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AI Review Game 2. Teams. A: Miles, Max, Ellie, Seth B: Will, Graham, Schuyler C: Peter, Clyde, Emily, Yumi D: George, Stafford, Josh, Eric E: Thomas, Fritz, Evan. 1. Why do we use alpha-beta?. 2. Name 3 kinds of machine learning. 3. What kind of learning is ID3?. 4.

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Teams
Teams

A: Miles, Max, Ellie, Seth

B: Will, Graham, Schuyler

C: Peter, Clyde, Emily, Yumi

D: George, Stafford, Josh, Eric

E: Thomas, Fritz, Evan


1.

  • Why do we use alpha-beta?


2.

  • Name 3 kinds of machine learning.


3.

  • What kind of learning is ID3?


4.

  • What is Ockham’s Razor?


5.

  • What is the Chinese Room metaphor?


6.

  • What is the difference in strong and weak AI?


7.

  • What is an example of a manipulator robot?


8.

  • Besides manipulation, what are the other 2 main tasks of robotics?


9.

  • What is the difference between passive and active machine learning?


10.

  • What is the difference between the areas of voice recognition and natural language processing?


11.

  • What is the difference between information retrieval and information extraction?


12.

  • What are the two ways to encode colors?


13.

  • What is the Bayer pattern?


14.

  • What is entropy?


15.

  • What is the formula for entropy?


16.

  • Describe how to use minimax for a 3-player game.


17.

  • Describe how to use minimax for a game of chance.


18.

  • What is a Markov Decision Process?


19.

  • What is the difference between a state’s utility and its reward?


20.

  • Name a reason you might prefer policy iteration over value iteration.


All play
All-play

Examples:

  • Raining Morning Yes

  • Raining Afternoon Yes

  • Sunny Morning No

  • Sunny Afternoon Yes

  • WindyMorning Maybe

  • Windy Afternoon Yes

  • What is formula for the information needed to make the decision (the entropy of the set)?  

    b) Given that the information needed to make the decision is I, what is the formula for the gain for asking for weather first?


All play1

2 -1 12 3 0 -3 -1 -10 2 1

All-play


All play2
All-play

R: s1: -0.2 U: s1: 0.2

s2: -0.2 s2: 0.1

s3 : 0.3 s3: 0.3

s4 : -0.1 s4: 0.1

T(s1): s1 s2 s3 s4

a1 .1 .7 .2 0

a2 .1 .3 .5 .1

Gamma = 0.2

What is the new valueof U[s1]?


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