CS2351 / CS61 / 10144 CS601 - ARTIFICIAL INTELLIGENCE April May 2015

Anna University Questions - CS2351 / CS61 / 10144 CS601 - ARTIFICIAL INTELLIGENCE April may 2015, Computer Science and Engineering, Sixth semester, Regulation 2008/2010, Previous year university exam question papers

Exam	B.E/B.Tech. (Full Time) DEGREE END SEMESTER EXAMINATIONS
Academic Year	April May 2015
Subject Code	CS2351 / CS61 / 10144 CS601
Subject Name	Artificial Intelligence
Branch	Computer Science and Engineering
Semester	Sixth Semester
Regulation	2008/2010

B.E / B.Tech. (Full Time) DEGREE END SEMESTER EXAMINATIONS, APRIL / MAY 2015

Computer Science and Engineering

Sixth Semester

CS2351 / CS61 / 10144 CS601 - ARTIFICIAL INTELLIGENCE

(Regulations 2008/2010)

Time : 3 Hours                      Answer A L L Questions                Max. Marks 100

PART-A (10 x 2 = 20 Marks)

1. Define Ideal Rational agents.

2. Why problem formulation must follow goal formulation?

3. Differentiate forward chaining and backward chaining.

4. What is the use of online search agent in unknown environment?

5. Define partial order planner.

6. What are the differences and similarity in problem solver and planner?

7. List down the applications of Bayesian network.

8. Define uncertainty. How it is solved?

9. What are the methods of statistical learning?

10. State the advantages of Inductive learning.

Part-B (5* 16 = 80 Marks)

11. (a) (i) Explain any two informed search strategies. (10)

(ii) Discuss about constraint satisfaction problem. (6)

Or

(b) Explain the following uninformed search strategies;

(i) Depth first search (6)

(ii) Iterative Deepening Depth First Search. (6)

(iii) Bidirectional search (4)

12. (a) Explain forward chaining and backward chaining algorithms with an example.

Or

(b) (i) Illustrate the use of first order logic to represent knowledge. (10)

(ii) Write short note on unification. (6)

13. (a) Explain the concept of planning with state space search using suitable examples. (16)

Or

(b) Explain the use of planning graph in providing better heuristic estimation with suitable example. (16)

14. (a) (i) Explain the inference in temporal models. (10)

(ii) Write short note on Hidden Markov model. (6)

Or

(b) Explain about the exact inference in Bayesian networks. (16)

15. (a) The following table consists of training data from an employee database. The data have been generalized. Let status be the class label attribute. Construct decision tree from the given data. (16)

Department	Age	Salary	Count	Status
Sales	31..35	46K..50K	30	Senior
Sales	26..30	26K..30K	40	Junior
Sales	31..35	31K..35K	40	Junior
Systems	21..25	46K..50K	20	Junior
Systems	31..35	66K..70K	5	Senior
Systems	26..30	46K..50K	3	Junior
Systems	41..45	66K..70K	3	Senior
Marketing	36..40	46K..50K	10	Senior
Marketing	31..35	41K..45K	4	Junior
Secretary	46..50	36K..40K	4	Senior
Secretary	26..30	26K..30K	6	Junior

Or

(b) Explain in detail about Active and Passive reinforcement learning. (16)

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