Genetic Algorithm MCQs

1.     Produces two new offspring from two parent string by copying selected bits from each parent is called

(A) Mutation

(B) Inheritance

(C) Crossover

(D) None of these

Ans: C

2.     Genetic operators includes

(A) Crossover

(B) Mutation

(C) Both A & B

(D) None of these

Ans: C

3.     What is the correct representation of GA?

(A) GA(Fitness, Fitness_threshold, p)

(B) GA(Fitness, Fitness_threshold, p, r )

(C) GA(Fitness, Fitness_threshold, p, r, m)

(D) GA(Fitness, Fitness_threshold)

      Ans: C

4. When would the genetic algorithm terminate?

(A) Maximum number of generations has been produced

(B) Satisfactory fitness level has been reached for the population.

(C) Both A & B

(D) None of these

Ans:  C

5.     Genetic algorithm is a

(A) Search technique used in computing to find true or approximate solution to optimization and search problem

(B) Sorting technique used in computing to find true or approximate solution to optimization and sort problem

(C) Both A & B

(D) None of these

Ans:  A

6.     GA techniques are inspired by _________ biology.

(A) Evolutionary

(B) Cytology

(C)  Anatomy

(D)  Ecology

Ans: A

7.     _________ evolution over many generations was directly influenced by the experiences of individual organisms during their lifetime

(A) Baldwin

(B) Lamarckian

(C) Bayes

(D) None of these

Ans: B

8. ILP stand for

(A) Inductive Logical programming

(B) Inductive Logic Programming

(C) Inductive Logical Program

(D) Inductive Logic Program

Ans: B

9. What is/are the requirement for the Learn-One-Rule method?

(A) Input, accepts a set of +ve and -ve training examples.

(B) Output, delivers a single rule that covers many +ve examples and few -ve.

(C) Output rule has a high accuracy but not necessarily a high coverage.

(D) All of the above

Ans: D

10. _________ is any predicate (or its negation) applied to any set of terms.

(A) Literal

(B) Null

(C) Clause

(D) None of these

Ans: A

11. Ground literal is a literal that

(A) Contains only variables

(B) does not contains any functions

(C) does not contains any variables

(D) Contains only functions

 Ans: C

 Reinforcement Learning MCQs

1.     _________________ is a type of machine learning in which an agent learns to make decisions by interacting with an environment.

 

A. Supervised learning

B. Reinforcement learning

C. Semi-supervised learning

D. Unsupervised learning

Ans: B

 

2. Which of the following are applications of Reinforcement learning?

 

A. Robotics

B. video games

C. self-driving cars

D. All of the above

Ans: D

 3._________________ is defined as when an event, occurs due to a particular behavior, increases the strength and the frequency of the behavior.

 

A. Positive Reinforcement

B. Negative Reinforcement

C. Both A and B

D. None of the above

Ans : A

 

4. Which type of feedback does an agent in reinforcement learning receive?

A. Predictions
B. Labels
C. Clusters
D. Rewards or penalties

Ans: D

 5. ______________ is the Q-learning algorithm used for in reinforcement learning.

 

A. Image recognition

B. Clustering

C. Finding the optimal decision-making strategy

D. Natural language processing

Ans: C

 6. Which of the following are Advantages of reinforcement learning?

 

A. Maximizes Performance

B. Sustain Change for a long period of time

C. Too much Reinforcement can lead to an overload of states which can diminish the results

D. All of the above

Ans: D

 7.  In Reinforcement learning, the decision is made on the initial input or the input given at the start

  A. TRUE

B. FALSE

C. Can be true or false

D. Can not say

 Ans : B

 8. Which of the following is true about reinforcement learning?

 A. The agent gets rewards or penalty according to the action

B.    The agent navigates in an environmental building the example experiences or the training dataset.

C.    The target of an agent is to maximize the rewards and minimize the penalty.

D.    All of the above

Ans: D

 9. Q-Learning algorithm is off-policy algorithm because

A.    Policy being trained is exactly the one being executed

B.    Policy being trained is not necessarily the one being executed.
There is nothing called Q-Learning algorithm but Q-Value Iteration

D.    None of the above

Ans: B

 10. In Reinforcement learning decision is dependent, So we give labels to sequences of dependent decisions.

 

A. TRUE

B. FALSE

C. Can be true or false

D. Can not say

Ans: A

  WEEK 1 : b) Program to calculate the entropy and the information gain

 WEEK 1 : a) Write a program using scikit-learn to implement K-means Clustering

 WEEK 1 :  c) Program to implement perceptron

SVM

ARTIFICIAL INTELLIGENCE SYLLABUS

 ARTIFICIAL INTELLIGENCE

UNIT I

Introduction to Artificial Intelligence:

Introduction, History, Intelligent Systems, Foundations of AI, Applications, Tic- tac toe game playing, development of AI languages, Current trends in AI

Problem Solving:

State-Space Search and Control Strategies: Introduction, General Problem Solving, Characteristics of Problem, Exhaustive searches, heuristic search techniques, iterative deepening a*, Constraint Satisfaction

UNIT II

Problem Reduction and Game Playing: Introduction, problem reduction, game playing, alpha-beta pruning, two-player perfect information games Logic Concepts: Introduction, propositional calculus, proportional logic, natural deduction system, axiomatic system, semantic tableau system in proportional logic, resolution refutation in proportional logic, predicate logic

UNIT III

Knowledge Representation: Introduction, Approaches to Knowledge representation, Knowledge representation using Semantic Network, Extended Semantic Network for KR, Knowledge representation using Frames 

Advance Knowledge Representation Techniques: Introduction, Conceptual Dependency Theory, Script Structure, CYC Theory, Case Grammars, Semantic Web

UNIT IV

Expert Systems:

Expert Systems, Phases in building expert systems, Expert System Architecture, Rule Based Systems, Forward Chaining, Blackboard Systems, Blackboard architecture, Blackboard System vs Rulebased system, Truth maintenance system.

UNIT V

Uncertainty measure & Fuzzy sets and fuzzy logic:

Uncertainty measure: probability theory: Introduction, probability theory, Bayesian belief networks, certainty factor theory, dempster-shafer theory Fuzzy. 

sets and fuzzy logic: Introduction, fuzzy sets, fuzzy set operations, types of membership functions, multi valued logic, fuzzy logic, linguistic variables and hedges, fuzzy propositions, inference rules for fuzzy propositions, fuzzy systems.

Text Books:

1. Artificial Intelligence by Elaine Rich, Kevin Knight and Shivashankar B Nair, Tata McGraw Hill.

2. Introduction to Artificial Intelligence and Expert Systems by Dan W. Patterson, Pearson Education.

Reference Books:

1. Artificial Intelligence: A Modern Approach by S. Russell and P. Norvig, Prentice Hall