Perceiving data from the environment
Adapting to the environment and situations
Acting upon the Environment
Reversing the previously performed actions
D. Reversing the previously performed actions
An agent which needs user inputs for solving any problem
An agent which can solve any problem on its own without any human intervention
An agent which needs an exemplary similar problem defined in its knowledge base prior to the actual problem
All of the above
Deterministic and non- Deterministic
Observable and partially-observable
Static and dynamic
All of the above
Thinking humanly
Adapting to the environment and situations
To rule over humans
Real Life Problem Solving
Dependent Events
Independent Events
Neither a. nor b.
Both a. and b.
Discrete or Continuous
Observable and partially-observable
Static and dynamic
None of the above
Uncertainty in Environment
Poor battery life of the system
Improper training time
All of the above
Only iv.
All i., ii., iii. and iv.
ii. and iv.
None of the above
Conditional Probability gives 100% accurate results.
Conditional Probability can be applied to a single event.
Conditional Probability has no effect or relevance or independent events.
None of the above.
Sensors and Actuators
Wheels and steering
Arms and legs
All of the above
i. and ii.
i. and iii.
ii. and iii.
iii. and iv.
Movement and Humanly Actions
Perceiving and acting on the environment
Input and Output
None of the above
In deductive logic, the complete evidence is provided about the truth of the conclusion made
A top-down approach is followed
The agent uses specific and accurate premises that lead to a specific conclusion
All of the above
Perceiving data from the environment
Adapting to the environment and situations
Acting upon the Environment
Reversing the previously performed actions
Only i.
i. and iii.
ii. and iii.
All i, ii. and iii.
Sky and Land
Agent and environment
Yes or No
None of the above
Constraints should be taken care of while solving the problem
The text is converted from readable format to non-readable format
If numbers are not sufficient, we can use special symbols like $#@% to encrypt the text
None of the above
It provides solution in a reasonable time frame
It provides the reasonably accurate direction to a goal
It considers both actual costs that it took to reach the current state and approximate cost it would take to reach the goal from the current state
All of the above
Knowledge Level
Logical Level
Implementation Level
Can't be determined
Fuzzy Logic (FL) is a method by which any expert system or any agent based on Artificial Intelligence performs reasoning under uncertain conditions.
In this method, the reasoning is done in almost the same way as it is done in humans.
In this method, all the possibilities between 0 and 1 are drawn.
All of the above
Documentation for an AI agent
Production rules for an AI agent
Pseudo Code for an AI agent
None of the above
Constraints are a set of restrictions and regulations
While solving a CSP, the agent cannot violate any of the rules and regulations or disobey the restrictions mentioned as the constraints
It also focuses on reaching to the goal state
All of the above
Only i.
i. and iii.
ii. and iii.
All i, ii. and iii.
i. and ii.
i. and iii.
ii. and iii.
iii. and iv.
N- Queens Problem
Chess
Sudoku
None of the above
To solve real life problems
To play a game against a human in the same way as a human would do
To understand the environment variables
All of the above
Personal Enhancement Area in Science
Performance, Environment, Actuators and Sensors
Performance, Entity, Area, State
None of the above
Responding and providing solution to the problem
Meeting the preference of the user
Meeting the goal
All of the above
Machine Learning
Deep Learning
Both (1) and (2)
None of the above
In propositional Logic, each sentence is a declarative sentence
In propositional logic, the sentence can have answers other than True or False
Propositional Logic is a type of knowledge representation in AI
None of the above
i. and ii.
i. and iii.
ii. and iii.
iii. and iv.