1CS 188: Artificial IntelligenceSpring 2006Lecture 1: Introduction1/17/2006Dan Klein – UC BerkeleyMany slides from either Stuart Russell or Andrew Moore Administriviahttp://inst.cs.berkeley.edu/~cs188 Course StaffCourse StaffArlo FariaAria HaghighiJohn DeNeroDan KleinGSIsProfessorCourse Details Book: Russell & Norvig, AI: A Modern Approach, 2ndEd. Prerequisites: CS 61A or B: Experience with programming expected. Math 55 or CS 70: Propositional logic, basic probability. Work and Grading: 3+1 programming assignments (Python, groups of 1-2, 5 late days) 6 written assignments (individual write-ups, drop lowest) Mid-term and final Academic dishonesty policy Sections: 101 and 105 intended for non-majors First week: Friday 1-4pm in 275 Soda Hall: Python and Unix Account FormsToday What is AI? History of AI What can AI do? What is this course?What is AI?Act rationallyAct like humansThink rationallyThink like humansThe science of making machines that:2Acting 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, languageunderstanding, 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 courseRational Agents An agent is an entity thatperceives and acts (moreexamples next class) 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 resourcesAI-Adjacent Fields Philosophy: Logic, methods of reasoning Mind as physical system Foundations of learning, language, rationality Mathematics Formal representation and proof Algorithms, computation, (un)decidability, (in)tractability Probability and statistics Psychology Adaptation Phenomena of perception and motor control Experimental techniques (psychophysics, etc.) Economics: formal theory of rational decisions Linguistics: knowledge representation, grammar Neuroscience: physical substrate for mental activity Control theory: homeostatic systems, stability simple optimal agent designs3A (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, agents, everywhere… “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 English into spoken Swedish 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
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