Learning From Observations. “In which we describe agents that can improve their behavior through diligent study of their own experiences.”  Artificial Intelligence: A Modern Approach. Prepared by: San Chua, Natalie Weber, Henry Kwong. Outline. Learning agents Inductive learning
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“In which we describe agents that can improve their behavior through diligent study of their own experiences.”
Artificial Intelligence: A Modern Approach
Prepared by: San Chua, Natalie Weber, Henry Kwong
 Critic: provides the learning element with information on how well the agent is doing based on a fixed performance standard. E.g. the audience
 Problem Generator: provides the performance element with suggestions on new actions to take.
Different variations of handwritten 3’s
applied to x.
Goal Predicate:
Will wait for a table?
Patrons?
none
full
some
No
Yes
WaitEst?
>60
010
3060
1030
No
Alternate?
Hungry?
Yes
yes
no
yes
no
No
Yes
Reservation?
Fri/Sat?
yes
no
yes
no
No
Yes
No
Yes
http://www.cs.washington.edu/education/courses/473/99wi/
Patrons?
none
full
some
WaitEst?
010
>60
3060
1030
Hungry?
yes
no
Yes
r [Patrons(r, full) Wait_Estimate(r, 1030)
Hungry(r, yes)] Will_Wait(r)
“The most likely hypothesis is the simplest one that is consistent with all observations.”
+ X1, X3, X4, X6, X8, X12 (Positive examples)  X2, X5, X7, X9, X10, X11 (Negative examples)
+ X1, X3, X4, X6, X8, X12 (Positive examples)  X2, X5, X7, X9, X10, X11 (Negative examples)
Patrons?
none
full
some
+
 X7, X11
+X1, X3, X6, X8

+X4, X12
 X2, X5, X9, X10
+ X1, X3, X4, X6, X8, X12 (Positive examples)  X2, X5, X7, X9, X10, X11 (Negative examples)
Patrons?
none
full
some
+
 X7, X11
+X1, X3, X6, X8

+X4, X12
 X2, X5, X9, X10
No
Yes
+ X1, X3, X4, X6, X8, X12 (Positive examples)  X2, X5, X7, X9, X10, X11 (Negative examples)
Patrons?
none
full
some
+
 X7, X11
+X1, X3, X6, X8

+X4, X12
 X2, X5, X9, X10
No
Yes
Hungry?
no
yes
+ X4, X12
 X2, X10
+
 X5, X9
Better
Attribute
Patrons?
none
full
some
+
 X7, X11
+X1, X3, X6, X8

+X4, X12
 X2, X5, X9, X10
Not As Good An Attribute
Type?
French
Burger
Italian
Thai
+ X1
 X5
+X6
 X10
+ X4,X8
 X2, X11
+X3, X12
 X7, X9
http://www.cs.washington.edu/education/courses/473/99wi/
Patrons?
none
some
full
Hungry?
No
Yes
No
Yes
Type?
No
French
burger
Italian
Thai
Yes
Fri/Sat?
Yes
No
no
yes
No
Yes
http://www.cs.washington.edu/education/courses/473/99wi/
Goal Predicate:
Will wait for a table?
Patrons?
none
full
some
No
Yes
WaitEst?
>60
010
3060
1030
No
Alternate?
Hungry?
Yes
yes
no
yes
no
No
Yes
Reservation?
Fri/Sat?
yes
no
yes
no
No
Yes
No
Yes
http://www.cs.washington.edu/education/courses/473/99wi/
1. Collect a large set of examples.
2. Divide it into 2 disjoint set: the training set and the test set. It is very important that these 2 sets are separate so that the algorithm doesn’t cheat. Usually this division of examples is done randomly.
3. Use the learning algorithm with the training set as examples to generate a hypothesis H.
4. Measure the percentage of examples in the test set that are correctly classified by H.
5. Repeat steps 1 to 4 for different sizes of training sets and different randomly selected training sets of each size.
Learning Curve for the Decision Tree Algorithm
(On examples in the restaurant domain)
1.0
0.9
0.8
0.7
0.6
0.5
0.4
% correct on test set
Happy Graph
0 20 40 60 80 100
Training set size
“Artificial Intelligence A Modern Approach”, Stuart Russel Peter Norwig
1. Pick a random example to define the initial hypothesis
2. For each example,
3. Return the hypothesis
http://www.pitt.edu/~suthers/infsci1054/8.html
http://www.pitt.edu/~suthers/infsci1054/8.html
1. Initially, the Gset is True, and the Sset is False
2. For each new example, there are 6 possible cases:
a) false positive for Si in S
b) false negative for Si in S
c) false positive for Gi in G
d) false negative for Gi in G
e) Si more general than some other hypothesis in S or G
f) Gi more specific than some other hypothesis in S or G
3. Repeat the process until one of three things happens:
a) Only one hypothesis left in the version space.
b) The version space collapses, i.e. either G or S becomes empty.
c) We run out of examples while the version space still has several hypotheses.
OR
How can you know that a hypothesis is close to the target function if you don’t know what the target function is?