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CMU CS 15780 - Lecture

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15-780: Graduate AILecture 1. Intro & LogicGeoff Gordon (this lecture)Tuomas SandholmTAs Erik Zawadzki, Abe Othman1Admin2http://www.cs.cmu.edu/~ggordon/780/http://www.cs.cmu.edu/~sandholm/cs15-780S11/15-780 󲰡 Graduate AI 󲰡Spring 2011Tuesdays and Thursdays from 10:30-Noon in GHC 4307.School of Computer Science, Carnegie Mellon University.PeopleThis class is taught byProfessors Geoff Gordon andTuomas Sandholm. The TAsare Abe Othman and ErikZawadzki.Office hours are at noon afterclass on Tuesday (Tuomas -GHC 9205) and Thursday(Geoff - GHC 8105). Abe andErik have their office hoursMonday at 8pm andWednesday at ??? in GHC9225. Recitations will takeplace Friday afternoon.Website3Website highlightsBook: Russell and Norvig. Artificial Intelligence: A Modern Approach, 3rd ed. Grading: 4–5 HWs, “mid”term, projectProject: proposal, 2 interim reports, final report, posterOffice hoursRecitation (when?)4Website highlightsAuthoritative source for readings, HWsPlease check the website regularly for readings (for Lec. 1–3, Russell & Norvig Chapters 7–9)5BackgroundSuggest familiarity with at least some of the following:Linear algebraCalculusAlgorithms & data structuresComplexity theoryLogic6Waitlist, AuditsAudits: register, fill out audit formMust do final project, but no HWs, testsWaitlist: if you’re on it, let us knowIf you need us to sign something, catch us after class or in office hours7Course email list15780students AT cs.cmu.eduTo subscribe/unsubscribe:email 15780students-request@…word “help” in subject or bodyBy the end of this week, everyone’s official email should be in the list—we’ll send a test message8Intro9Definition by examplesCard gamesPokerBridge Board gamesDeep BlueTD-GammonSamuels’s checkers player10Web search11Recommender systems12from http://www.math.wpi.edu/IQP/BVCalcHist/calctoc.htmlComputer algebra systems13Grand Challenge road raceRed team: Whittaker et alJunior: Thrun et al14RobocupVeloso et al15Landing a “bird”Standard airplane: laminar flow over wings“easy” simulation and control problemBirds: way beyond performance envelope of planesSecret: exploit turbulent flow (e.g., push off from vortex)But can’t efficiently solve diff eqs for simulation, much less use them to plan optimal landinghttp://www.youtube.com/watch?v=LA6XSrM0V_0&feature=player_embedded16Landing a “bird”Cory, Tedrake, et al.17Landing a “bird”Cory, Tedrake, et al.18Kidney exchangeIn US, ≥ 50,000/yr get lethal kidney diseaseCure = transplant, but donor must be compatible (blood type, tissue type, etc.)Wait list for cadaver kidneys: 2–5 yearsLive donors: have 2 kidneys, can survive w/ 1Illegal to buy/sell, but altruists/friends/family donate19Kidney ExchangePatientDonorPair 1PatientDonorPair 220Kidney ExchangePatientDonorPair 1PatientDonorPair 220Optimization: cycle coverCycle length constraint ⇒ NP-complete combinatorial optimizationNational market: ~10,000 patients at any one time21More examplesMotor skills: riding a bicycle, learning to walk, playing pool, … VisionSocial skills: attending a party, giving directions, …22More examplesNatural language understandingSpeech recognition23Common threadsFinding the needle in the haystackSearchOptimizationSummation / integration Set the problem up well (so that we can apply a standard algorithm)24Common threadsSequential decisions, delayed feedbackShoot or passSteering a carLanding a “bird”25Common threadsManaging uncertaintychance outcomes (e.g., dice)sensor uncertainty (“hidden state”)other agents26Classic AINo uncertainty, pure searchMathematicadeterministic planningSudokuThis is the topic of Part I of the coursehttp://www.cs.qub.ac.uk/~I.Spence/SuDoku/SuDoku.html27UncertaintyAdding outcome or sensor uncertainty to planning: unsolved problem, lots of current AI researchone-step decisions: graphical modelsoutcome only: MDPssensors: POMDPs, DBNsother agents: game theoryTopic of Part II of


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