Thursday, May 5, 2016

ANU B.Tech CS IT Artificial Intelligence April 2016 Question Paper

University/ Board: Acharya Nagarjuna University
B Tech Computer Science and Engineering
[Total No. of Questions : 09]
CS/IT 324 (CR)
III/IV B.Tech Degree Examinations, March - April 2016
Second Semester
CS/IT
Artificial Intelligence
Time : 3 hours
Maximum Marks : 70

Answer question No.1 Compulsory
Answer ONE question from each Unit

1. Answer the following [7 x 2 = 14M]
a) Define Artificial Intelligence
b) Means-Ends analysis
c) Static evaluation function in minimax procedure
d) Cryptarithmetic puzzle
e) Comment on propositional Vs first-order inference
f) Learning
g) Decision tree

UNIT - I [1 x 14 = 14M]

2. a) Discuss the areas of application of Artificial Intelligence.
2. b) Explain the design issues in the design of search programs. (OR)
3. Discuss the following in detail.
a) Hill Climbing
b) Best First Search
c) Constraint satisfaction

UNIT - II [1 x 14 = 14M]

4. a) Discuss with examples procedural versus Declarative representations. Which is advantageous? Give reasons.
4. b) What is default logic? Explain with an example. (OR)
5. a) Design a set of primitives (node and arcs) that could be used to construct a semantic net representation of information about food and cooking that would make it possible to interpret the information about recipe.
5. b) Explain in detail about Representational Adequacy, Inferential adequacy, Inferential efficiency and Acquisitional efficiency.

UNIT - III [1 x 14 = 14M]

6. a) What are the three general approaches to Natural Language Processing?
6. b) Describe systemic grammars and semantic grammars. (OR)
7. a) Explain Non-linear planning using constraint posting.
7. b) Write a brief on reactive systems.

UNIT - IV [1 x 14 = 14M]

8. Write short notes on the following:
a) Decision trees in learning
b) Riddle of the utility problem
c) Rote learning-checker's game (OR)
9. a) Write a brief on Expert system shells.
9. b) Write a brief on Explanation based learning
9. c) What do you mean by incremental learning.

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