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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
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