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.

   

Unit 5 - 16 mark quest

                  

Unit 5

16 mark Questions

1. Explain in detail about Learning with Decision Tree?

2. Write about Explanation Based Learning?

3. Explain about Statistical Learning?

4. Explain in detail about Reinforcement Learning? 

5. a)    Give a brief overview on decision tree inductive learning algorithm. (16) 

6. Discuss the following:

       (i)     Passive reinforcement learning. (8)

       (ii)    Active reinforcement learning. (8)

Unit 5 - 2 mark quest

Unit 5

2 mark questions.

1. Define Learning Agent.
2. List out the 3 types of Machine Learning.
3. Define Supervised Learning.
4. Define UnSupervised Learning.
5. Define reinforcement Learning.
6. Define Decision tree
7. Define training Set
8. What is information gain.
9. Define test set.
10. What is Overfitting.
11. What is the solution for overfitting.
12. What memoization.
13. Define Bayesian learning.
14. Define MAP.
15.Define MDL.
16. What is Maximum likelihood
17.What is reward.
18.Define MDP.
19. Define Passive Learning.
20. Define Active Learning.

Unit 4 -16 mark questions.

Unit 4

 16 mark questions

1. Write in detail of basic probability.
2. Explain in detail about Bayesian Network.
3. Explain about inference in Bayesian Network & their types.
4.What is Temporal Model write in detail.
5. Explain about Hidden Markov Model.

Unit 4 - 2 mark quest

Unit 4

2 Mark Questions

 1. Define Uncertainty
 2. List out the 3 reasons why medical diagnosis fails to use FOL
 3. Define Conditional Probability & its other name.
 4. Define UnConditional Probability & its other name.
 5. Define Decision theory
 6. What are the 3 types  of random variables
 7. State the Product rule.
8. What is Kolmogorov's axioms.
9. Give an example for bayesian network
10. What is CPT.
11. Define Markov Assumption.
12. Define transition Model.
13. Define Sensor Model.
14. What are the two types of Markov Process.
15. What are the basic inference tasks.
16. Define Hidden Markov Model.

Unit 3 - 16 mark quest

 16 Mark Questions

Part B

   1. Explain in detail about
       (a) Forward State Space Search (8)
       (b) Backward State Space Search (8)
   2. Explain about Partial Order Planning with example (16)
   3. (i) Write about Planning Graph (8)
       (ii) Explain the job scheduling problem with resource constraint (8)
  4. Write in detail Conditional Planning with example. (16)
  5. Explain about Execution Monitoring & Re planning with example(16) 
  6. Explain in detail the continuous planning with example.(16)