Introduction to Artificial Intelligence (40-417)

1. Introduction to Artificial Intelligence(40-417)

2. Course Page • http://sina.sharif.edu/~sani/courses/ai • Announcements • Slides • Assignments

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

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. 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." 8

9. Turing test AI system Experimenter Control 9

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. 10

11. 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 11

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

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 13

14. 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. 14

15. Exponential Growth 15

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

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. 17

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) 18

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

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

21. Program 1 Board: 9-element vector 0 : blank, 1 : X , 2 : O Move table: 39 Rows of 9-element vectors Algorithm: 1. transform board vectorfrom base 3 to 10 2. use (1) as the move tableindex 3. change the board by using the vector from (2) 21

22. Comments: Advantages: efficient in terms of time, optimal game of tic-tac-toe in theory Disadvantages: space - move tablespace work - move table error prone - move table three dimension - 327, no longer work at all 22

23. Program2 Board: program1 2 : blank, 3 : X, 5 : O Turn: gamemoves1,2,3,..... odd-numbered move : x even-numbered move : o Algorithm : 3sub 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] 23

24. 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] … 24

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

26. Program2' program 2board 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 26

27. Program3 : minimax 0 -10 10 0 -10 10 10 -1 10 0 0 0 27

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