CS2351 ARTIFICIAL INTELLIGENCE APRIL/MAY 2011 ANNA UNIVERSITY QUESTION PAPER QUESTION BANK IMPORTANT QUESTIONS 2 MARKS AND 16 MARKS

CS 2351 ARTIFICIAL INTELLIGENCE APRIL/MAY 2011 ANNA UNIVERSITY QUESTION PAPER QUESTION BANK IMPORTANT QUESTIONS 2 MARKS AND 16 MARKS

CS-2351 ARTIFICIAL INTELLIGENCE APRIL/MAY 2011 ANNA UNIVERSITY QUESTION PAPER QUESTION BANK IMPORTANT QUESTIONS 2 MARKS AND 16 MARKS

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

Sixth Semester

Computer Science and Engineering

CS 2351 — ARTIFICIAL INTELLIGENCE

(Regulation 2008)

Time : Three hours Maximum : 100 marks

Answer ALL questions

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?

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)