Partially True and Partially False
Completely True and Completely False
Both 1) and 2)
None of the above
A. Partially True and Partially False
Knowledge Level
Logical Level
Implementation Level
Can't be determined
Partially True and Partially False
Completely True and Completely False
Both 1) and 2)
None of the above
For all
For some
For every
All of the above
100% accurate
Estimated values
Wrong values
None of the above
Dependent Events
Independent Events
Neither a. nor b.
Both a. and b.
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.
Between 0 to 1 (Both inclusive)
Between 0 to 1 (Both exclusive)
Between -1 to +1
None of the above
Only i.
i. and iii.
ii. and iii.
iii. and iv.
Searching for relevant data in the surroundings
Searching into its own knowledge base for solutions
Seeking for human inputs for approaching towards the solution
None of the above
Thinking humanly
Adapting to the environment and situations
To rule over humans
Real Life Problem Solving
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
100% accurate
Estimated values
Wrong values
None of the above
Uncertainty in Environment
Poor battery life of the system
Improper training time
All of the above
Only valid data
Only invalid data
Both valid and invalid data
None of the above
Knowledge Level
Logical Level
Implementation Level
Can't be determined
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
Uncertainty arises when we are not 100 percent confident in our decisions
Whenever uncertainty arises, there is needs to be an estimation taken for getting to any conclusion
The AI agent should take certain decisions even in the situations of uncertainty
All of the above
The Breadth First Search (BFS)
The Depth First Search (DFS)
The A* search
None of the above
Deterministic and non- Deterministic
Observable and partially-observable
Static and dynamic
All 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
Modus Ponens
Resolution
Backward Chaining
All of the above
Discrete or Continuous
Observable and partially-observable
Static and dynamic
None of the above
Only iv.
All i., ii., iii. and iv.
ii. and iv.
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
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
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
Only iv.
All i., ii., iii. and iv.
ii. and iv.
None of the above
Half true Half False
Somewhat true but not entirely false
Agent has no information about the event
Both a. and b.
Knowledge gathering strategy
Final step of solving the AI problem, which is applying the strategies
State space deciding
None of the above
Simple based reflex agent
Model based reflex agent
Goal based agent
Utility based agent