Transcript Introduction to Artificial Intelligence

Introduction to Artificial
Intelligence
(40-417)
Course Page
http://sina.sharif.edu/~sani/courses/ai
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Announcements
Slides
Assignments
Textbooks
Main
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S Russell and P Norvig, Artificial
Intelligence: A Modern Approach, Prentice
Hall, 3rd ed., 2010.
Auxiliary
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E Rich, k Knight, and S Nair, Artificial
Intelligence, McGraw-Hill, 3rd ed., 2009.
Evaluation
Quizzes and Assignments (10%)
Prolog Assignments (20%)
Mid-Term Exam (35%)
Final Exam (35%)
What is Artificial Intelligence
(AI)?
Views of AI fall into four categories in Two dimensions:
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Thinking/Reasoning vs. behavior/action
Success according to human standards vs. success according
to an ideal concept of intelligence (rationality):
Act like humans
Act rationally
Think like humans
Think rationally
The textbook advocates "acting rationally"
Act Rationally
Rational behavior: doing the right thing
“The right thing”:
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is expected to maximize goal achievement,
given the available information
Limited resource, imperfect knowledge
Doesn't necessarily (but often) involve
thinking
Doesn’t necessarily have anything to do
with how humans solve the same problem.
Rational agents
An agent is an entity that perceives and acts
The text book focuses on designing rational agents
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An agent is a function from percept histories to actions:
f : P*  A
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For any given class of environments and task, we seek
the agent with the best performance.
Problem: computational limitations make perfect
rationality unachievable.
The Origins of AI
1950 Alan Turing’s paper, Computing Machinery and
Intelligence, described what is now called “The Turing
Test”.
Turing predicted that in about fifty years "an average
interrogator will not have more than a 70 percent chance
of making the right identification after five minutes of
questioning".
1957 Newell and Simon predicted that "Within ten years
a computer will be the world's chess champion."
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Turing test
AI system
Experimenter
Control
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Eliza, 1965
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Patient: You are like my father in some ways.
Doctor: What resemblance do you see?
Patient : You are not very aggressive.
Doctor : What makes you think I am not very aggressive?
Patient : You don’t argue with me.
Doctor : Why do you think I don’t argue with you?
Patient : You are afraid of me.
Doctor : Does it please you to believe I am afraid of you?
Patient : My father is afraid of everybody.
Doctor : What else comes to mind when you think of your father?
Patient : Bullies.
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The Chinese Room
She does not
know
Chinese
Correct
Responses
Chinese
Writing is
given to the
person
Set of rules, in
English, for
transforming
phrases
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Newell and Simon Prediction
In 1997, Deep Blue beat Gary
Kasparov.
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Why Did They Get it Wrong?
They failed to understand at least
three key things:
•The need for knowledge (lots of it)
•Scalability and the problem of
complexity and exponential growth
•The need to perceive the world
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Scalability
Solving hard problems
requires search in a
large space.
To play master-level
chess requires
searching about 8 ply
deep. So about 358
nodes must be
examined.
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Exponential Growth
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But Chess is Easy
•The rules are simple enough to fit on one page
•The branching factor is only 35.
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A Harder One
John saw a boy and a girl with a red wagon with one blue
and one white wheel dragging on the ground under a tree
with huge branches.
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How Bad is the Ambiguity?
•Kim (1)
•Kim and Sue (1)
•Kim and Sue or Lee (2)
•Kim and Sue or Lee and Ann (5)
•Kim and Sue or Lee and Ann or Jon (14)
•Kim and Sue or Lee and Ann or Jon and Joe (42)
•Kim and Sue or Lee and Ann or Jon and Joe or Zak (132)
•Kim and Sue or Lee and Ann or Jon and Joe or Zak and
Mel (469)
•Kim and Sue or Lee and Ann or Jon and Joe or Zak and
Mel or Guy (1430)
•Kim and Sue or Lee and Ann or Jon and Joe or Zak and
Mel or Guy and Jan (4862)
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Computer as Artist
Two paintings done by Harold Cohen’s Aaron program:
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What is an AI Technique?
Example: Tic-Tac-Toe program
complexity
use of generalizations
clarity of knowledge
extensibility
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9
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Program 1
Board: 9-element vector
0 : blank, 1 : X , 2 : O
Move table: 39 Rows of 9-element vectors
Algorithm:
1. transform board vector from base 3 to 10
2. use (1) as the move table index
3. change the board by using the vector from (2)
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Comments:
Advantages:
efficient in terms of time,
optimal game of tic-tac-toe in theory
Disadvantages:
space - move table space
work - move table
error prone - move table
three dimension - 327, no longer work at all
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Program 2
Board: program1
2 : blank, 3 : X, 5 : O
Turn: game moves 1,2,3,.....
odd-numbered move : x
even-numbered move : o
Algorithm : 3 sub procedures
Make2: Board[5] or Board [2, 4, 6, or 8],
Posswin (p): 18 (3*3*2) for p = X
50 (5*5*2) for p = O
Go (n) : Move to Board [n]
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Sterategy
Turn=1 Go (1)
Turn=2 Go (5) or Go (1)
Turn=3 Go (9) or Go (3)
Turn=4 Go(Posswin(X)) or Go(Make2)
Turn=5 Go(Posswin(X)), or
Go(Posswin(O)), or Go(7), or Go(3) [fork]
…
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Comments:
Less efficient than Program 1 (time)
More efficient (space)
More clarity (strategy)
Easier to change (strategy)
Cannot extend to three dimension
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Program 2'
program 2 board
magic square 15
possible win check:
S = sum of two paired owned by a player
D = 15 – S
if 0 < D < 10 and Board [D] is empty then the player can
win
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1
6
3
5
7
4
9
2
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Program 3 :O minimax
X
X
O O
O
X
X
O O
X
O
X
X
-10
X
O
X
X
X
X
10
O O
O O O
X
X
O X O
X
X
X
10
O O O
0
X
-10
X
O O X
O
X O X
-1
X
X
X
10
O
X
10
O
O X O
O X O
X
X
X
X O X
0
0
X
O
0
X O
0
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Comments
much more complex (time and space)
Extendable
AI technique
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