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AKTU · B.Tech · 6th semester

AKTU Artificial Intelligence (KME062) Question Paper 2021-22

UniversityDr. A.P.J. Abdul Kalam Technical University (AKTU), Lucknow
CourseB.Tech, Semester VI
SubjectArtificial Intelligence (KME062)
ExamTheory Examination 2021-22 (even semester)
Time · Marks3 hours · 100 marks
SourceAKTU question paper, from the ABESIT question paper bank
View original PDF Other years

Note: Attempt all Sections. If require any missing data; then choose suitably.

Section A

Q1. Attempt all questions in brief · 2 × 10 = 20 marks

  1. Write a short note on Iterative Improvement Algorithm.
  2. What are the different approaches of AI?
  3. Describe First Order Logic in AI.
  4. What is resolution?
  5. Define Hidden Markov Model (HMM).
  6. What is Inductive Learning?
  7. Define Clustering.
  8. Explain term Maximum a Posteriori (MAP).
  9. Write some applications of Speech Recognition.
  10. Define Practical Natural Language Processing.

Section B

Q2. Attempt any three of the following

  1. What is Heuristic search? Give the desirable properties of Heuristic search algorithm.
  2. What is Propositional Logic? Define the various Inference Rules with the help of examples.
  3. What is Bayesian Theory? Explain the role of prior probability and posterior probability in Bayesian classification.
  4. Describe Supervised Learning and Unsupervised Learning.
  5. Define Intelligent Agents. Define the types of communicating agent.

Section C

Q3

Attempt any one part of the following

  1. Discuss the problem of Hill Climbing Algorithm.
  2. What is Problem Space? How problem can be defined as state space search?

Q4

Attempt any one part of the following

  1. Explain Unification Algorithm used for reasoning under Predicate Logic with an example.
  2. Differentiate between Forward and Backward chaining of Inference with the help of an example.

Q5

Attempt any one part of the following

  1. What are Planning graphs? Explain the methods of planning and acting in the real world.
  2. Explain the method of handling Approximate Inference in Bayesian Network.

Q6

Attempt any one part of the following

  1. Illustrate Decision Trees Technique using suitable example.
  2. Define Reinforcement Learning (RL). Write its applications.

Q7

Attempt any one part of the following

  1. Define the Architecture and Configuration Bases of Robots.
  2. What are Image- Processing Operations? Define how Vision can be used for navigation and manipulation.

Other years

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