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CSE 471/598 Introduction to Artificial Intelligence

CSE 471/598 Introduction to Artificial Intelligence. http://www.public.asu.edu/~huanliu/AI06S/cse471-598.htm. Spring 2006. Introduction. You: Future AI Experts TA: Trevor L Tang, Brickyard 214 T: 4:30 – 5:30pm, Th 3:10-4:10 (venue is tentative), l.tang@asu.edu, and

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CSE 471/598 Introduction to Artificial Intelligence

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  1. CSE 471/598 Introduction to Artificial Intelligence http://www.public.asu.edu/~huanliu/AI06S/cse471-598.htm Spring 2006

  2. Introduction • You: Future AI Experts • TA: Trevor L Tang, Brickyard 214 T: 4:30 – 5:30pm, Th 3:10-4:10 (venue is tentative), l.tang@asu.edu, and • Me: Huan Liu, hliu@asu.edu (http://www.public.asu.edu/~huanliu) • My office hours • Slides are updated periodically CSE 471/598, CBS 598 H. Liu

  3. Course introduction • What is AI (many definitions of AI) • One definition: a field to enable computers with human-level intelligence with attempts to understand intelligent entities. • We will evaluate many later. • What is this course about (multi-purpose) • understand ourselves better • build automated intelligent agents • improve problem solving skills CSE 471/598, CBS 598 H. Liu

  4. Course workload and evaluation • A lot of work is expected from you. “No pain, no gain!” • Projects (30%, 2-3) – all in Lisp? • Exam(s) (2*25%) • Homework (~20%) • Quizzes and class participation (~10% extra) • Which grading system • Late penalty, YES and exponentially increased • Academic integrity(http://www.public.asu.edu/~huanliu/conduct.html) CSE 471/598, CBS 598 H. Liu

  5. Course plan • Text Book: AI - A Modern Approach • 2nd Edition in green • Reading assignment: chapters covered • About 13-15 chapters • Our goal: “to finish all chapters” One major subject per week TIP Try to keep up and avoid catch-up CSE 471/598, CBS 598 H. Liu

  6. Major Topics • Intelligent agents • Problem solving • Knowledge and reasoning • Acting logically • Learning • Uncertainty TIP Comprehend the topics with your common sense CSE 471/598, CBS 598 H. Liu

  7. 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. • You get feedback from us, and we expect feedback from you, too  • Any questions? • Use myASU to send email and for discussions CSE 471/598, CBS 598 H. Liu

  8. Introduction of AI Gearing up for a fun semester about intelligent agents What is an intelligent agent in your view?

  9. What is AI • About thinking and acting • We are not alone, but … (Homo genus) http://en.wikipedia.org/wiki/Homo_(genus) • Acting humanly: The Turing test (by Turing 1950) • Its original purpose • What do we need to pass the test? http://www.loebner.net/Prizef/loebner-prize.html • Does that serve our original purpose? • Thinking humanly: Cognitive modeling • “Think-aloud” to learn from human and recreate in computer programs (GPS) • What the Eyes see, a camera cannot http://www.topcharoen.co.th/web/illusion/illusion-a19.gif CSE 471/598, CBS 598 H. Liu

  10. What is AI (2) • Thinking rationally: Syllogisms, Logic • What would you act on $50 iBooks late last year? • Unable to deal with uncertainty • Some paradoxes: Liar, Barber • Gödel's incompleteness and Turing's undecidability • Acting rationally: A rational agent (something that acts) to achieve best or best expected outcomes • Some rational actions do not involve inference • An example – a reflex doe not need inference • A set of definitions (Figure 1.1) CSE 471/598, CBS 598 H. Liu

  11. Foundations of AI • Philosophy (428 B.C. - Present) – reasoning and learning • Can formal rules be used to draw valid conclusions? • How does the mental mind arise from a physical brain? • Where does knowledge come from? • How does knowledge lead to action? CSE 471/598, CBS 598 H. Liu

  12. Mathematics (c. 800 - Present) - logic, probability, decision making, computation • What are the formal rules to draw conclusions? • What can be computed? • How do we reason with uncertain information? • Economics (1776-present) • How should we make decisions so as to maximize payoff? • How should we do this when others may not go along? • How should we do this when the payoff may be far in the future? CSE 471/598, CBS 598 H. Liu

  13. Neuroscience (1861-present) • How do brains process information • Processing speed, memory size in a computer (Figure 1.3) • Psychology (1879 - Present) - investigating human mind • How do humans and animals think and act? • Mind Wide Open • Computer engineering (1940 - Present) - ever improving tools • How can we build an efficient computer? • Moors Law CSE 471/598, CBS 598 H. Liu

  14. Control theory and Cybernetics (1948-present) • How can artifacts operate under their own control? • Feedback and adapt • Linguistics (1957 - Present) - the structure and meaning of language • How does language relate to thought? • Computational linguistics CSE 471/598, CBS 598 H. Liu

  15. Brief History of AI • Gestation of AI (1943 -1955) • McCulloch and Pitts’s model of artificial neurons • Minsky’s 40-neuron network • Alan Turing’s Computing Machinary and Intelligence • Birth of AI (1956) • A 2-month Dartmouth workshop of 10 attendees – the name of AI • Newell and Simon’s Logic Theorist • Should another name like `computational rationality’ be used? Any suggestion? • Early enthusiasm, great expectations (1952 - 1969) • GPS by Newell and Simon, Lisp by McCarthy, Blockworld by Minsky CSE 471/598, CBS 598 H. Liu

  16. AI facing reality (1966 - 1973) • Many predictions of AI’s coming successes • A computer would be a chess champion in 10 years (1957) • Machine translation – Syntax is not enough • Intractability of the problems attempted by AI • “What computers cannot do” in 76 • Knowledge-based systems (1969 - 1979) • Knowledge is power, acquiring knowledge from experts • Expert systems (MYCIN) • AI - an industry (1980 - present) • Many AI systems help companies to save money and increase productivity CSE 471/598, CBS 598 H. Liu

  17. The return of neural networks (1986 – present) • PDP books by Rumelhart and McClelland • Connectionist models vs. symbolic models • AI – a science (1987 – present) • Build on existing theories vs. propose brand new ones • Rigorous empirical experiments • Learn from data – data mining • AI – intelligent agents (1995 – present) • Working agents embedded in real environments with continuous sensory inputs CSE 471/598, CBS 598 H. Liu

  18. Smart bombs Deep Blue, and others E-Game industry Intelligent houses Intelligent appliances RoboCup Mars rovers Biometrics Communications (email, word processor) Auto driving from E to W (98% vs. 2%) Consumer protection Some examples of AI applications CSE 471/598, CBS 598 H. Liu

  19. Concluding remarks • “The real value of the discipline, Mr. Lazowska said, is less in acquiring a skill with technology tools - the usual definition of computer literacy - than in teaching students to manage complexity; to navigate and assess information; to master modeling and abstraction; and to think analytically in terms of algorithms, or step-by-step procedures.” from http://www.nytimes.com/2005/08/23/technology/23geeks.html • What is AI about? CSE 471/598, CBS 598 H. Liu

  20. Refresher for LISP • What is it? • ANSI Common Lisp, Paul Graham, Prentice Hall • Input (e.g., terminal, files) • Output (e.g., files, printing) • Processing (various operations) • How to run it? CSE 471/598, CBS 598 H. Liu

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