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Introduction to Artificial Intelligence (40-417).

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Presentation on theme: "Introduction to Artificial Intelligence (40-417)."— Presentation transcript:

1 Introduction to Artificial Intelligence (40-417)

2 Course Page Announcements Slides Assignments

3 Textbooks Main S Russell and P Norvig, Artificial Intelligence: A Modern Approach, Prentice Hall, 3 rd ed., 2010. Auxiliary E Rich, k Knight, and S Nair, Artificial Intelligence, McGraw-Hill, 3 rd ed., 2009.

4 Evaluation Quizzes and Assignments (10%) Prolog Assignments (20%) Mid-Term Exam (35%) Final Exam (35%)

5 What is Artificial Intelligence (AI)? Views of AI fall into four categories in Two dimensions: Thinking/Reasoning vs. behavior/action Success according to human standards vs. success according to an ideal concept of intelligence (rationality): The textbook advocates "acting rationally" Act like humansAct rationally Think like humansThink rationally

6 Act Rationally Rational behavior: doing the right thing “The right thing”: 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.

7 Rational agents An agent is an entity that perceives and acts The text book focuses on designing rational agents An agent is a function from percept histories to actions: For any given class of environments and task, we seek the agent with the best performance. Problem: computational limitations make perfect rationality unachievable.

8 8 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."

9 9 Turing test Experimenter AI system Control

10 10 Eliza, 1965 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.

11 11 The Chinese Room Set of rules, in English, for transforming phrases Chinese Writing is given to the person Correct Responses She does not know Chinese

12 12 Newell and Simon Prediction In 1997, Deep Blue beat Gary Kasparov.

13 13 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

14 14 Scalability Solving hard problems requires search in a large space. To play master-level chess requires searching about 8 ply deep. So about 35 8 nodes must be examined.

15 15 Exponential Growth

16 16 But Chess is Easy The rules are simple enough to fit on one page The branching factor is only 35.

17 17 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.

18 18 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)

19 19 Computer as Artist Two paintings done by Harold Cohen’s Aaron program:

20 20 What is an AI Technique? Example: Tic-Tac-Toe program complexity use of generalizations clarity of knowledge extensibility 123 456 789

21 21 Program 1 Board: 9-element vector 0 : blank, 1 : X, 2 : O Move table: 3 9 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)

22 22 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 - 3 27, no longer work at all

23 23 Program 2 Board: program1 2 : blank, 3 : X, 5 : O Turn: game moves1,2,3,..... 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]

24 24 Sterategy Turn=1Go (1) Turn=2 Go (5) or Go (1) Turn=3Go (9) or Go (3) Turn=4Go(Posswin(X)) or Go(Make2) Turn=5Go(Posswin(X)), or Go(Posswin(O)), or Go(7), or Go(3) [fork] …

25 25 Comments: Less efficient than Program 1 (time) More efficient (space) More clarity (strategy) Easier to change (strategy) Cannot extend to three dimension

26 26 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 834 159 672

27 27 Program 3 : minimax O X X OO X X OO X X OOX X X OO X XX OXO X X OXO X XO OO X XX OOO X XX OO X XOX OOO X XX OO X XOX OXO X XO 1010000 -1010 0 -10 0

28 28 Comments much more complex (time and space) Extendable AI technique

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