LectureOutlinesProfessorWei-MinShen

*  Welcome*  Classstructures/rules(syllabus,HM,projects,tests)*  WhatisAI?*  WhataretherelatedfieldsofAI?*  HowwasAIstarted?(history)*  WhatisthestateoftheartofAI?

Wk1Ch1:Introduction

*  ShowSuperBotmoviesandsimulations*  Project1:Descriptionandassignment*  Agent/Robot*  Whatisinsidearobot/agent?(see,act,think,learn)*  Whatdoesitseek?(goal,utility,solveproblems,“fame/

fortune”)

*  Environment/world*  Whatcanbeseen?*  Whatcanbechanged?*  Whoelsearethere?(Obstacleandotheragents)

*  HW:Defineanenvironment/worldandarobotbyyourself

Wk1Ch2:IntelligentAgents

*  Howtorepresentaproblem?*  states,actions,initials,goals,(e.g.,tic-tac-toe)

*  Howtosolveaproblem?*  Search:fromheretothere,initialstogoals*  Depth-first,breadth-first

*  Howgoodisyoursolution?(fig6.1,ALFE)*  Howgoodisyourstate?Howcostlyisanaction?*  Best-first,DynamicProgramming,A*,etc.*  Canyouguaranteeanything?(optimalvsheuristic)

*  Howmuchdoyouwanttopayforyoursolution?*  Howdeep/widecanyougo?*  Predeterminedvsdynamic(e.g.,iterativedeepening)

*  Onewayormanyways(bi-directional)?*  Howbigisaproblem?Canyouputthewholeworldinyourhead?*  TowerofHanoi,chess,robot-and-world,

*  HM:statespaceforTOH,assignvaluesforstateandactions.

Wk2Ch3:SolvingProblems

*  Howtofindtheextremesoff(x)?*  Whyisitsoimportant?*  Whyisitsohard?(noonehasofferedageneralsolution!)*  Whichwaytogo?*  Howmuchcanyousee?(localvsglobal)*  Howmanypointscanyouremember?*  Howbigisyourstep?(skipTHEpoint?)*  Howwellcanyouguess?*  Howmuchdoyouknowaboutthefunction?*  Howdoyouknowyouaredone?*  Willthefunctionchangebyitself?

*  HW:yourownanswersfortheabovequestions

Wk2Ch4:Optimization

*  Keyidea:optimizationforyouselfandyouropponent*  Whenyoucanseethewholegame*  Example,Tic-tac-toe(fig5.1)*  Optimal:CanIalwayswin,orneverloss?*  Key:lookahead,chosethebest*  Minmaxalgorithm,Alpha-betapruning*  Fig5.2,keyidea:whatcanIgainiftheopponentgivesmetheleast

*  Whencannotseethewholegame(chess)*  howfartolook?howgoodisastate?Canyoupredictyouropponent?

*  Whenthereisarandomplayer(dice)*  Israndomplayinganygood?*  WhatarethebestAIgameplayers?*  HW:Writeaminmaxalgorithmfortic-tac-toe

Wk3Ch5:GamePlaying

*  Example:MapColoring,canyouthinkofanother?*  Formalism:Variables,ValueSets,Constraints,*  Independentsubset?Existingcycles?

*  Thebasicalgorithms*  GlobalBacktracking*  Ordervariables:themostconstrainedvariablesfirst*  Satisfyoneatatime,ifstuck,backtracking

*  Localsearch:heuristic,findthemin-conflictssolutionlocally*  Centralizedvs.distributed(DCOP)

*  HW:Magicsquare,defineitandsolveit

Wk3Ch6:ConstraintSatisfaction

*  Therealworldanditsrepresentationbylogics*  Theworldyoucreatedinsimulation(e.g.wumpus)*  syntax(onpaper,α,β)*  semantics(inworld/modelM),”satisfy”*  Propositionallogic:truthtablefor*  Theoremproving,inferencerule,resolution,hornclause,

forward/backwardchaining,*  Propositionalagent:currentstate,goalstate,localsearch,

globalsearch*  HW:PLrulesforWumpusorTic-Tac-Toe

Wk4Ch7-9:Logics

α |= β ⇔ M (α )⊆M (β )¬,∧,∨,⇒,⇔

*  Syntaxandsemantics*  Addvariables(predicates)andquantifiers*  Knowledgeengineering*  Captureandrepresenthumanknowledge,e.g.,using

FOL

*  HW:FOLrulesforWumpusorTic-Tac-Toe

Wk4Ch8:First-orderlogic

*  ModusPonenswithvariables*  Unification:findingthevaluesforvariables*  1.Forwardchaining*  E.g.productionsystems,ACT,SOAR*  2.Backwardchaining*  E.g.,Prolog*  3.Resolution*  ConverttoCNF,eliminatingP,-P.

Wk4Ch9:InferenceinFOL

*  Project2:Robotsearchesatargetroomonamap*  RepresentingActions*  Preandpostcondition(singleorsetofstates)

*  Searchingforoptimalsolutions*  Graph-Based,Forward,Backward.*  DynamicProgramming,A*,Q-search(readALFE6.1)

*  Searchingforsatisfacingsolutions*  means-end-analysis*  Heuristicplanning

*  Otherplanningmethods*  Logicdeduction:Booleansatisfactory,situation-calculus*  Constraint-satisfaction*  Partiallyorderedplans

Wk5Ch10:ActionsandPlanning

*  Whyhard?Time,duration,resources,deadlines,orders,unknowneffects,etc.*  Deterministicapproaches*  Thelongestfirst(criticalpath,takeatleastthislong)*  HierarchicalPlanning*  High-levelactionsfirst*  Findprimitivesolutions*  Findabstractsolutions

*  Non-deterministicapproaches*  Sensor-based,contingency,incremental,*  Multi-agentplanning(collaboration&coordination)

*  HW:modifyWumpusasaschedulingproblem

Wk5Ch11:Scheduling

*  WhatisKR?Representation+Processes*  Whyimportant?Simon’sexamplecards/ttt*  Format:Attribute-basedandRelational-based*  Ontology*  Objects:(attributes,part-of),bothphysicalandmental*  Events(process,time),*  Relations,functions,operators:mostlymental

*  Reasoning*  Semanticnet,logic,default(monotonicornot),truthmaintenance

*  Example*  WorkoutanontologyfortheWumpusbasedontheexampleofInternet

shoppinginthebook

Wk6Ch12:KnowledgeRepresentation

*  Notallknowledgearecertain*  Earlierstoriesofexpertsystems(medical,legal)

*  Twobigchallenges*  Commonsense(vague):“Waterflowsdown”*  Uncertainty*  Probability=?=LogicofScience

Wk6:LogicsandProbabilities

*  Whereisprobability?(intheworldorinyourmind)*  Notation:variableX,valuexi,P(X=xi),P(X)denotesforallvaluesofX,

P(X,Y),P(X,y)*  Remembertwoaxioms*  Sumaxiom:P(A|B)+P(~A|B)=1*  Productaxiom:P(AB|C)=P(A|C)P(B|AC)=P(B|C)P(A|BC)

*  Theycandomuchmorethanlogics*  Deductivereasoning:*  IfA->BandA,thenB*  IfA->Band~B,then~A*  IfA->BandB,then“Abecomemoreplausible”

*  Inductivereasoning:*  IfA->Band~A,then“Bbecomelessplausible”*  IfA->”Bbecomesmoreplausible”andB,then“Abecomemoreplausible”

*  HM:workoutthemathwhytheaboveistrue(ALFE,p102)

Wk6-7Ch13:Probability

*  Propositionallogic+probability*  Fulljointdistribution(fig13.3:truthtable+probability)*  Inferences*  Usingfulljointdistributions(1stpartofproductrule)*  Marginalizationorsummingout*  UsingBayesianrule(2ndpartofproductrule)*  ExampleforWumpusworld

*  Bayesiannetwork:topology+CPTs(fig14.2)*  Comparetotruth-tableformat(25rows)*  Inferences,exactvsapproximate

*  RelationalandFOL+probability*  Otheruncertainreasoningtheories*  Dempster-Shafer,Fuzzysetsandlogic,

Wk7Ch14:ProbabilityReasoning

*  Readings:AIMA15*  Examples:Speechrecognition,see=?=truth

*  Given:amapofcoloredrooms,andanexperience(act/see):e1:t

*  Compute:whichroomtherobotwas/is/will-bein:X*  States,observations,transitions,actions,sensors,

*  Definitions(ALFE5.10)*  Wecaninfer:*  P(Xt|e1:t)whichroomIaminnow(stateestimation)*  P(Xt+k|e1:t)whichroomIwillbeinattimet+k(prediction)*  P(Xk|e1:t)whichroomIwasinattimek(smoothing)*  P(x1:t|e1:t)whatroomsIhavebeeninthepast(explain)*  P(Mt|e1:t)canIimprovemymap(learning)

*  HW:Defineanautomataforyourroomsinthesimulation

Wk8:HMM,DBN,Automata

*  Readings:AIMA16,17*  StateshaveutilityU(s)*  MaximumExpectedUtility*  EU(a|e)=Sum[P(s|a,e)U(s)]

*  MarkovDecisionProcess(MDP):state,action,transitionP(s’|s,a),reward:sèr,or(s,a)èr*  Solutionsarerepresentedaspolicy:sèa.*  Totaldiscountedexpectedreward(Bellman):*  Equation17.5orinALFEpage167.

*  PartiallyObservedMDP(POMDP)*  Statescannotbecompletelyobserved*  NeedasensormodelP(z|s)

*  Objective:findingtheoptimalpolicybasedonutilities*  ReviewforMidtermexam(ifyouhaveanyquestions)*  HW:DefineyourownworldandrobotusingPOMDP

Wk9:Utility,Actions,MDP,POMDP

*  Goodluckonyourtest!

Wk10:Midterm

*  TypesReadings:AIMACh18,ALFE4*  Project3:arobotlearnsamodelofacoloredroomtofindatargetquickly*  TypesofMachineLearning*  Unsupervisedvs.Supervised(examples,reinforcement,andenvironment)*  Incrementalvs.patch;onlinevs.offline

*  KeyIdeas:Hypothesesandexamples*  Hypothesesspace(ALFEfig4.3):Attribute-basedvsrelation-based*  Learning=Searchinhypothesisspaceforonetocoverexamples(bothseenandunseen)*  Falsenegative(covertoolittle)àGeneralization*  Falsepositive(covertoomuch)àSpecialization*  Correctnesscriteria:Overfitting,Identical,PAC,andothers

*  AlgorithmsforAttribute-BasedLearning*  VersionSpace*  DecisionTrees(HW:)*  NeuralNetwork(fig18.19andALEF4.11)(HW:)*  LinearRegression:Learnh=ax+bfromexamples(x,y)byminimizingΣ(y-h)2[eq18.2-3]*  Supportvectormachines(HW:18.16,18.25)*  EnsembleLearningandBoosting*  Learnanewhypothesisbasedontheresultsofthelasthypothesis(weighttheexamples)

Wk11:SupervisedLearning1:Attribute-Based

*  Supportvectormachines(HW:18.16,18.25)*  Thekeyideaofmax-marginseparator(fig18.30)*  Supportvectors:thepointsclosesttotheseparator,all

otherdatapointshavezeroweights*  Whenthedataarenotlinearlyseparable(fig18.31),

rewriteseparatorinahigherdimension*  Itisnon-parametric,but(eq.18.13)canusefewer

examplesthanthetrainingdata

Wk11:SupportVectorMachines

*  Readings:AIMA19,ALFE4.7-4.10*  Ageneralchallenge:agoodhypothesisspace

*  Tooopen:tooslowtoconverge*  Toorestrict:ruleoutthegoodhypotheses*  Goodprioriknowledgewouldhelp

*  Relation-basedlearning*  Deductive(e.g.EBL):useprioriknowledgetoexplainexamplesànewhypotheses(fig19.7)*  Inductivealgorithms(ALFE4.7-4.10)*  FOIL*  CDL(ComplementaryDiscriminationLearning)

*  HW:learntherelationalrulesofXfromexamples

Wk12:SupervisedLearning2Relation-Based

*  Readings:AIMA20,ALFE5.10*  Clustering*  NaïveBayesModels*  ByBayesianApproach(Cheeseman’sCLASSIFY)*  K-meanalgorithm(seeAndrewNg’slecture)

*  TheEMalgorithm*  ClusteringwithmixturesofGaussians(fig20.11)*  Bayesiannetworkwithhiddenvariables(fig20.13)*  HiddenMarkovmodels(fig20.14)*  PO-MDP(ALFE5.10)

*  HW:LearnaBayesianmodelfromexamples

Wk13:UnsupervisedLearningProbability-Based

*  ReinforcementLearning*  Delayedsupervision/reward*  Whenweonlyknowtherewardsfromthegoals

Week13:ReinforcementLearning

*  HowtolearningPOMDPwithoutknowingthestates

Wk14:Surprise-BasedLearning

*  ActionsandPerceptions*  Communication*  Naturallanguageoverview*  Speechunderstanding*  Collaboration*  Self-Organizationandself-reconfiguration*  Rubenstein2010.

Wk15:IntegratedIntelligence

*  Relational+ProbabilisticModels*  Existingresearch:FOL+Probability

*  StructuralLearning*  Existingresearch:SBL,*  Scaleuptotherealworld*  Integratewithallaspects*  Reconfigurebothmindandbody

Mk16:TowardstheFuture

*  GoodLuck!

Mk17:FinalExam