Documentation for an AI agent
Production rules for an AI agent
Pseudo Code for an AI agent
None of the above
B. Production rules for an AI agent
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.
Discrete or Continuous
Observable and partially-observable
Static and dynamic
None of the above
Only i.
i. and iii.
ii. and iii.
All i, ii. and iii. and iv.
Partially True and Partially False
Completely True and Completely False
Both 1) and 2)
None of the above
Estimation
Likelihood
Observations
All of the above
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
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
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
Modus Ponens
Resolution
Backward Chaining
All of the above
Thinking humanly
Adapting to the environment and situations
To rule over humans
Real Life Problem Solving
A state space can be defined as the collection of all the problem states
A state space is a state which exists in environment which is in outer space
A state space is the total space available for the agent in the state
All of the above
Simple based reflex agent
Model based reflex agent
Goal based agent
Utility based agent
Deterministic and non- Deterministic
Observable and partially-observable
Static and dynamic
All of the above
0
-1
+1
Is decided in prior to every problem
Uncertainty in Environment
Poor battery life of the system
Improper training time
All of the above
Only i.
i. and iii.
ii. and iii.
All i, ii. and iii.
Simple based Reflex agent
Model Based Reflex Agent
Goal Based Agent
All 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
i. and ii.
i. and iii.
ii. and iii.
iii. and iv.
For all
For each
For every
All of the above
Knowledge Level
Logical Level
Implementation Level
Can't be determined
Only i.
i. and iii.
ii. and iii.
All i, ii. and iii.
Only valid data
Only invalid data
Both valid and invalid data
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.
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
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
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
i. and ii.
i., ii. and iii.
ii. and iii.
iii. and iv.
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
Thinking
Eating
Sleeping
None of the above