University Question Paper April/MAY 2011

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)

Important 16 mark questions.

UNIT   I - V

1. Explain the following uninformed search strategies.

                 (i)      Iterative deepening depth-first search.    
        (ii)     Bidirectional search.

                (iii)    Depth limited search.

                (iv)     Breadth first search.  

2. Discuss any two uninformed search methods with examples.                    

3. Explain the following search strategies.

                 (i)     Min-Max search procedure.

                (ii)    A*(AO*) search.

               (iii) Alpha beta Pruning Algorithm. 

4.   (i)  Explain how a constraint satisfaction problem (CSP) may be solved.

      (ii)  Describe any one algorithm for determining    optimal moves in an adversarial search (games).

5. i) Describe the general process of knowledge engineering. 

   (ii) Discuss the syntax and semantics of first-order logic.

6. Explain the resolution procedure in detail.

7. Explain the Unification used for reasoning under predicate logic with Eg.

8. Describe the forward chaining and backward chaining algorithm with suitable example.

9. Explain the concept of planning with state space search using suitable examples.

10. Explain the use of planning graphs in providing better heuristic estimates with suitable  Eg.

11. (i) Explain the Hidden Markov Models.

    (ii)Explain about inference in temporal Models.

12. Explain the method of handling approximate inference in Bayesian networks.

13.Give a brief overview on decision tree inductive learning algorithm.

14.Discuss the following:

    (i)     Passive reinforcement learning.

   (ii)    Active reinforcement learning.

15.(i)  Outline the different forms of learning.

     (ii) Explain the main points of explanation based learning (EBL).Indicate how prior knowledge is used in this technique.

16. Explain the concept of learning using decision tree & Neural network approach.