1. Artificial Intelligence Introduction (2)

2. What is Artificial Intelligence ?  making computers that think?  the automation of activities we associate with human thinking, like decision making, learning... ?  the art of creating machines that perform functions that require intelligence when performed by people ?  the study of mental faculties through the use of computational models ?

3. What is AI?  There are no crisp definitions Q. What is artificial intelligence? A. It is the science and engineering of making intelligent machines, especially intelligent computer programs. Q. what is intelligence? A. Intelligence is the computational part of the ability to achieve goals in the world. Varying kinds and degrees of intelligence occur in people, many animals and some machines.

4. 4 Alan M Turing, Hero  Helped to found theoretical CS –1936, before digital computers existed  Helped to found practical CS –wartime work decoding Enigma machines –ACE Report, 1946  Helped to found practical AI –first (simulated) chess program  Helped to found theoretical AI …

5. 5 What did Turing think?  Turing (in 1950) believed that by 2000 –computers available with 128Mbytes storage –programmed so well that interrogators have only a 70% chance after 5 minutes of being right  “By 2000 the use of words and general educated opinion will have altered so much that one will be able to speak of machines thinking without expecting to be contradicted”

6. Turing Test  Three rooms contain a person, a computer, and an interrogator.  The interrogator can communicate with the other two by teleprinter.  The interrogator tries to determine which is the person and which is the machine.  The machine tries to fool the interrogator into believing that it is the person.  If the machine succeeds, then we conclude that the machine can think.

7. 7 The Imitation Game  Interrogator in one room –computer in another –person in a third room  From typed responses only (text-only), can interrogator distinguish between person and computer?  If the interrogator often guesses wrong, say the machine is intelligent.

8. 8 Can Machines Think?  Turing starts by defining machine & think –Will not use everyday meaning of the words  otherwise we could answer by Gallup poll –Instead, use a different question  closely related, but unambiguous  “I believe the original question to be too meaningless to deserve discussion”

9. 9 A sample game  Turing suggests some Q & A’s:  Q: Please write me a sonnet on the subject of the Forth Bridge  A: Count me out on this one, I never could write poetry  Q: Add 34957 to 70764. –(pause about 30 seconds)  A: 105621  Q: Do you play chess?  A: Yes  Q: I have K at my K1, and no other pieces. You have only K at K6 and R at R1. It is your move. What do you play? –(pause about 15s)  A: R-R8 mate

10. 10 Some Famous Imitation Games  1960sELIZA –Rogerian psychotherapist  1970sSHRDLU –Blocks world reasoner  1980s NICOLAI –unrestricted discourse  1990sLoebner prize –win $100,000 if you pass the test

11. “Chinese room” argument [Searle 1980]  Person who knows English but not Chinese sits in room  Receives notes in Chinese  Has systematic English rule book for how to write new Chinese characters based on input Chinese characters, returns his notes –Person=CPU, rule book=AI program, really also need lots of paper (storage) –Has no understanding of what they mean –But from the outside, the room gives perfectly reasonable answers in Chinese!  Searle’s argument: the room has no intelligence in it! image from http://www.unc.edu/~prinz/pictures/c-room.gif

12. Why is AI hard? Two usual ingredients (for standard AI)  Representation –need to represent our knowledge in computer readable form  Reasoning –need to be able to manipulate knowledge and derive new knowledge –finding the successful way usually involves search Both of these are hard.

13. Knowledge Representation  It is the problem of capturing in a formal language suitable for computer manipulation.  We will study logic as a language for AI

14. Representation Language  An AI representation language must : –Handle qualitative knowledge –Allow new knowledge to be inferred from set of facts and rules

15. Search Problem Search is a problem-solving technique to explores successive stages in problem-solving process.

16. Search Problem  We need to define a space to search in to find a problem solution  To successfully design and implement search algorithm, we must be able to analyze and predict its behavior.

17. State Space Search One tool to analyze the search space is to represent it as space graph, so by use graph theory we analyze the problem and solution of it.

18. Graph Theory A graph consists of a set of nodes and a set of arcs or links connecting pairs of nodes. Island1Island2 River1 River2

19. Graph structure  Nodes = {a, b, c, d, e}  Arcs = {(a,b), (a,d), (b,c), ….} a d b e c

20. Tree  A tree is a graph in which two nodes have at most one path between them.  The tree has a root. a b cd efghij

21. Space representation In the space representation of a problem, the nodes of a graph correspond to partial problem solution states and arcs correspond to steps in a problem-solving process

22. Example  Let the game of Tic-Tac-toe 123 84 765

23. 123 84 765 123 746 582 143 876 582 143 786 562 143 764 582 113 746 582 133 746 582 143 176 582 143 576 782

24. Strategies for search  The strategies for state space search are: Data-driven and goal-driven search

25. Data-Driven search  It is called forward chaining  The problem solver begins with the given facts and a set of legal moves or rules for changing state to arrive to the goal.

26. Goal-Driven Search  Take the goal that we want to solve and see what rules or legal moves could be used to generate this goal.  So we move backward.

27. Search Implementation  In both types of moving search, we must find the path from start state to a goal.  We use goal-driven search if –The goal is given in the problem –There exist a large number of rules –Problem data are not given

28. Search Implementation  The data-driven search is used if –All or most data are given –There are a large number of potential goals –It is difficult to form a goal