knowledge.
5. Discuss various approaches and issues in knowledge representation. Also discuss various
problems in representing knowledge.
6. Explain the algorithm of predicate logic resolution.
7. Write unification algorithm and explain resolution in predicate logic.

8. Explain Non Monotonic reasoning and discuss various logic associated with it.
9. Explain the difference between forward and backward reasoning and under what
conditions each would be best to use for given set of problem.
10. Explain constraint satisfaction problem with an example.
11. Give an example where depth first search is better than breadth first search.
12. Explain best first search.
13. Differentiate between breadth first search and breadth first search.
14. Write backward and forward chaining system with suitable examples.
15. Differentiate between semantic net and partitioned sematic ne.t
16. Write A* algorithm and explain how it is used to find minimal cost path.
17. Explain Minimax Search procedure. In Addition to Alpha Beta what are the modifications
to the minimax procedure that can improve its procedure.
18. Write short note on:
a. Logic programming
b. Forward and backward reasoning.
c. Unification algorithm.
d. Logic programming.
19. Construct CD representation of the following:
i.
John pushed the bike.
ii.
John ate ice cream with spoon.
iii.
John went to market from home.
iv. The plants grow.
20. The unicorn is a mammal if it is horned. If the unicorn is either immortal or a mammal,
then it is horned. If the unicorn is mythical, then it is immortal, but if it is not mythical,
then it is a mortal mammal. Using resolution prove that unicorn is mammal.
UNIT III
2 Marks:
1. What is resolution principle?
2. What is probability reasoning?
3. How logical reasoning is differing with probabilistic reasoning ?
4. What is reasoning under uncertainty?
5. What is certainty factor?
6. Define rule based system.
7. What is the difference between the two quantifiers in the logics?
8. What are the issues for knowledge representation?
9. Define fuzzy logic.
10. What is fuzzy function?
7 Marks questions:
1. Explain and prove the Bayes Theorem. What is meant by conditional probability?
2. Explain frames.

3.
4.
5.
6.

Explain Resolution algorithm with suitable example.

Explain Unification Algorithm with suitable example.
Explain CD with suitable examples.
Consider the following sentences
i.
John likes all kinds of food.
ii.
Apples are food.
iii.
Chicken is food.
iv. Anything anyone eats and isnt killed by its food.
v. Bill eats peanuts and is still alive.
vi.
Sue eats everything Bill eats.
(a) Translate these sentences into formulas in predicate logic.
(b) Prove that John likes peanuts using backward chaining.
(c) Convert the formulas of part a into clause form.
(d) Prove that John likes peanut using resolution.
(e) Use resolution to answer the question, what food does Sue eat?

7. Assume the following fact:

i.
Steve only likes easy courses.
ii.
Sciences courses are hard.
iii.
All the courses in the basket weaving department are easy.
iv. BK301 is a basket weaving course.
Use resolution to answer the question, what course would Steve like.
8. Describe Augmented Transition networks.
9. Explain semantic analysis.
10. Explain supervised and un-supervised learning.
UNIT IV
2 Marks:
1. Define planning system.
2. What is linear planning?
3. Define non-linear planning.
4. Write a program in lisp to find the maximum of two numbers.
5. What are Lisp manipulation functions?
6. Define Skolemization.
7. What is script?
8. Define certainty factor.
9. Give examples for forward chaining and backward chaining
10. What is parsing?
7 Marks questions:
1. Discuss the following.
a. Component of planning System.

b. Non linear planning.

2. Write Short note on Non linear planning using constraints posting
3. Consider and solve the following block word problem.
Start on (C,A) goal on (A,B)
On table (A) on (B,C)
On table (B)
Armepty
4. What are the Steps in natural Language processing? List and explain them briefly.
5. Explain the various forms of learning.
6. Write a short note on AI languages.
7. Write short note on :
a. Grammars and parsers.
b. Top-down versus bottom up parsing
8. Explain partial order planning.
9. Write notes on machine translation.
10. Distinguish between forward planning and backward planning.

UNIT V
2 Marks:
1. What is expert system shell?
2. What is learning?
3. Mention the types of learning.
4. Define rote learning.
5. What is inductive learning?
6. Give examples for explanation based learning.
7. What is knowledge acquisition?
8. What is LISP?
9. Write any two differences between LISP and PROLOG.
10. What is backtracking?
7 Marks questions:
1. Discuss any two from the following.
a)
Learning in problem solving.
b)
Learning from example.
c)
Explanation based Learning.
2. Explain the basic architecture of an expert system. Also give its applicability it different
areas with suitable examples.
3. Write short notes on.
a)
DENDRAL.
b)
MYCIN.

4. How is the learning process in a decision tree?

5. Explain the various methods of logical formulation in logical learning?
6. How are explanation based learning done?
7. Elaborate upon inductive learning.
8. What are the different types of learning?
9. Discuss rule based expert system architecture.
10. Explain the components of ES.
11. Draw the block diagram of expert system and explain the architecture.
12. Write a short note on Explanation based learning.
13. Write a short note on Rote learning.