CS 4700: Foundations of Artificial Intelligence

1. CS 4700:Foundations of Artificial Intelligence Carla P. Gomes gomes@cs.cornell.edu http://www.cs.cornell.edu/Courses/cs4700/2008fa/Module: Introduction (Reading R&N: Chapter 1)

2. Overview of this Lecture • Course Administration • What is Artificial Intelligence? • Course Themes, Goals, and Syllabus

3. Course Administration

4. CS 4700:Foundations of Artificial Intelligence Lectures: Monday, Wednesday, Friday 1:115 – 12:05 Location: Phillips Hall, room 101 Lecturer: Prof. Gomes Office: 5133 Upson Hall Phone: 255 9189 Email: gomes@cs.cornell.edu Administrative Assistant: Kelly Duby Kelly Duby  <kduby@cs.cornell.edu>      4105 Upson Hall, 255-0980 Web Site:http://www.cs.cornell.edu/Courses/cs4700/2008fa/

5. CS 4700:Foundations of Artificial Intelligence Head Teaching Assistants Yunsong Guo guoys @cs.cornell.edu Anton Morozov  amoroz @cs.cornell.edu Teaching Assistants Clayton Chang cc843 @cornell.edu Sean Sullivan sps27 @cornell.edu Web Site:http://www.cs.cornell.edu/Courses/cs4700/2008fa/

6. Office Hours • Prof. Gomes: • Office: 5133 Upson Hall • I prefer to meet during my scheduled office hours, however, • if you need to meet with me at a different time please • schedule an appointment by email. • TAs - TBA Fridays: 1:15p.m – 2:15 p.m. (starting next week)

7. Grades Midterm (15%) Homework                     (45%) Participation                   (5%) Final                               (35%)

8. Homework • Homework is very important. It is the best way for you to learn the • material. You are encouraged to discuss the problems with your • classmates, but all work handed in should be original, written by you in • your own words. • Assignments turned in late will drop 5 points for each period of 24 • hours for which the assignment is late. In addition, no assignments • will be accepted after the solutions have been made available.No late • homework will be accepted

9. Mailing List • cs4700ta-l@lists.cs.cornell.edu. • Contact us by using this mailing list.  The list is set to mail all • the TA's and Prof. Gomes -- you will get the best response • time by using this facility, and all the TA's will know the • question you asked and the answers you receive.

10. CS 4701:Practicum in Artificial Intelligence (Optional) • CS4701 Project  (Optional) • CS4700 is a co-requisite for CS473.  • There will be an organizational meeting in Hollister Hall room 110 on Tuesday, September 2nd at  3:35pm. • The main assignment for CS4701 is a course project. Students will work in groups (probably pairs).  A project proposal is required.  A separate project handout with project suggestions, details, and due dates regarding the project proposal,  and final project write-up will be made available from the course home page. • Grading CS4701 • 20%: Project proposal • 80%: Final code, write-up, and presentation

11. Required Textbook Artificial Intelligence: A Modern Approach (AIMA) (Second Edition) by Stuart Russell and Peter Norvig Artificial Intelligence : A New Synthesis By Nils Nilsson

12. Lecture notes and reading material http://www.cs.cornell.edu/Courses/cs4700/2008fa/ Optional reading material

13. Welcome to this class! • We will work together throughout this semester. • Questions and suggestions are welcome anytime. • E.g., if you find anything incorrect or unclear, send an email or talk to me. • Any questions?

14. Overview of this Lecture • Course Administration • What is Artificial Intelligence? • Course Themes, Goals, and Syllabus

15. AI: Goals • Ambitious goals: • understand “intelligent” behavior • build “intelligent” agents

16. What is Intelligence? • Intelligence: • “the capacity to learn and solve problems” (Webster dictionary) • the ability to act rationally

17. What is AI? • Views of AI fall into four different perspectives: • Thinking versus Acting • Human versus Rational Human-like Intelligence “Ideal” Intelligent/ Rationally Thought/ Reasoning Behavior/ Actions

18. Human Thinking Human Acting Rational Thinking Rational Acting Different AI Perspectives 2. Systems that think like humans 3. Systems that think rationally 1. Systems that act like humans 4. Systems that act rationally

19. 1. Acting Humanly Human-like Intelligence “Ideal” Intelligent/ Rationally Thought/ Reasoning Behavior/ Actions

20. Alan Turing In 1936, Alan Turing, a British mathematician, showed that there exists a relatively simple universal computing device that can perform any computational process. Computers use such a universal model. Turing Machine (abstraction) Turing also showed the limits of computation – some problems cannot be computed even with the most powerful computer and even with unlimited amount of time – e.g., Halting problem.

21. Acting humanly: Turing Test Alan Turing • Turing (1950) "Computing machinery and intelligence": • "Can machines think?“ Instead, "Can machines behave intelligently?" • Operational test for intelligent behavior: the Imitation Game • Predicted that by 2000, a machine might have a 30% chance of fooling a lay person for 5 minutes • Anticipated all major arguments against AI in following 50 years • Suggested major components of AI AI system passes if interrogator cannot tell which one is the machine (interaction via written questions)

22. Acting humanly: Turing Test Turing test  identified key research areas in AI: • Natural Language Processing – to communicate with the machine; • Knowledge Representation – to store and manipulate information; • Automated reasoning – to use the stored information to answer questions and draw new conclusions; • Machine Learning – to adapt to new circumstances and to detect and extrapolate patterns. but does a machine need to act humanly to be considered intelligent?

23. Some Famous Imitation Games • 1960s ELIZA Joseph Weizenbaum • Rogerian psychotherapist • 1990s ALICE • Loebner prize • win $100,000 if you pass the test

24. ELIZA: impersonating a Rogerian psychotherapist 1960s ELIZA Joseph Weizenbaum http://www-ai.ijs.si/eliza/eliza.html Eliza: a friend you could never have before Eliza: Hello. I am ELIZA. How can I help you? You: Well I feel sad Eliza: Do you often feel sad? You: not very often, but it's becoming more common Eliza: Please go on.

25. Chat Bot – ALICE(AI Foundation) • Human: hi my name is CarlaALICE: How are you doing, Carla.Human: what is your name?ALICE: My name is ALICE.Human: what is your full name?ALICE: ALICE = Artificial Linguistic Internet Computer Entity.Human: when were you born?ALICE: I was connected to the net on November 23, 1995. • Human: Have you passed the Loebner Prize?ALICE: The Loebner Prize is an annual Turing Test sponsored by New York philanthropist Hugh Loebner. Dr. Wallace and ALICE won it in 2000. The ALICE program was ranked "most human computer" by the panel of judges. • Human: who is Dr. Wallace?ALICE: He is a famous computer scientist, the author of ALICE, and a graduate of Carnegie Mellon. • Human: Where does Dr. Wallace work?ALICE: he works from his home in San Francisco.

26. The Loebner contest • A modern version of the Turing Test, held annually, with a $100,000 cash prize. • Hugh Loebner was once director of UMBC’s Academic Computing Services (née UCS) • http://www.loebner.net/Prizef/loebner-prize.html • Restricted topic (removed in 1995) and limited time. • Participants include a set of humans and a set of computers and a set of judges. • Scoring • Rank from least human to most human. • Highest median rank wins $2000. • If better than a human, win $100,000. (Nobody yet…)

27. 2. Thinking Humanly Human-like Intelligence “Ideal” Intelligent/ Rationally Thought/ Reasoning Behavior/ Actions

28. Thinking humanly: modeling cognitive processes • Requires scientific theories of internal activities of the brain; • 1) Cognitive Science (top-down) : computer models + experimental techniques from psychology •  Predicting and testing behavior of human subjects • 2) Cognitive Neuroscience (bottom-up) •  Direct identification from neurological data Both approaches are now distinct from AI 1960s "cognitive revolution": information-processing psychology

29. 3. Thinking Rationally Human-like Intelligence “Ideal” Intelligent/ Rationally Thought/ Reasoning Behavior/ Actions

30. Thinking rationally: formalizing the "laws of thought“ • Logic  Making the right inferences! Several Greek schools developed various forms of logic: notation and rules of derivation for thoughts; • Aristotle: what are correct arguments/thought processes? (characterization of “right thinking”); • Socrates is a man All men are mortal -------------------------- Therefore Socrates is mortal • More contemporary logicians (e.g. Boole, Frege, Tarski)  • Direct line through mathematics and philosophy to modern AI • Limitations:: • Not all intelligent behavior is mediated by logical deliberation • What is the purpose of thinking? What thoughts should I have?

31. Course Perspective 4. Acting Rationally Human-like Intelligence “Ideal” Intelligent/ Rationally Thought/ Reasoning Behavior/ Actions

32. Acting rationally: rational agent • Rational behavior: doing the right thing • The right thing: that which is expected to maximize goal achievement, given the available information • Doesn't necessarily involve thinking – e.g., blinking reflex – but thinking should be in the service of rational action

33. Rational agents • An agent is an entity that perceives and acts • This course is about designing rational agents • Abstractly, an agent is a function from percept histories to actions: • [f: P*A] • For any given class of environments and tasks, we seek the agent (or class of agents) with the best performance • Caveat: computational limitations make perfect rationality unachievable  design best program for given machine resources

34. Focus of CS4700 (most recent progress). Building Intelligent Machines • I Building exact models of human cognition view from psychology and cognitive science • II Developing methods to match or exceed human • performance in certain domains, possibly by • very different means  e.g., Deep Blue;

35. Methodology of AI • Theoretical aspects • Mathematical formalizations, properties, algorithms • Engineering aspects • The act of building (useful) machines • Empirical science • Experiments

36. CS4700 What's involved in Intelligence? • A) Ability to interact with the real world to perceive, understand, and act speech recognition and understanding image understanding (computer vision) • B) Reasoning and Planning modelling the external world problem solving, planning, and decision making ability to deal with unexpected problems, uncertainties • C) Learning and Adaptation • We are continuously learning and adapting. We want systems that adapt to us!

37. Historic Perspective

38. AI Leverages from different disciplines • Philosophy • e.g., foundational issues in logic, methods of reasoning, • mind as physical system, foundations of learning, • language, rationality • Computer science and engineering • e.g., complexity theory, algorithms, logic and inference, • programming languages, and system building (hardware • and software). • Mathematics and physics • e.g., probability theory, statistical modeling, continuous mathematics, • Markov models, statistical physics, and complex systems. • and others, e.g., cognitive science, neuroscience, economics, psychology, linguistics,

39. AI More direct Influence • Obtaining an understanding of the human mind is one of the • final frontiers of modern science. • George Boole, Gottlob Frege, and Alfred Tarski formalizing the laws of human thought • Alan Turing, John von Neumann, and Claude Shannon • thinking as computation • Direct Founders: • John McCarthy, Marvin Minsky, Herbert Simon, and Allen Newell the start of the field of AI (1959)

40. History of AI:MilestonesThe gestation of AI 1943-1956 • 1943 : McCulloch and Pitts • McCulloch and Pitts’s model of artificial neurons • Minsky’s 40-neuron network • 1950 : Turing’s “Computing machinery and intelligence” • 1950s Early AI programs, including Samuel’s checkers program, Newell and Simon’s Logic theorist • 1956 Dartmouth meeting : Birth of “Artificial Intelligence” • A 2-month Dartmouth workshop of 10 attendees – the name of AI • Newell and Simon’s Logic Theorist • Do you think AI is a god name?

41. Perceptrons Early neural nets More about Neural Nets later in the course…

42. History of AILook, Ma, no hands !(1952-1969)Early enthusiasm, great expectations • 1957 Herb Simon: It is not my aim to surprise or shock you – but the simplest way I can summarize is to say that there are now in the world machines that think, that learn and that create. Moreover their ability to do these things is going to increase rapidly until – in the visible future – the range of problems that they can handle will be coextensive with the range to which human mind has been applied. • 1958 : John McCarthy’s LISP • 1965 : J.A. Robinson invents the resolution principle, basis for automated theorem • Intelligent reasoning in Microworlds (such as Block’s world)

43. A D D A B C C T Initial State Goal State The Block’s world

44. History of AIA dose of reality (1966-1978) • 1965 : Weizenbaum’s ELIZA • Difficulties in automated translation (try http://babelfish.yahoo.com/) • Syntax is not enough • “the spirit is willing but the flesh is weak” “the vodka is good but the meat is rotten” • Limitations of Perceptrons discovered •  can only represent linearly separable functions Neural network research almost disappears • NP-Completeness (Cook 72) • Intractability of the problems attempted by AI, • Worst- case result….

45. History of AIKnowledge based systems (1969-79) • Intelligence requires knowledge - Knowledge based systems as opposed to weak methods (general-purpose search methods)  Expert Systems, E.g.: • Mycin : diagnose blood infections • R1 : configuring computer systems

46. History of AIAI becomes industry (1980-88) • Expert systems • Lisp-machines • Return of Neural Nets  End of 80’s – limitations of expert systems became clear, even though they have been quite successful in certain domains.

47. History of AI:2000-AI is Alive and Kicking • Current work on “intelligent agents”: • Emphasis on integration of reasoning (search and inference as well as probabilistic reasoning), knowledge representation, and learning techniques • AI as a science: Combining theoretical and empirical analysis • Mathematical sophistication of AI techniques AAAI08 Key challenge: building flexible and scalable AI systemsin the Open World . • “… A better understanding of the problems and their complexity properties, • combined with increased mathematical sophistication, • has led to workable research agendas and robust methods” R&N.

48. AI Achievements A few recent examples…

49. 1996 - EQP: Robbin’s Algebras are all boolean A mathematical conjecture (Robbins conjecture) unsolved for 60 years! The Robbins problem was to determine whether one particular set of rules is powerful enough to capture all of the laws of Boolean algebra. One way to state the Robbins problem in mathematical terms is: Can the equation not(not(P))=P be derived from the following three equations? [1] P or Q = Q or P, [2] (P or Q) or R = P or (Q or R), [3] not(not(P or Q) or not(P or not(Q))) = P. First creative mathematical proof by computer: unlike brute-force based proofs such as the 4-color theorem. [An Argonne lab program] has come up with a major mathematical proof that would have been called creative if a human had thought of it. New York Times, December, 1996 http://www-unix.mcs.anl.gov/~mccune/papers/robbins/

50. Microsoft Office’97 + Answer Wizard • Diagnosis reasoning using Bayesian Models • Restricted NLP