Advanced Database Management System - Tutorials and Notes: CS2351 / CS61 / 10144 CS601 - ARTIFICIAL INTELLIGENCE April May 2015

Wednesday, 2 March 2016

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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