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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

slide3
1.
  • Why do we use alpha-beta?
slide4
2.
  • Name 3 kinds of machine learning.
slide5
3.
  • What kind of learning is ID3?
slide6
4.
  • What is Ockham’s Razor?
slide7
5.
  • What is the Chinese Room metaphor?
slide8
6.
  • What is the difference in strong and weak AI?
slide9
7.
  • What is an example of a manipulator robot?
slide10
8.
  • Besides manipulation, what are the other 2 main tasks of robotics?
slide11
9.
  • What is the difference between passive and active machine learning?
slide12
10.
  • What is the difference between the areas of voice recognition and natural language processing?
slide13
11.
  • What is the difference between information retrieval and information extraction?
slide14
12.
  • What are the two ways to encode colors?
slide15
13.
  • What is the Bayer pattern?
slide16
14.
  • What is entropy?
slide17
15.
  • What is the formula for entropy?
slide18
16.
  • Describe how to use minimax for a 3-player game.
slide19
17.
  • Describe how to use minimax for a game of chance.
slide20
18.
  • What is a Markov Decision Process?
slide21
19.
  • What is the difference between a state’s utility and its reward?
slide22
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 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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