Texas A & M University Artificial Intelligence Questions Artificial Intelligence Exercises: 2.5: If the current target location were to be moved, the mid

Texas A & M University Artificial Intelligence Questions Artificial Intelligence

Exercises:

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2.5: If the current target location were to be moved, the middle layer of Example 2.5 travels to the original position of that target and does not try to go to the new position. Change the controller so that the robot can adapt to targets moving.

2.6: The current controller visits the locations in the to_do list sequentially.

(a) Change the controller so that it is opportunistic; when it selects the next location to visit, it selects the location that is closest to its current position. It should still visit all the locations.

(b) Give one example of an environment in which the new controller visits all the locations in fewer time steps than the original controller.

(c) Give one example of an environment in which the original controller visits all the locations in fewer time steps than the modified controller.

(d) Change the controller so that, at every step, the agent heads toward whichever target location is closest to its current position.

(e) Can the controller from part (d) get stuck and never reach a target in an example where the original controller will work? Either give an example in which it gets stuck and explain why it cannot find a solution, or explain why it gets to a goal whenever the original can.

2.9: Suppose you have a new job and must build a controller for an intelligent robot. You tell your bosses that you just have to implement a command function and a state transition function. They are very skeptical. Why these functions? Why only these? Explain why a controller requires a command function and a state transition function, but not other functions. Use proper English. Be concise

Textbook: https://artint.info/2e/html/ArtInt2e.Ch2.html for reference. Artificial Intelligence
Exercises:
2.5:
If the current target location were to be moved, the middle layer
of Example 2.5 travels to the original position of that target and does not
try to go to the new position. Change the controller so that the robot can
adapt to targets moving.
2.6:
The current controller visits the locations in the to_do list sequentially.
(a)
Change the controller so that it is opportunistic; when it selects the
next location to visit, it selects the location that is closest to its
current position. It should still visit all the locations.
(b)
Give one example of an environment in which the new controller
visits all the locations in fewer time steps than the original controller.
(c)
Give one example of an environment in which the original controller
visits all the locations in fewer time steps than the modified
controller.
(d)
Change the controller so that, at every step, the agent heads toward
whichever target location is closest to its current position.
(e)
Can the controller from part (d) get stuck and never reach a target in
an example where the original controller will work? Either give an
example in which it gets stuck and explain why it cannot find a
solution, or explain why it gets to a goal whenever the original can.
2.9:
Suppose you have a new job and must build a controller for an intelligent
robot. You tell your bosses that you just have to implement a command
function and a state transition function. They are very skeptical. Why these
functions? Why only these? Explain why a controller requires a command
function and a state transition function, but not other functions. Use proper
English. Be concise
Textbook: https://artint.info/2e/html/ArtInt2e.Ch2.html for reference.

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