The total number of proposition symbols in AI are…………..
a) 3 proposition symbols
b) 1 proposition symbols
c) 2 proposition symbols
d) No proposition symbols
Explanation: There are totally 2 proposition symbols. The two proposition symbols are true and false.
The total number of logical symbols in AI are…………..
a) There are 3 logical symbols
b) There are 5 logical symbols
c) Number of logical symbols are baed on the input
d) Logical symbols are not used
Explanation: There are totally five logical symbols. The five logical symbols are:
a) Negation
b) Conjunction
c) Disjunction
d) Implication
e) Biconditional
Which of the following are the approaches to Artificial Intelligence?
a) Applied approach
b) Strong approach
c) Weak approach
d) All of the mentioned
Explanation: Strong AI is used to build machines that can truly reason and solve problems.
Weak AI deals with building computer-based Artificial Intelligence that can act as if it were intelligent but cannot truly reason and solve problems. Applied approach creates commercially viable “smart” systems.
In the Cognitive approach, a computer is used to test theories about how the human mind works.
Face Recognition system is based on which type of
approach?
a) Weak AI approach
b) Applied AI approach
c) Cognitive AI approach
d) Strong AI approach
Explanation: Applied approach aims to produce commercially viable “smart” systems such as, for example, a security system that recognizes the faces of people to provide access. The applied approach has already enjoyed considerable success.
Which of the following is an advantage of artificial
intelligence?
a) Reduces the time taken to solve the problem
b) Helps in providing security
c) Have the ability to think hence makes the work easier
d) All of the above
Explanation: Artificial intelligence creates a machine that can think and make decisions without human involvement.
Which of the following can improve the performance of an AI agent?
a) Perceiving
b) Learning
c) Observing
d) All of these
Explanation: An AI agent learns from previous states by saving it and responding to the same situation better if it occurs again in the future. Hence, learning can improve the performance of an AI agent.
