Welcome to CompSci 171 spring 2007 Introduction to AI.Slide 2Academic (Dis)HonestySlide 4Meet HALDifferent Types of Artificial IntelligenceActing humanly: Turing TestActing rationally: rational agentAcademic Disciplines important to AI.History of AIState of the artConsider what might be involved in building a “intelligent” computer….Can we build hardware as complex as the brain?Must an Intelligent System be Foolproof?Can Computers play Humans at Chess?Can Computers Talk?Can Computers Recognize Speech?Recognizing human speech (ctd.)Can Computers Understand speech?Can Computers Learn and Adapt ?Can Computers “see”?Can Computers plan and make decisions?Intelligent Systems in Your Everyday LifeAI Applications: Consumer MarketingAI Applications: Identification TechnologiesAI Applications: Predicting the Stock MarketAI-Applications: Machine TranslationSummary of Today’s LectureICS-171:Notes 1: 1Welcome to CompSci 171 spring 2007 Introduction to AI.Instructor: Max Welling, [email protected] Office hours: Fr. 12-1pm in BH 4028Teaching Assistants: Vadim Bichutskiy Readers: Radu MarinescuRobert MateescuBo Gong Book: Artificial Intelligence, A Modern ApproachRussell & NorvigPrentice Hall http://www.ics.uci.edu/~welling/teaching/ICS171spring07/ICS171spring07.htmlICS-171:Notes 1: 2• Grading: -Homework (10%). -Quizzes (each other week) (20%) -Two projects (20%) -A midterm (20%) -A Final Exam (30%)Graded Quizzes and Assignmentscan be picked up from Distribution Center or in Discussion SectionGrading Disputes:Turn in your work for regrading at the discussion section to the TA within 1 week.Note: we will regrade the entire paper: so your new grade could be higher or lower. Course related issues can be addressed in the first 10 minutes of every class.ICS-171:Notes 1: 3Academic (Dis)Honesty•It is each student’s responsibility to be familiar with UCI’s current policies on academic honesty•Violations can result in getting an F in the class (or worse)•Please take the time to read the UCI academic honesty policy–in the Fall Quarter schedule of classes–or at: http://www.reg.uci.edu/REGISTRAR/SOC/adh.html•Academic dishonesty is defined as:–Cheating–Dishonest conduct–Plagiarism–CollusionICS-171:Notes 1: 4 Syllabus:Lecture 1. Introduction: Goals, history (Ch.1)Lecture 2. Agents (Ch.2)Lecture 3-4. Uninformed Search (Ch.3)Lecture 5-6 Informed Search (Ch.4)Lecture 7-8. Constraint satisfaction (Ch.5). Lecture 9-10 Games (Ch.6)Lecture 11. MidtermLecture 12. Propositional Logic (Ch.7)Lecture 13. First Order Logic (Ch.8) Lecture 14. Inference in logic (Ch.9) Lecture 15-16 Uncertainty (Ch.13)Lecture 17. Learning (Ch.18). Lecture 18. ?? Lecture 19-20. Statical Learning Methods (Ch.20)This is a very rough syllabus. It is almost certainly the case that we will deviate from this. Some chapters will be treated only partially.ICS-171:Notes 1: 5Meet HAL•2001: A Space Odyssey–classic science fiction movie from 1969•HAL–part of the story centers around an intelligent computer called HAL–HAL is the “brains” of an intelligent spaceship–in the movie, HAL can•speak easily with the crew•see and understand the emotions of the crew•navigate the ship automatically•diagnose on-board problems•make life-and-death decisions•display emotions•In 1969 this was science fiction: is it still science fiction?ICS-171:Notes 1: 6Different Types of Artificial Intelligence•Modeling exactly how humans actually think–cognitive models of human reasoning •Modeling exactly how humans actually act–models of human behavior (what they do, not how they think)•Modeling how ideal agents “should think”–models of “rational” thought (formal logic)–note: humans are often not rational!•Modeling how ideal agents “should act” –rational actions but not necessarily formal rational reasoning–i.e., more of a black-box/engineering approach•Modern AI focuses on the last definition–we will also focus on this “engineering” approach–success is judged by how well the agent perform-- modern methods are inspired by cognitive & neuroscience (how people think).ICS-171:Notes 1: 7Acting humanly: Turing Test•Turing (1950) "Computing machinery and intelligence":•"Can machines think?" "Can machines behave intelligently?"•Operational test for intelligent behavior: the Imitation Game•Suggested major components of AI: - knowledge representation - reasoning, - language/image understanding, - learningCan you think of a theoretical system that could beat the Turing test yet you wouldn’t find very intelligent?ICS-171:Notes 1: 8Acting rationally: rational agent• Rational behavior: Doing that was is expected to maximize one’s “utility function” in this world.•An agent is an entity that perceives and acts. •A rational agent acts rationally.•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 resourcesICS-171:Notes 1: 9Academic Disciplines important to AI.•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.•Economics utility, decision theory, rational economic agents •Neuroscience neurons as information processing units.•Psychology/ how do people behave, perceive, process Cognitive Scienceinformation, represent knowledge. •Computer building fast computers engineering•Control theory design systems that maximize an objectivefunction over time •Linguistics knowledge representation, grammarICS-171:Notes 1: 10History of AI•1943 McCulloch & Pitts: Boolean circuit model of brain•1950 Turing's "Computing Machinery and Intelligence"•1956 Dartmouth meeting: "Artificial Intelligence" adopted•1950s Early AI programs, including Samuel's checkersprogram, Newell & Simon's Logic Theorist, Gelernter's Geometry Engine•1965 Robinson's complete algorithm for logical reasoning•1966—73 AI discovers computational complexityNeural network research almost disappears•1969—79 Early development
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