Welcome to the Uncertain Knowledge MCQs Page

Dive deep into the fascinating world of Uncertain Knowledge with our comprehensive set of Multiple-Choice Questions (MCQs). This page is dedicated to exploring the fundamental concepts and intricacies of Uncertain Knowledge, a crucial aspect of Artificial Intelligence. In this section, you will encounter a diverse range of MCQs that cover various aspects of Uncertain Knowledge, from the basic principles to advanced topics. Each question is thoughtfully crafted to challenge your knowledge and deepen your understanding of this critical subcategory within Artificial Intelligence.

Check out the MCQs below to embark on an enriching journey through Uncertain Knowledge. Test your knowledge, expand your horizons, and solidify your grasp on this vital area of Artificial Intelligence.

Note: Each MCQ comes with multiple answer choices. Select the most appropriate option and test your understanding of Uncertain Knowledge. You can click on an option to test your knowledge before viewing the solution for a MCQ. Happy learning!

Uncertain Knowledge MCQs | Page 1 of 6

Explore more Topics under Artificial Intelligence

01

Introduction to AI

02

Intelligent Agents

03

Problem Solving

04

Game Planning

05

Knowledge and Reasoning

06

Learning

07

Perceiving and Acting

08

Natural Language Processing

09

LISP Programming

10

Expert Systems

Q1.

Uncertainty arises in the wumpus world because the agent’s sensors give only

Full & Global information

Partial & Global Information

Partial & local Information

Full & local information

Discuss

Answer: (c).Partial & local Information

Q2.

A Hybrid Bayesian network contains

Both discrete and continuous variables

Only Discrete variables

Only Discontinuous variable

Both Discrete and Discontinuous variable

Discuss

Answer: (a).Both discrete and continuous variables

Q3.

How is Fuzzy Logic different from conventional control methods?

IF and THEN Approach

FOR Approach

WHILE Approach

DO Approach

Discuss

Answer: (a).IF and THEN Approach

Q4.

If a hypothesis says it should be positive, but in fact it is negative, we call it

A consistent hypothesis

A false negative hypothesis

A false positive hypothesis

A specialized hypothesis

Discuss

Answer: (c).A false positive hypothesis

Q5.

What is used for probability theory sentences?

Conditional logic

Logic

Extension of propositional logic

None of the mentioned

Discuss

Answer: (c).Extension of propositional logic

Q6.

Where does the dependence of experience is reflected in prior probability sentences?

Syntactic distinction

Semantic distinction

Both a & b

None of the mentioned

Discuss

Answer: (a).Syntactic distinction

Q7.

Where does the degree of belief are applied?

Propositions

Literals

Variables

Statements

Discuss

Answer: (a).Propositions

Q8.

How many formal languages are used for stating propositions?

Discuss

Answer: (b).2

Q9.

What is the basic element for a language?

Literal

Variable

Random variable

All of the mentioned

Discuss

Answer: (c).Random variable

Q10.

How many types of random variables are available?

Discuss

Answer: (c).3

Page 1 of 6

Welcome to the Uncertain Knowledge MCQs Page

Uncertain Knowledge MCQs | Page 1 of 6

Explore more Topics under Artificial Intelligence

Introduction to AI

Intelligent Agents

Problem Solving

Game Planning

Knowledge and Reasoning

Learning

Perceiving and Acting

Natural Language Processing

LISP Programming

Expert Systems

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