To implement humanly behavior.
To deal with unknown environment.
To improve the reasoning capability of the agent.
All of the above
D. All of the above
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
So that the agent can have decision making capability
So that the agent can think and act humanly
So that the agent can apply the logic for finding the solution to any particular problem
All of the above
i. and ii.
i. and iii.
ii. and iii.
iii. and iv.
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
Knowledge Level
Logical Level
Implementation Level
Can't be determined
Estimation
Likelihood
Observations
All of the above
Only valid data
Only invalid data
Both valid and invalid data
None of the above
i. v. ii. iv. iii.
i. ii. iii. iv. v.
ii. i. v. iv. iii.
None of the above
Only i.
i. and iii.
ii. and iii.
All i, ii. and iii.
100% accurate
Estimated values
Wrong values
None of the above
Knowledge Level
Logical Level
Implementation Level
Can't be determined
Deterministic and non- Deterministic
Observable and partially-observable
Static and dynamic
All of the above
Sensors and Actuators
Wheels and steering
Arms and legs
All 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
i. and ii.
i. and iii.
ii. and iii.
iii. and iv.
Top-down approach
Bottom-up approach
No specific approach
According to precedence
Simple based reflex agent
Model based reflex agent
Goal based agent
Utility based agent
Quantifiers are numbers ranging from 0-9.
Quantifiers are the quantity defining terms which are used with the predicates.
Quantifiers quantize the term between 0 and 1.
None of the above
Knowledge gathering strategy
Final step of solving the AI problem, which is applying the strategies
State space deciding
None of the above
Deciding which data Structure to choose
Forming the control strategy
Inferring for similar problems in the knowledge base
All 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
Only iv.
All i., ii., iii. and iv.
ii. and iv.
None of the above
Forward Chaining
Backward Chaining
Both a. and b.
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
Machine Learning
Deep Learning
Both (1) and (2)
None of the above
Deterministic and non- Deterministic
Observable and partially-observable
Static and dynamic
Left sided and right sided
Sky and Land
Agent and environment
Yes or No
None of the above
In situations of uncertainty, probabilistic theory can help us give an estimate of how much an event is likely to occur or happen.
It helps to find the probability whether the agent should do the task or not.
It does not help at all.
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
To implement humanly behavior.
To deal with unknown environment.
To improve the reasoning capability of the agent.
All of the above