University of Kerala - Old Question Paper Collection
Course: MCA Degree
FIFTH SEMESTER MCA DEGREE SECOND SERIES EXAMINATION, 2014
(2006 Admn.) 06.405.1: ARTIFICIAL INTELLIGENCE.
Time : 3 Hours
Max. Marks : 100
PART A
Answer all questions. Each carries 4 marks.
1. Define Artificial Intelligence.
2. Discuss forward chaining and backward chaining.
3. Explain reinforcement learning.
4. What is semantics of quantifier?
5. Do we need an expert system? Justify your answer.
6. What is production rule?
7. What is semantics of quantifier?
8. Define conditional probability with example.
9. How to represent learning rate.
10. How to represent learning rate.
[10 x 4 = 40 Marks]
PART B
Answer any six questions. Each question carries 10 marks.
11. Explain various type of search methods with example.
12. Discuss the alpha-beta cut process.
13. How probabilistic reasoning leads to decision making.
14. Describe briefly the frame based knowledge representation by explaining various types of inheritance present in frame based systems?
15. Explain the breadth first and depth first search algorithms with an example.
16. Compare supervised learning and un-supervised learning.
17. Describe briefly the frame based knowledge based representation by explaining various types of inheritance present in the frame based systems. [6 x 10 = 60 Marks]
Course: MCA Degree
FIFTH SEMESTER MCA DEGREE SECOND SERIES EXAMINATION, 2014
(2006 Admn.) 06.405.1: ARTIFICIAL INTELLIGENCE.
Time : 3 Hours
Max. Marks : 100
PART A
Answer all questions. Each carries 4 marks.
1. Define Artificial Intelligence.
2. Discuss forward chaining and backward chaining.
3. Explain reinforcement learning.
4. What is semantics of quantifier?
5. Do we need an expert system? Justify your answer.
6. What is production rule?
7. What is semantics of quantifier?
8. Define conditional probability with example.
9. How to represent learning rate.
10. How to represent learning rate.
[10 x 4 = 40 Marks]
PART B
Answer any six questions. Each question carries 10 marks.
11. Explain various type of search methods with example.
12. Discuss the alpha-beta cut process.
13. How probabilistic reasoning leads to decision making.
14. Describe briefly the frame based knowledge representation by explaining various types of inheritance present in frame based systems?
15. Explain the breadth first and depth first search algorithms with an example.
16. Compare supervised learning and un-supervised learning.
17. Describe briefly the frame based knowledge based representation by explaining various types of inheritance present in the frame based systems. [6 x 10 = 60 Marks]
0 comments:
Pen down your valuable important comments below