1. Carla P. Gomes INFO372 CS-INFO 372: Explorations in Artificial Intelligence Prof. Carla P. Gomes gomes@cs.cornell.edu Introduction http://www.cs.cornell.edu/courses/cs372/2008sp

2. Carla P. Gomes INFO372 Overview of this Lecture Course Administration What is Artificial Intelligence? Course Themes, Goals, and Syllabus

3. Carla P. Gomes INFO372 Course Administration

4. Carla P. Gomes INFO372 Lectures: Tuesday and Thursday - 10:10 - 11:25 Location: Phillips Hall, room 307 Lecturer: Prof. Gomes Office: 5133 Upson Hall Phone: 255 9189 Email: gomes@cs.cornell.edu Administrative Assistant: Beth Howard ( bhoward@cs.cornell.edu ) 5136 Upson Hall, 255-4188 TAs: Robert Xiao rkx2@cornell.edu Yunsong Guo Web Site: http://www.cs.cornell.edu/courses/cs372/2008sp INFO372 – Explorations in Artificial Intelligence Course Administration

5. Carla P. Gomes INFO372 Office Hours TAs: Robert Xiao rkx2@cornell.edu TBA Yunsong Guo guoys@cs.cornell.eduTBA Prof. Gomes: Office: 5133 Upson Hall If you need to meet with me at a different time please schedule an appointment by email. Wednesdays 12:00 – 1:00 p.m.

6. Carla P. Gomes INFO372 Grades Midterm (30%) Homework (25%) Participation (5%) Final (40%) 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. No late homework will be accepted

7. Carla P. Gomes INFO372 Textbook Artificial Intelligence: A Modern Approach (AIMA) (Second Edition) by Stuart Russell and Peter Norvig Artificial Intelligence : A New Synthesis By Nils Nilsson Linear Programming by Vasek Chvatal Principles of Constraint Programming By Krzysztof Apt

8. Carla P. Gomes INFO372 Overview of this Lecture Course Administration What is Artificial Intelligence? Course Themes, Goals, and Syllabus

9. Carla P. Gomes INFO372 What is Intelligence? Historical Perspective of AI State-of-the-art and Challenges What is Artificial Intelligence (AI)?

10. Carla P. Gomes INFO372 What is AI? Ambitious goals: –understand “intelligent” behavior –build “intelligent” agents

11. Carla P. Gomes INFO372 What is Intelligence? Intelligence: –“the capacity to learn and solve problems” (Webster dictionary) –the ability to act rationally Artificial Intelligence: –build and understand intelligent entities –synergy between: philosophy, psychology, and cognitive science computer science and engineering mathematics and physics

12. Carla P. Gomes INFO372 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., statistical modeling, continuous mathematics, Markov models, statistical physics, and complex systems. and others, e.g., cognitive science, neuroscience, economics, psychology, linguistics, statistics…

13. Carla P. Gomes INFO372 AI: Historical Perspective Obtaining an understanding of the human mind is one of the final frontiers of modern science. Founders: George Boole (1779-1848), Gottlob Frege (1848-1925), and Alfred Tarski (1902-1983) formalizing the laws of human thought Alan Turing (1912-1954), John von Neumann (1903-1957), Claude Shannon (1916-2001) thinking as computation John McCarthy (1927- ), Marvin Minsky (1927 - ), Herbert Simon (1916- 2001), and Allen Newell (1927-1992) the start of the field of AI (1959)

14. 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.

15. Carla P. Gomes INFO372 Halting Problem The halting problem is a decision problem which can be stated as follows: –Given a description of a program and a finite input, decide whether the program finishes running or will run forever, given that input. Alan Turing proved in 1936 that a general algorithm to solve the halting problem for all possible program-input pairs cannot exist. We say that the halting problem is undecidable.

16. Carla P. Gomes INFO372 Acting humanly: Turing Test Turing (1950) "Computing machinery and intelligence": "Can machines think?"  "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: knowledge, reasoning, natural language understanding, learning AI system passes if interrogator cannot tell which one is the machine Alan Turing

17. Carla P. Gomes INFO372 Some Famous Imitation Games 1960sELIZA Joseph Weizenbaum –Rogerian psychotherapist 1990s ALICE Loebner prize –win $100,000 if you pass the test

18. Carla P. Gomes INFO372 ELIZA: impersonating a Rogerian psychotherapist 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. 1960sELIZA Joseph Weizenbaum

19. Carla P. Gomes INFO372 Chat Bot – ALICE (AI Foundation) Human: hi my name is Carla ALICE: 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.it 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.

20. Carla P. Gomes INFO372 Acting humanly: Turing Test 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? Turing test  identified key research areas in AI:

21. Carla P. Gomes INFO372 Other perspectives on Intelligence Thinking humanly: cognitive modeling Requires scientific theories of internal activities of the brain; How to validate? 1) Cognitive Science (top-down)  Predicting and testing behavior of human subjects – computer models + experimental techniques from psychology 2) Cognitive Neuroscience (bottom-up)  Direct identification from neurological data Thinking rationally: "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 Acting rationally: rational agent Rational behavior: doing 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; Always doing the right thing  sometimes not feasible in complex environments  Computational demands can be too high!

22. Carla P. Gomes INFO372 Different Approaches 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; Focus of INFO372 (most recent progress).

23. Carla P. Gomes INFO372 Man vs. Machiens The Hardware The brain –a neuron, or nerve cell, is the basic information processing unit (10^11 ) –many more synapses (10^14) connect the neurons –cycle time: 10^(-3) seconds (1 millisecond) How complex can we make computers? –10^8 or more transistors per CPU –supercomputer: hundreds of CPUs, 10^10 bits of RAM –cycle times: order of 10^(-9) seconds (1 nanosecond)

24. Carla P. Gomes INFO372 Computer vs. Brain

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26. Carla P. Gomes INFO372 Conclusion –In near future we can have computers with as many processing elements as our brain, but: far fewer interconnections (wires or synapses) much faster updates. Fundamentally different hardware may require fundamentally different algorithms! – Very much an open question.

27. Carla P. Gomes INFO372 What is AI? Thinking humanly Thinking Rationally Acting Humanly Acting Rationally Thought/ Reasoning Behavior/ Actions Human-like Intelligence “Ideal” Intelligent/ Rationally

28. Carla P. Gomes INFO372 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! INFO 372

29. Carla P. Gomes INFO372 A few examples… State-of-the-art Reasoning and Planning in AI

30. Carla P. Gomes INFO372 1997: Deep Blue beats the World Chess Champion I could feel human-level intelligence across the room -Gary Kasparov, World Chess Champion (human…) vs.

31. Carla P. Gomes INFO372 Deep Blue vs. Kasparov One of the most famous modern computers, Deep Blue, which defeated Gary Kasparov at chess. Game 1: 5/3/97: Kasparov wins Game 2: 5/4/97: Deep Blue wins Game 3: 5/6/97: Draw Game 4: 5/7/97: Draw Game 5: 5/10/97: Draw Game 6: 5/11/97: Deep Blue wins “I felt a new kind of Intelligence” ( across the board from him) Kasparov 1997 The value of IBM’s stock Increased by $18 Billion!

32. Carla P. Gomes INFO372 How Intelligent is Deep Blue? Saying Deep Blue doesn't really think about chess is like saying an airplane doesn't really fly because it doesn't flap its wings. - Drew McDermott

33. Carla P. Gomes INFO372 On Game 2 (Game 2 - Deep Blue took an early lead. Kasparov resigned, but it turned out he could have forced a draw by perpetual check.) This was real chess. This was a game any human grandmaster would have been proud of. Joel Benjamin grandmaster, member Deep Blue team

34. Carla P. Gomes INFO372 Kasparov on Deep Blue 1996: Kasparov Beats Deep Blue “I could feel --- I could smell --- a new kind of intelligence across the table.” 1997: Deep Blue Beats Kasparov “Deep Blue hasn't proven anything.”

35. Carla P. Gomes INFO372 Game Tree Search How to search a game tree was independently invented by Shannon (1950) and Turing (1951). Technique called: MiniMax search. Evaluation function combines material & position.

36. Carla P. Gomes INFO372 Game Tree Search

37. Carla P. Gomes INFO372 History of Search Innovations Shannon, TuringMinimax search1950 Kotok/McCarthyAlpha-beta pruning1966 MacHackTransposition tables1967 Chess 3.0+Iterative-deepening1975 BelleSpecial hardware1978 Cray BlitzParallel search1983 HitechParallel evaluation1985 Deep BlueAll of the above 1997

38. Carla P. Gomes INFO372 Transposition Tables Introduced by Greenblat's Mac Hack (1966) Basic idea: caching –once a board is evaluated, save it in a hash table (data structure that associates keys with values), avoid re-evaluating. –called “transposition” tables, because different orderings (transpositions) of the same set of moves can lead to the same board. –Form of root learning (memorization) –Don’t repeat blunders  can’t beat the computer twice in a row using same moves Deep Blue --- huge transposition tables (100,000,000+), must be carefully managed.

39. Carla P. Gomes INFO372 Positions with Smart Pruning Search DepthPositions 260 42,000 660,000 82,000,000 10 (<1 second DB)60,000,000 122,000,000,000 14 (5 minutes DB)60,000,000,000 162,000,000,000,000 How many lines of play does a grand master consider? Around 5 to 7

40. Carla P. Gomes INFO372 Special-Purpose and Parallel Hardware Belle (Thompson 1978) Cray Blitz (1993) Hitech (1985) Deep Blue (1987-1996) –Parallel evaluation: allows more complicated evaluation functions –Hardest part: coordinating parallel search –Deep Blue never quite plays the same game, because of “noise” in its hardware!

41. Carla P. Gomes INFO372 Deep Blue Hardware –32 general processors –220 VSLI chess chips Overall: 200,000,000 positions per second –5 minutes = depth 14 Selective extensions - search deeper at unstable positions –down to depth 25 !

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43. Carla P. Gomes INFO372 Tactics into Strategy As Deep Blue goes deeper and deeper into a position, it displays elements of strategic understanding. Somewhere out there mere tactics translate into strategy. This is the closest thing I've ever seen to computer intelligence. It's a very weird form of intelligence, but you can feel it. It feels like thinking. –Frederick Friedel (grandmaster), Newsday, May 9, 1997

44. Carla P. Gomes INFO372 1996 - EQP: Robbin’s Algebras are all boolean [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/ A mathematical conjecture (Robbins conjecture) unsolved for decades 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.

45. Carla P. Gomes INFO372 For two days in May, 1999, an AI Program called Remote Agent autonomously ran Deep Space 1 (some 60,000,000 miles from earth) 1999: Remote Agent takes Deep Space 1 on a galactic ride

46. Carla P. Gomes INFO372 Remote Agent: 1999 Winner of NASA's Software of the Year Award It's one small step in the history of space flight. But it was one giant leap for computer-kind, with a state of the art artificial intelligence system being given primary command of a spacecraft. Known as Remote Agent, the software operated NASA's Deep Space 1 spacecraft and its futuristic ion engine during two experiments that started on Monday, May 17, 1999. For two days Remote Agent ran on the on-board computer of Deep Space 1, more than 60,000,000 miles (96,500,000 kilometers) from Earth. The tests were a step toward robotic explorers of the 21st century that are less costly, more capable and more independent from ground control. http://ic.arc.nasa.gov/projects/remote-agent/index.html

47. Carla P. Gomes INFO372 2000: SCIFINANCE synthesizes programs for financial modeling Develop pricing models for complex derivative structures Involves the solution of a set of PDEs (partial differential equations) Integration of object- oriented design, symbolic algebra, and plan-based scheduling

48. Carla P. Gomes INFO372 Proverb 1999: Solving Crossword Puzzles as Probabilistic Constraint Satisfaction Proverb solves crossword puzzles better than most humans Michael Littman et a. 99

49. Robocup @ Cornell 199 http://www.mae.cornell.edu/raff/MultiAgentSystems/MultiAgentSystems.htm

50. Carla P. Gomes INFO372 2003 Robocup Italy

51. Carla P. Gomes INFO372 2005 Autonomous Control: DARPA GRAND CHALLENGE October 9, 2005 Stanley and the Stanford RacingTeam were awarded 2 million dollars for being the first team to complete the 132 mile DARPA Grand Challenge course (Mojave Desert). Stanley finished in just under 6 hours 54 minutes and averaged over 19 miles per hours on the course.

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53. Carla P. Gomes INFO372 DARPA - Urban Challenge (2007) The Urban Challenge features autonomous ground vehicles maneuvering in a mock city environment, executing simulated military supply missions while merging into moving traffic, navigating traffic circles, negotiating busy intersections, and avoiding obstacles.

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56. Carla P. Gomes INFO372 Many Other Applications Financial planning Marketing E-business Telecommunications Manufacturing Operations Management Production Planning Transportation Planning System Design Health Care

57. Carla P. Gomes INFO372 Course Themes, Goals, and Syllabus

58. Carla P. Gomes INFO372 Goals of INFO 372 Focus of Info 372: Problem Solving Introduce the students to a range of computational modeling approaches and solution strategies using examples from AI and Information Science. Formalisms: Logical representations; Constraint-based languages, Mathematical programming; Multi-agent formalisms (including adversarial games); Solution strategies: Logical inference; General complete backtrack search; Local search; Dynamic Programming;

59. Carla P. Gomes INFO372 Goals of INFO 372 Special models: Satisfiability (SAT); Maximum SAT; Horn Constraint Satisfaction; Binary Constraint Satisfaction; Mixed Integer Programming, Linear Programming and Network Flow Models; Themes: Expressiveness and efficiency tradeoffs of the various representation formalisms  Students learn about the tradeoffs in modeling choices.; Concrete examples to move from one representation modeling formalism to another formalism;

60. Carla P. Gomes INFO372 Summary Discussed Artificial Intelligence and characteristics of intelligent systems. Gave series of example systems, involving e.g. game playing, automated reasoning, and planning. Computers are getting smarter !!! Suggested Reading: Chapter 1 Russell & Norvig

61. Carla P. Gomes INFO372 The END