Quiz #1 - Fuzzy Logic, Probabilities, Uncertainty and Fuzziness]

Quiz #1 - Fuzzy Logic, Probabilities, Uncertainty and Fuzziness]

Which branch of mathematics is closely related to fuzzy logic?

• Calculus

• Linear algebra

• Probability theory

• Number theory

Probability theory

Conditional probability starts with the probability of two events occurring together and calculates the probability of one event given the other.

• True

• False

True

Uncertainty arises when the available information is complete and reliable.

• True

• False

False

What does it use to simulate multiple possible outcomes using random sampling and probabilistic models?

• Fuzzy Logic

• Rule-Based

• Monte Carlo Simulation

• Baye's Theorem

Monte Carlo Simulation

What is fuzzy logic primarily used for?

• Handling uncertainty and imprecision

• Processing images

• Binary decision making

• Solving linear equations

Handling uncertainty and imprecision

In what year Baye's theorem introduced.

• 1990

• 1970

• 1770

• 1980

1770

Which of the following is not a characteristic of fuzzy logic?

• Linguistic variables

• Membership functions

• Fuzzy sets

• Knowledge boundaries

Knowledge boundaries

In AI and machine learning (ML), handling uncertainty and fuzziness is crucial for improving decision-making, model reliability, and adaptability to real-world problems.

• True

• False

True

What is the range of membership values in fuzzy logic? 

• [0, ∞]

• [0, 1]

• [0, ∞]

• [-1, 1]

[0, 1]

What is the purpose of defuzzification in fuzzy logic? 

• To convert fuzzy outputs into crisp values

• To calculate the centroid of a fuzzy set

• To convert crisp inputs into fuzzy values

• To optimize membership functions

To convert fuzzy outputs into crisp values

In an expert system, how is conditional probability used? 

• To infer causes from observed effects

• All of the above

• To calculate the likelihood of events given certain conditions

• To assign probabilities to different hypotheses

All of the above

Which uncertainty management technique involves assigning probabilities to possible outcomes?

• Bayesian inference

• Fuzzy logic

• Dempster-Shafer theory

• Certainty factors

Bayesian inference

What does the term "fuzzy inference" refer to in fuzzy logic?

• The process of fuzzification

• The process of determining the degree of membership in a fuzzy set

• The process of making decisions based on fuzzy rules

• The process of defuzzification

The process of making decisions based on fuzzy rules

Which of the following is a limitation of fuzzy logic?

 

• It struggles with handling subjective opinions

• It requires extensive training data

• It cannot be implemented in computer systems

• It is incompatible with probabilistic models

It requires extensive training data

Which of the following techniques is commonly used for representing uncertainty in expert systems?

• Fuzzy logic

• Neural networks

• Boolean logic

• Decision trees

Fuzzy logic

What theory extends probability theory by allowing belief representation with unknown probabilities?

• Certainty Factors

• Dempster-Shafer Theory

• Fuzzy Logic

• Probability Theory

Dempster-Shafer Theory

 Which of the following applications is NOT suitable for fuzzy logic?

• Image recognition

• Temperature control in air conditioning systems

• Sorting algorithms

• Stock market prediction

Sorting algorithms

Fuzzy logic is particularly useful in situations where:

• Decision-making is entirely deterministic

• All data is precise and well-defined

• Variables are imprecise or ambiguous

• All options have equal weight in decision-making

Variables are imprecise or ambiguous

What does conditional probability measure? 

• The likelihood of an event occurring given that another event has already occurred

• The likelihood of two mutually exclusive events occurring simultaneously

• The likelihood of two independent events occurring simultaneously

• The likelihood of an event occurring

The likelihood of an event occurring given that another event has already occurred

What is the purpose of uncertainty reasoning in expert systems?

• To represent and manage uncertainty in knowledge

• To eliminate uncertainty entirely

• To generate absolute certainty in decision making

• To ignore uncertainty and focus solely on deterministic reasoning

To represent and manage uncertainty in knowledge

In a medical diagnosis scenario, if P(Disease) is 0.1, P(Positive|Disease) is 0.9, and P(Positive|No Disease) is 0.2, what is P(Disease|Positive)?

• 0.81

• 0.45

• 0.18

• 0.9

0.9

In a natural language processing system, how might conditional probability be used? 

• To assess the sentiment of a given text

• All of the above

• To predict the next word in a sentence

• To determine the probability of different grammatical structures

All of the above

What does P(B|A) represent in Bayes' Theorem? 

• Joint probability of events A and B occurring

• Probability of event B occurring given that event A has occurred

• Probability of event A occurring given that event B has occurred

• Conditional probability of event B

Probability of event B occurring given that event A has occurred

In a weather prediction system, which of the following would be an example of conditional probability?

• The probability of rain tomorrow

• The probability of rain and wind occurring simultaneously

• The probability of rain or snow tomorrow

• The probability of rain given that it's cloudy today

The probability of rain given that it's cloudy today

Who is Bayes' Theorem named after?

• Thomas Bayes

• Isaac Newton

• Albert Einstein

• Carl Friedrich Gauss

Thomas Bayes