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
B. When the agent goes from one state to another, it is known as a move
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
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
Deterministic and non- Deterministic
Observable and partially-observable
Static and dynamic
All of the above
N- Queens Problem
Chess
Sudoku
None of the above
100% accurate
Estimated values
Wrong values
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.
Only i.
i. and iii.
ii. and iii.
All i, ii. and iii.
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
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.
Only i.
i. and iii.
ii. and iii.
All i, ii. and iii. and iv.
i. and ii.
i. and iii.
ii. and iii.
iii. and iv.
For all
For some
For every
All of the above
Simple based reflex agent
Model based reflex agent
Goal based agent
Utility based agent
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
Partially observable environment
Dynamic nature of the environment
Inaccessible area in the environment
All of the above
Simple based Reflex agent
Model Based Reflex Agent
Goal Based Agent
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
100% accurate
Estimated values
Wrong values
None of the above
The Breadth First Search (BFS)
The Depth First Search (DFS)
The A* search
None of the above
Movement and Humanly Actions
Perceiving and acting on the environment
Input and Output
None of the above
Knowledge Level
Logical Level
Implementation Level
Can't be determined
Thinking
Eating
Sleeping
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
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
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
Sky and Land
Agent and environment
Yes or No
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
Partially True and Partially False
Completely True and Completely False
Both 1) and 2)
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.