1CS 188: Artificial IntelligenceFall 2008Lecture 1: Introduction8/28/2008Dan Klein – UC BerkeleyMany slides over the course adapted fromeither Stuart Russell or Andrew Moore Administriviahttp://inst.cs.berkeley.edu/~cs188 Course StaffCourse StaffSlav PetrovDan KleinGSIsProfessorAria HaghighiAnhPhamPercy LiangAnna RaffertyAlex SimmaDavid GollandCourse Details Book: Russell & Norvig, AI: A Modern Approach, 2ndEd. Prerequisites: (CS 61A or B) and (Math 55 or CS 70) There will be a lot of statistics and programming Work and Grading: Four assignments divided into checkpoints Programming: Python, groups of 1-2 Written: solve together, write-up alone 5 late days Mid-term and final Participation Fixed scale Academic integrity policyAnnouncements Important stuff: Python lab: THIS Friday and Wednesday, 11am-4pm in 275 Soda Hall Get your account forms (in front after class) First assignment on web soon Sections this coming week; start out in your assigned section, but can then move if space Waitlist: I don’t control enrollment, but most should get in Communication: Announcements: webpage Newsgroup Staff email: [email protected] IRC? Questions?Today What is AI? Brief history of AI What can AI do? What is this course?2Sci-Fi AI? What is AI?Think like humans Think rationallyAct like humans Act rationallyThe science of making machines that:Acting Like Humans? Turing (1950) “Computing machinery and intelligence” “Can machines think?” → “Can machines behave intelligently?” Operational test for intelligent behavior: the Imitation Game Predicted by 2000, 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, language understanding, learning Problem: Turing test is not reproducible or amenable to mathematical analysisThinking Like Humans? The cognitive science approach: 1960s ``cognitive revolution'': information-processing psychology replaced prevailing orthodoxy of behaviorism Scientific theories of internal activities of the brain What level of abstraction? “Knowledge'' or “circuits”? Cognitive science: Predicting and testing behavior of human subjects (top-down) Cognitive neuroscience: Direct identification from neurological data (bottom-up) Both approaches now distinct from AI Both share with AI the following characteristic:The available theories do not explain (or engender) anything resembling human-level general intelligence Hence, all three fields share one principal direction!Images from Oxford fMRI centerThinking Rationally? The “Laws of Thought” approach What does it mean to “think rationally”? Normative / prescriptive rather than descriptive Logicist tradition: Logic: notation and rules of derivation for thoughts Aristotle: what are correct arguments/thought processes? Direct line through mathematics, philosophy, to modern AI Problems: Not all intelligent behavior is mediated by logical deliberation What is the purpose of thinking? What thoughts should I (bother to) have? Logical systems tend to do the wrong thing in the presence of uncertaintyActing Rationally 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 Thinking can be in the service of rational action Entirely dependent on goals! Irrational ≠ insane, irrationality is sub-optimal action Rational ≠ successful Our focus here: rational agents Systems which make the best possible decisions given goals, evidence, and constraints In the real world, usually lots of uncertainty … and lots of complexity Usually, we’re just approximating rationality “Computational rationality” a better title for this course3Maximize Your Expected UtilityRational Agents An agent is an entity thatperceives and acts (moreexamples later) This course is about designingrational agents Abstractly, an agent is a functionfrom percept histories to actions: For any given class of environments and tasks, we seek the agent (or class of agents) with the best performance Computational limitations make perfect rationality unachievable So we want the best program for given machine resources[demo: pacman]A (Short) History of AI 1940-1950: Early days 1943: McCulloch & Pitts: Boolean circuit model of brain 1950: Turing's “Computing Machinery and Intelligence” 1950—70: Excitement: Look, Ma, no hands! 1950s: Early AI programs, including Samuel's checkers program, Newell & Simon's Logic Theorist, Gelernter's Geometry Engine 1956: Dartmouth meeting: “Artificial Intelligence” adopted 1965: Robinson's complete algorithm for logical reasoning 1970—88: Knowledge-based approaches 1969—79: Early development of knowledge-based systems 1980—88: Expert systems industry booms 1988—93: Expert systems industry busts: “AI Winter” 1988—: Statistical approaches Resurgence of probability, focus on uncertainty General increase in technical depth Agents and learning systems… “AI Spring”? 2000—: Where are we now? What Can AI Do?Quiz: Which of the following can be done at present? Play a decent game of table tennis? Drive safely along a curving mountain road? Drive safely along Telegraph Avenue? Buy a week's worth of groceries on the web? Buy a week's worth of groceries at Berkeley Bowl? Discover and prove a new mathematical theorem? Converse successfully with another person for an hour? Perform a complex surgical operation? Unload a dishwasher and put everything away? Translate spoken Chinese into spoken English in real time? Write an intentionally funny story?Unintentionally Funny Stories One day Joe Bear was hungry. He asked his friend Irving Bird where some honey was. Irving told him there was a beehive in the oak tree. Joe walked to the oak tree. He ate the beehive. The End. Henry Squirrel was thirsty. He walked over to the river bank where his good friend Bill Bird was sitting. Henry slipped and fell in the river. Gravity drowned. The End. Once upon a time there was a dishonest fox and a vain crow. One day the
View Full Document
Unlocking...