Deterministic and non- Deterministic
Observable and partially-observable
Static and dynamic
All of the above
C. Static and dynamic
Deterministic and non- Deterministic
Observable and partially-observable
Static and dynamic
All of the above
Only iv.
All i., ii., iii. and iv.
ii. and iv.
None of the above
S=12; E=5; N=6; D=8; M=1; O=0; R=8; Y=2
S=9; E=5; N=6; D=7; M=1; O=0; R=8; Y=2
S=5; E=5; N=6; D=7; M=1; O=0; R=8; Y=2
S=9; E=5; N=9; D=7; M=1; O=0; R=8; Y=2
Simple based reflex agent
Model based reflex agent
Goal based agent
Utility based agent
Only i.
i. and iii.
ii. and iii.
All i, ii. and iii. and iv.
Only i.
i. and iii.
ii. and iii.
All i, ii. and iii.
The certainty factor is same as the probability of any event
The Certainty Factor (CF) is a numeric value that tells us about how likely an event or a statement is supposed to be true
The Certainty Factor (CF) is a numeric value that tells us about how certain we are about performing a particular task
None of the above
Only iv.
All i., ii., iii. and iv.
ii. and iv.
None of the above
When the agent moves from one place to another, then it is called the move of the agent
When the agent goes from one state to another, it is known as a move
Both (1) and (2)
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
It is the way in which facts and information are stored in the storage system of the agent
When we conclude the facts and figures to reach a particular decision, that is called inference
We modify the knowledge and convert it into the format which is acceptable by the machine
All of the above.
i. and ii.
i. and iii.
ii. and iii.
iii. and iv.
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.
Perceiving data from the environment
Adapting to the environment and situations
Acting upon the Environment
Reversing the previously performed actions
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
Reaching the goal in minimal amount of time
Reaching the goal in minimal cost
Reaching the initial state again after reaching the goal state
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
3 levels
2 levels
4 levels
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
Knowledge Level
Logical Level
Implementation Level
Can't be determined
Only valid data
Only invalid data
Both valid and invalid data
None of the above
Encryption Problem
Constraint Satisfactory Problem
Number problem
All of the above
Personal Enhancement Area in Science
Performance, Environment, Actuators and Sensors
Performance, Entity, Area, State
None of the above
Deterministic and non- Deterministic
Observable and partially-observable
Static and dynamic
All of the above
Deterministic and non- Deterministic
Observable and partially-observable
Static and dynamic
Left sided and right sided
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
Top-down approach
Bottom-up approach
No specific approach
According to precedence
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
Modus Ponens
Resolution
Backward Chaining
All of the above