CMSC 671 Fall 2001Today’s classCourse OverviewCourse enrollmentCourse materialsHomework and grading policiesAcademic integrityStaff availabilityIntroduction to Artificial IntelligenceBig QuestionsWhat is AI?Other possible AI definitionsWhat’s easy and what’s hard?HistoryFoundations of AIWhy AI?Possible ApproachesThink wellAct wellThink like humansAct like humansTuring TestElizaSlide 24Colby’s PARRYParry meets ElizaThe Loebner ContestWhat can AI systems doWhat can’t AI systems do yet?LISPWhy Lisp?Why all those parentheses?Basic Lisp typesBasic Lisp functionsUseful help facilitiesGreat! How can I get started?Slide 37CMSC 671CMSC 671Fall 2001Fall 2001Professor Marie desJardins, [email protected], ECS 216, x3967TA: Suryakant Sansare, [email protected]’s class•Course overview•Introduction–What is AI?–History of AI•Lisp – a first lookCourse OverviewCourse OverviewCourse enrollment•Everyone who was on the wait list as of Tuesday 8/28 has been permitted to enroll.•If you are not enrolled and not on the wait list, take a form and fill it out. (You don’t need to provide a transcript as it says.) •I’ll let you know by the next class whether you can enroll.Course materials•Course website: http://www.cs.umbc.edu/671/Fall01/–Course description and policies (main page)–Course syllabus and schedule (subject to change!)–Pointers to homeworks and papers (send me URLs for interesting / relevant websites, and I’ll add them to the page!)–FAQ (eventually), class slid•Course mailing list: [email protected]–Send mail to [email protected]–subscribe cmsc671 Your Name–Send general questions to the list–Requests for extensions, inquiries about status, requests for appointments should go directly to Prof. desJardins and/or SuryakantHomework and grading policies•Seven homework assignments (mix of written and programming)•Due every other Tuesday at midnight•One-time extensions of up to a week will generally be granted if requested in advance•Last-minute requests for extensions will be denied•Late policy:–.000001 to 24 hours late: 25% penalty–24 to 48 hours late: 50% penalty–48 to 72 hours late: 75% penalty–More than 72 hours late: no credit will be givenAcademic integrity•Instructor’s responsibilities:–Be respectful–Be fair–Be available–Tell the students what they need to know and how they will be graded•Students’ responsibilities:–Be respectful–Do not cheat, plagiarize, or lie, or help anyone else to do so–Do not interfere with other students’ academic activities•Consequences include (but are not limited to) a reduced or failing grade on the assignment, or in the classStaff availability•Prof. desJardins–Official office hours: Tues. and Thurs. before class (2:45 – 3:45)–Often need to leave right after class, so that’s not a good time to chat–Appointments may also be made by request (24 hours notice is best)–Drop in whenever my door is open –Will try to respond to e-mail within 24 hours–Direct general questions (i.e., those that other students may also be wondering about) to the class mailing list•TA Suryakant–Office hours: Tues. and Thurs. after class (5:30 – 6:30)Introduction to Introduction to Artificial Artificial IntelligenceIntelligenceChapter 1Big Questions•Can machines think?•And if so, how?•And if not, why not?•And what does this say about human beings? •And what does this say about the mind?What is AI?•There are no crisp definitions•Here’s one from John McCarthy, (He coined the phrase AI in 1956) - see http://www.formal.Stanford.EDU/jmc/whatisai/)Q. What is artificial intelligence? A. It is the science and engineering of making intelligent machines, especially intelligent computer programs. It is related to the similar task of using computers to understand human intelligence, but AI does not have to confine itself to methods that are biologically observable. Q. Yes, but what is intelligence? A. Intelligence is the computational part of the ability to achieve goals in the world. Varying kinds and degrees of intelligence occur in people, many animals and some machines.Other possible AI definitions•AI is a collection of hard problems which can be solved by humans and other living things, but for which we don’t have good algorithmic solutions–e.g., understanding spoken natural language, medical diagnosis, circuit design, etc.•AI Problem + Sound theory = Engineering problem•Some problems used to be thought of as AI but are now considered not–e.g., compiling Fortran in 1955, symbolic mathematics in 1965•AI is thus, by nature, pre-scientific in Kuhn’s termsWhat’s easy and what’s hard?•It’s been easier to mechanize many of the high level tasks we usually associate with “intelligence” in people–e.g., Symbolic integration, proving theorems, playing chess, medical diagnosis, etc.•It’s been very hard to mechanize tasks that lots of animals can do–walking around without running into things–catching prey and avoiding predators–interpreting complex sensory information (e.g., visual, aural, …)–modeling the internal states of other animals from their behavior–working as a team (e.g. with pack animals)•Is there a fundamental difference between the two categories?HistoryFoundations of AIComputerScience & EngineeringAIMathematicsCognitiveSciencePhilosophyPsychology LinguisticsBiologyEconomicsWhy AI?•Engineering: To get machines to do a wider variety of useful things–e.g., understand spoken natural language, recognize individual people in visual scenes, find the best travel plan for your vacation, etc.•Cognitive Science: As a way to understand how natural minds and mental phenomena work–e.g., visual perception, memory, learning, language, etc.•Philosophy: As a way to explore some basic and interesting (and important) philosophical questions–e.g., the mind body problem, what is consciousness, etc.Possible ApproachesThinkActLike humansWellGPSElizaRationalagentsHeuristicsystemsAI tends to work mostly in this areaThink well•Develop formal models of knowledge representation, reasoning, learning, memory, problem solving, that can be rendered in algorithms.•There is often an emphasis on a systems that are provably correct, and guarantee finding an optimal solution. ThinkActLike humansWellGPSElizaRationalagentsHeuristicsystemsAct well•For a given set of inputs, generate an