Anna University
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 reinforcementlearning 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)
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 reinforcementlearning 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)
No comments:
Post a Comment