7/27/2019 AI Model QP

1/4

B.E./B.Tech. DEGREE EXAMINATION, APRIL/MAY 2011Sixth Semester

Computer Science and EngineeringCS 2351 ARTIFICIAL INTELLIGENCE

(Regulation 2008)

Time : Three hours Maximum : 100 marksAnswer 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 reasoningapproach for an AI problem?4. What are the limitations in using propositional logic to represent theknowledge base?5. Define partial order planner.6. What are the differences and similarities between problem solving andplanning?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 learningagents.Or(b) Explain AO* algorithm with an example.12. (a) Explain unification algorithm used for reasoning under predicate logicwith an example.Or(b) Describe in detail the steps involved in the knowledge Engineeringprocess.13. (a) Explain the concept of planning with state space search using suitableexamples.Or(b) Explain the use of planning graphs in providing better heuristicestimates with suitable examples.14. (a) Explain the method of handling approximate inference in BayesianNetworks.

7/27/2019 AI Model QP

2/4

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)

B.E/B.TECH DEGREE EXAMINATION, MAY/JUNE 2009

Sixth semester

(Regulation 2004)

Computer science and engineering

CS 1351ARTIFICIAL INTELLIGENCE

(Common to B.E (parttime) fifth semester regulation 2005)

Time: three hours maximum: 100 marks

Answer ALL questions

PART A- (10 x 2 = 20 marks)

1. Define ideal rational agent

2. Define a data type to represent problems and nodes.

3. How does one characterize the quality of heuristic?

4. Formally define game as a kind of search problems.

5. Joe, tom and Sam are brothers-represent using first order logic symbols.

6. List the canonical forms of resolution.

7. What is Q-learning?

8. List the issues that affect the design of a learning element.

7/27/2019 AI Model QP

3/4

9. Give the semantic representation of john loves Mary.

10. Define DCG.

PART B(5 x 16 = 80 marks)

11. (a) explain uninformed search strategies.(16)

(Or)

(b) How searching is used to provide solutions and also describe some real world problems? (16)

12. (a) describe alpha-beta pruning and its effectiveness.(16)

(Or)

(b) Write in detail about any two informed search strategies. (16)

13. (a) elaborate forward and backward chaining.(16)

(Or)

(b) Discuss the general purpose ontology with the following elements:

(i) Categories (4)

(ii) Measures (4)

(iii) Composite objects (4)

(iv) Mental events and mental objects.(4)

14. (a) explain with an example learning in decision trees.(16)

(Or)

(b) Describe multilayer feed-forward networks. (16)

15.(a) (i) list the component steps of communication.(8)

(ii) Write short notes about ambiguity and disambiguation.(8)

7/27/2019 AI Model QP

4/4

(Or)

(b) Discuss in detail the syntactic analysis (PARSING). (16)