ARTIFICIAL INTELLIGENCE–Nov / Dec 2011 Question Paper

Anna University

B.E./B.Tech. DEGREE EXAMINATION, NOVEMBER/DECEMBER 2011.

Sixth Semester

Computer Science and Engineering

CS 2351 — ARTIFICIAL INTELLIGENCE

(Common to Seventh Semester – Electronics and Instrumentation Engineering)

(Regulation 2008)

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Time : Three hours

Maximum : 100 marks

Answer ALL questions.

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PART A — (10 × 2 = 20 marks)

1. What is a rational agent?

2. State the significance of using heuristic functions?

3. Distinguish between predicate and propositional logic.

4. What factors justify whether the reasoning is to be done in forward or backward reasoning?

5. Distinguish between state space search and plan space search.

6. Define partial order planning.

7. List two applications of Hidden Markov model.

8. What are the logics used in reasoning with uncertain information?

9. Define Inductive learning.

10. Distinguish between supervised learning and unsupervised learning.

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PART B — (5 × 16 = 80 marks)

11. (a) Explain AO* algorithm with a suitable example. State the limitations in the algorithm.

Or

(b) Explain the constraint satisfaction procedure to solve the cryptarithmetic problem.

12. (a) Consider the following facts

Team India

Team Australia

Final match between India and Australia

India scored 350 runs Australia score 350 runs India lost 5 wickets Australia lost 7 wickets

The team which scored the maximum runs wins

If the scores are same then the team which lost minimum wickets wins the match.

Represent the facts in predicate, convert to clause form and prove by resolution

"India wins the match".

Or

	(b)	Analyse the missionaries and Cannibals problem which is stated as follows. 3 missionaries and 3 cannibals are on one side of the river
		along with a boat that can hold one or two people. Find a way to get everyone to the other side, without leaving a group of missionaries in one place out numbered by the cannibals in that place.
		(i) Formulate a problem precisely making only those distinctions necessary to ensure a valid solution. Draw a diagram of the complete state space.
		(ii) Design appropriate search algorithm for it.
13.	(a)	Explain the concept of planning with state space search. How is it different from partial order planning?

Or

(b) What are planning graphs? Explain the methods of planning and acting in the real world.

14. (a) Explain the concept of Bayesian network in representing knowledge in an uncertain domain.

Or

(b) Write short notes on : (i) Temporal models

(ii) Probafilistic Reasoning.

15. (a) Explain in detail learning from observation and explanation based learning.

Or

(b) Explain in detail statistical learning methods and reinforcement learning.

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ARTIFICIAL INTELLIGENCE–April / May 2011 Question Paper

Anna University

B.E./B.Tech. DEGREE EXAMINATION, APRIL/MAY 2011

Sixth Semester

Computer Science and Engineering

CS 2351 — ARTIFICIAL INTELLIGENCE

(Regulation 2008)

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Time : Three hours
Maximum : 100 marks

Answer ALL questions

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PART A — (10 × 2 = 20 marks)

1. List down the characteristics of intelligent agent.
2. What do you mean by local maxima with respect to search technique?
3. What factors determine the selection of forward or backward reasoning
approach for an AI problem?
4. What are the limitations in using propositional logic to represent the
knowledge base?
5. Define partial order planner.
6. What are the differences and similarities between problem solving and
planning?
7. List down two applications of temporal probabilistic models.
8. Define Dempster-Shafer theory.
9. Explain the concept of learning from example.
10. How statistical learning method differs from reinforcement learning method?

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PART B — (5 × 16 = 80 marks)

11. (a) Explain in detail on the characteristics and applications of learning agents.
Or
(b) Explain AO* algorithm with an example.

12. (a) Explain unification algorithm used for reasoning under predicate logic with an example.
Or
(b) Describe in detail the steps involved in the knowledge Engineering process.

13. (a) Explain the concept of planning with state space search using suitable examples.
Or
(b) Explain the use of planning graphs in providing better heuristic
estimates with suitable examples.

14. (a) Explain the method of handling approximate inference in Bayesian Networks.
Or
(b) Explain the use of Hidden Markov Models in Speech Recognition.

15. (a) Explain the concept of learning using decision trees and neural network approach.
Or
(b) Write short notes on :
(i) Statistical learning. (8)
(ii) Explanation based learning. (8)

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