1. Traditional set theory is also known as Crisp Set theory.
a) True
b) False

a) True
Explanation:
Traditional set theory set membership is fixed or exact either the member is in the set or not. There is only two crisp values true or false. In case of fuzzy logic there are many values. With weight say x the member is in the set.

2. What is the consequence between a node and its predecessors while creating Bayesian network?
a) Conditionally dependent
b) Dependent
c) Conditionally independent
d) Both a & b

c) Conditionally independent
Explanation: The semantics to derive a method for constructing Bayesian networks were led to the consequence that a node can be conditionally independent of its predecessors.

5. Like relational databases there does exists fuzzy relational databases.
a) True
b) False

a) True
Explanation: Once fuzzy relations are defined, it is possible to develop fuzzy relational databases. The first fuzzy relational database, FRDB, appeared in Maria Zemankova's dissertation.

6. _____________ is/are the way/s to represent uncertainty.
a) Fuzzy Logic
b) Probability
c) Entropy
d) All of the mentioned

d) All of the mentioned
Explanation: Entropy is amount of uncertainty involved in data. Represented by H(data).

8. Fuzzy logic is usually represented as
a) IF-THEN-ELSE rules
b) IF-THEN rules
c) Both a & b
d) None of the mentioned

b) IF-THEN rules
Explanation: Fuzzy set theory defines fuzzy operators on fuzzy sets. The problem in applying this is that the appropriate fuzzy operator may not be known. For this reason, fuzzy logic usually uses IF-THEN rules, or constructs that are equivalent, such as fuzzy associative matrices.
Rules are usually expressed in the form:
IF variable IS property THEN action

10. Where does the Bayes rule can be used?
a) Solving queries
b) Increasing complexity
c) Decreasing complexity
d) Answering probabilistic query

d) Answering probabilistic query
Explanation: Bayes rule can be used to answer the probabilistic queries conditioned on one piece of evidence.

11. Fuzzy Set theory defines fuzzy operators. Choose the fuzzy operators from the following.
a) AND
b) OR
c) NOT
d) EX-OR

a) AND
b) OR
c) NOT
Explanation: The AND, OR, and NOT operators of Boolean logic exist in fuzzy logic, usually defined as the minimum, maximum, and complement;

12. Which of the following is used for probability theory sentences?
a) Conditional logic
b) Logic
c) Extension of propositional logic
d) None of the mentioned

c) Extension of propositional logic
Explanation: The version of probability theory we present uses an extension of propositional logic for its sentences.

15. The values of the set membership is represented by
a) Discrete Set
b) Degree of truth
c) Probabilities
d) Both b & c

b) Degree of truth
Explanation: Both Probabilities and degree of truth ranges between 0 - 1.

17. How many types of random variables are available?
a) 1
b) 2
c) 3
d) 4

c) 3
Explanation: The three types of random variables are Boolean, discrete and continuous.

20. This set of Artificial Intelligence MCQs focuses on "Fuzzy Logic - 1".
1. Fuzzy logic is a form of
a) Two-valued logic
b) Crisp set logic
c) Many-valued logic
d) Binary set logic

c) Many-valued logic
Explanation: With fuzzy logic set membership is defined by certain value. Hence it could have many values to be in the set.

21. Where do we implement Artificial Intelligence Fuzzy Logic?

It's a multi valued logic.
In Boolean logic is two valued logic,where we will say
an element belongs to a set with membership 1, if it
doesn't belongs to the set then it's membership is 0.

Where as in fuzzy sets we say degree of membership
between 0 and 1.

For example,we have a set of men age.
In Boolean logic a person X aged 51 we will say x is old
and a person Y aged 49 we will say young.
In fuzzy logic we say X belongs to the old men set with a
membership of 0.51 and to young men set with a membership of
0.49

So Boolean logic is our Black&White TV where as Fuzzy logic
is Color TV Fuzzy logic is a Discrete spectrum of values.

22. What is Artificial Intelligence Fuzzy Logic?

Fuzzy logic is a form of many-valued logic; it deals with reasoning that is approximate rather than fixed and exact. In contrast with traditional logic theory, where binary sets have two-valued logic: true or false, fuzzy logic variables may have a truth value that ranges in degree between 0 and 1. Fuzzy logic has been extended to handle the concept of partial truth, where the truth value may range between completely true and completely false. Furthermore, when linguistic variables are used, these degrees may be managed by specific functions.