Artificial Intelligence Fuzzy Logic Interview Questions And Answers

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.

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.

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.

a) Either 0 or 1, between 0 & 1
Explanation: Refer the definition of Fuzzy set and Crisp set.

a) True

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

a) Fuzzy Set
Explanation: Fuzzy logic deals with linguistic variables.

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

d) Probability distributions for Continuous variables

a) True

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

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;

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

a) Hedges

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

a) Complete description of the domain
Explanation: A Bayesian network provides a complete description of the domain

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

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.

b) Fully connected

c) Fuzzy Set
Explanation: Local structure is usually associated with linear rather than exponential growth in complexity.

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.

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.