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INF3490/4490Autumn2018INF3490/4490—BiologicallyInspiredComputing

November30th,2018Examhours:09:00–13:00Permittedmaterials:NoneThecourseteacherswillvisittheexamroomatleastonceduringtheexam.Theexamtextconsistsofproblems1-40(multiplechoicequestions)tobeansweredbyselectingtrueorfalseforeachstatement.Ifyouthinkastatementcouldbeeithertrueorfalse,considerthemostlikelyuse/case.Problems41-43areansweredbyenteringtext(preferablyinEnglishlanguage).Problems1-40haveatotalweightof80%,whileproblems41-43haveaweightof20%.ScoringinmultiplechoicequestionsEachproblemhasavariablenumberoftruestatements,butthereisalwaysonetrueandonefalsestatementforeachproblem.0.5pointisgivenforeachcorrectlymarkedstatement.Further,anincorrectlymarkedstatementoranunmarkedstatement(s)resultsin0point.Themaximumscoreforaquestionis2pointsandtheminimumis0.Sinceitispossibletogetapositivescorejustbyrandomanswering,thefinalgradingthresholdswillbeadjustedaccordingly.

1 1OptimizationAlgorithmsWhichoptimizationalgorithm(s)canguaranteetofindthebestsolutioninanysearchlandscape?EvolutionaryalgorithmsSelectanalternative

Hillclimbing/LocalSearchSelectanalternative

ExhaustiveSearchSelectanalternative

GradientDescentSelectanalternative:

True

False

False

True

False

True

True

False

Maximummarks:2

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2 2ExplorationWhichis/aretrueaboutexplorationExplorationisonlyusedforcontinuousoptimizationSelectanalternative:

ExplorationisonlyusedfordiscreteoptimizationSelectanalternative

ExhaustivesearchisapurelyexploratorytechniqueSelectanalternative

HillclimbingisapurelyexploratorytechniqueSelectanalternative

True

False

True

False

False

True

False

True

Maximummarks:2

3 3OptimizationAlgorithmsOptimizationalgorithmsSimulatedannealingbalancesexploitationandexplorationwiththetemperatureparameterSelectanalternative:

GradientdescentcanbeusedforcontinuousoptimizationSelectanalternative

Gradientdescentfocusesmoreonexplorationthanexploitation

Selectanalternative

True

False

True

False

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Selectanalternative

SimulatedannealingonlyselectsneighborsolutionsthatarebetterthanthecurrentsolutionSelectanalternative

False

True

True

False

Maximummarks:2

4 4EvolutionaryAlgorithmsWhichis/arepartsofevolutionaryalgorithms?VariationoperatorsSelectanalternative:

ApopulationSelectanalternative

AnannealingscheduleSelectanalternative

AmomentumSelectanalternative

True

False

False

True

True

False

False

True

Maximummarks:2

5 5PermutationrepresentationWhichproperty/propertiesdoweoftenaimtoconservewhenapplyingvariationoperatorstogenotypesrepresentedaspermutations?

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TemperatureSelectanalternative:

AdjacencySelectanalternative

GradientsSelectanalternative

OrderSelectanalternative

False

True

True

False

False

True

False

True

Maximummarks:2

6 6BinarygenotypeForwhichproblem(s)wouldabinarygenotypebeanaturalchoice?Theknapsackproblem:Givenndifferentobjects,eachwithaknownweightwiandvaluevi,andaknapsackwithagivencapacityW,filltheknapsackwiththoseobjectsmaximizingitstotalvalue,whilestayingwithintheweightcapacity.Selectanalternative:

ThetravellingsalesmanproblemSelectanalternative

Optimizingtheshapeofanairplane'swings:GivenNdifferentpositionalongthewing,optimizetheheightandthicknessofthewingateachofthosepositions.Selectanalternative

False

True

True

False

True

False

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Optimizingarecipebyselectingasubsetfromalistofingredients.E.g.fromthelist[cheese,bacon,eggs,bread]onerecipe(anindividual)couldbe[eggs,bread]Selectanalternative

False

True

Maximummarks:2

7 7SelectionSelectioninEvolutionaryAlgorithmsEvolutionaryalgorithmsalwaysselecttheindividualwiththehighestfitnessSelectanalternative:

EvolutionaryalgorithmswithparentselectiondonotneedsurvivorselectionSelectanalternative

Intournamentselection,largertournamentsizesresultinahigherselectionpressureSelectanalternative

StochasticuniversalsamplingisaformoffitnessproportionateselectionSelectanalternative

True

False

False

True

True

False

False

True

Maximummarks:2

8 8DiversityWhichis/aremethod(s)toencouragediversityinevolutionaryalgorithms?Speciation

Selectanalternative:

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CrowdingSelectanalternative

FitnessSharingSelectanalternative

ElitismSelectanalternative

True

False

True

False

False

True

True

False

Maximummarks:2

9 9EvolutionStrategiesWhichgenotype(s)is/aretypicallyappliedinevolutionstrategies?Listsoffloating-pointnumbersSelectanalternative:

Asinglefloating-pointnumberSelectanalternative

PermutationsSelectanalternative

True

False

False

True

True

False

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ListsofbinarynumbersSelectanalternative

True

False

Maximummarks:2

10 10EvolutionaryAlgorithmsubtypesWhichis/areasubtypeofEvolutionaryAlgorithm?GeneticProgrammingSelectanalternative:

EvolutionaryProgrammingSelectanalternative

SimulatedAnnealingSelectanalternative

HillclimbingSelectanalternative

False

True

False

True

False

True

True

False

Maximummarks:2

11 11PerformancemeasurementsforevolutionaryalgorithmsWhichis/aretrueaboutperformancemeasurementsforevolutionaryalgorithms?Ondesignproblems,wecaremoreaboutpeakperformancethanworst-caseperformance


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Onrepetetiveproblems,wecaremoreaboutpeakperformancethanworst-caseperformanceSelectanalternative

Thesuccessrateofanevolutionaryalgorithmismeasuredbytakingthemedianfitnessofthebestindividualintheendofarun,andaveragingthatacrossNdifferentrunsSelectanalternative

Tocomparetheperformanceofdifferentevolutionaryalgorithms,weneedtoperformseveralindependentrunsofeachofthemSelectanalternative

False

True

False

True

False

True

True

False

Maximummarks:2

12 12DesignProblemsWhichis/aretrueaboutdesignproblemsEveryday,IKEAoptimizesthenumberofmeatballstoproduce,dependingontheexpectednumberofvisitorsestimatedwithfactorssuchasweather,seasonalchanges,etc.Thismeatballoptimizationisanexampleofadesignproblem.Selectanalternative:

ThecostsofcarryingouttheoptimizedplanisusuallyfargreaterthanthecostofcomputingitSelectanalternative

Wecanusuallydomultiplerunsoftheoptimizationalgorithmwhensolvingadesignproblem

Selectanalternative

False

True

True

False

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Selectanalternative

OnlyevolutionaryalgorithmscansolvedesignproblemsSelectanalternative

False

True

True

False

Maximummarks:2

13 13MultiobjectiveevolutionaryalgorithmsWhichdifferbetweenmultiobjectiveandsingle-objectiveevolutionaryalgorithms?GeneticrepresentationsSelectanalternative:

VariationoperatorsSelectanalternative

ThemethodforselectingindividualsforthenextgenerationSelectanalternative

DiversitymaintenanceSelectanalternative

False

True

False

True

True

False

True

False

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Maximummarks:2

14 14MachinelearningWhatistrueaboutmachinelearning?MachinelearningtypicallyworksbestwithsmallamountsofdataSelectanalternative:

GeneralizationinmachinelearningmeanslearningtohandledatanotseenduringtrainingSelectanalternative

Machinelearningisiterative,typicallyrequiringtrainingoneachdatapointmorethanonceSelectanalternative

MachinelearningsystemscanneveroutperformhumanexpertsSelectanalternative

False

True

False

True

False

True

False

True

Maximummarks:2

15 15SupervisedlearningWhichis/aretrueforsupervisedlearning?SupervisedlearningrequiresatargetoutputforeachtrainingsampleSelectanalternative:

Classificationandregressioncanbesupervisedlearningtasks

Selectanalternative

True

False

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Selectanalternative

LearningtoplaychessbyplayingmatchesagainstanopponentisanexampleofsupervisedlearningSelectanalternative

LearningtorecognizedogsinimagesbyanalyzingimageslabelledwiththeircontentisanexampleofsupervisedlearningSelectanalternative

False

True

True

False

True

False

Maximummarks:2

16 16NeuralNetworksWhichis/aretrueaboutneuralnetworks?McCullochandPittsNeuronspreciselyreplicatechemicalprocessesinsidebiologicalneuronsSelectanalternative:

ThefirstartificialneuralnetworkstypicallyappliedasigmoidfunctionasactivationfunctionSelectanalternative

AssembliesofneuronsarecapableofuniversalcomputationSelectanalternative

PerceptronshavemultiplehiddenlayersSelectanalternative

False

True

False

True

True

False

True

False

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Maximummarks:2

17 17PerceptronlearningruleWhichareelementsintheperceptronlearningrule?Alearningrate,ηSelectanalternative:

Theneuron'sinput,xiSelectanalternative

Theerrorattheoutput,(ti-yi)

Selectanalternative

Thepreviousweightupdate,Δwij

Selectanalternative

False

True

False

True

False

True

True

False

Maximummarks:2

18 18NeuralnetworksWhichis/aretrueaboutneuralnetworks?PerceptronsarelimitedtolearninglinearlyseparableproblemsSelectanalternative:

False

True

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DifferentlayersmayapplydifferentactivationfunctionsSelectanalternative

MultilayerneuralnetworkslearnbyonlyadjustingweightsattheoutputlayerSelectanalternative

Gradientdescent-basedtrainingislimitedtotrainingneuralnetworkswithuptoonehiddenlayerSelectanalternative

True

False

False

True

True

False

Maximummarks:2

19 19BackpropagationWhatistrueaboutbackpropagation?WeightsareupdatedintheforwardphaseSelectanalternative:

DeltasaremultipliedbyweightsastheyarepropagatedbackwardsthroughanetworkSelectanalternative

BackpropagationcanbeappliedwhenusingathresholdactivationfunctionSelectanalternative

Backpropagationneverchangestheweightsconnectedtotheoutputsoftheneuralnetwork

Selectanalternative

True

False

True

False

True

False

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Selectanalternative

True

False

Maximummarks:2

20 20TrainingneuralnetworksWhichis/aretrueabouttrainingneuralnetworks?Inbatchtraining,weightsareupdatedaftereverytrainingsampleSelectanalternative

BackpropagationisaformofgradientdescentlearningSelectanalternative

MinibatchtrainingisacompromisebetweensequentialtrainingandbatchtrainingSelectanalternative

Inminibatchtraining,deltasareaccumulatedacrossseveraltrainingsamplesSelectanalternative:

True

False

False

True

False

True

True

False

Maximummarks:2

21 21DecisionboundaryWhatistrueaboutthisdecisionboundary?

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ItisprobablyoverfittingSelectanalternative:

ItwillprobablygeneralizepoorlytonewdataSelectanalternative

ItprobablyshowsperfectperformanceontrainingdataSelectanalternative

ItprobablyshowsperfectperformanceontestdataSelectanalternative

False

True

True

False

False

True

True

False

Maximummarks:2

22 22MultilayerneuralnetworkConsidertheANNbelow.AandBareinputs,Eistheoutputnode,andnumbersindicatetheweightsofconnections.Therearenobiasinputs.Assumeallneuronsapplylinearactivationfunctions,g(u)=u.WeinputthevaluesA=1,B=1.Whichvalue(s)areoutputbyanyofthehiddenneurons?

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-2Selectanalternative:

5Selectanalternative



True

False

True

False

False

True

False

True

Maximummarks:2

23 23ReinforcementLearningInreinforcementLearningTherewardtellsuswhichactionweshouldhavetaken.


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TherewardisneverdelayedSelectanalternative

TherewardisalwaysapositivevalueSelectanalternative

Itispossibletoreceivetheentirerewardattheend.Selectanalternative

False

True

False

True

True

False

False

True

Maximummarks:2

24 24ReinforcementLearningQ-LearningvsSARSAQ-Learningalwaysassumesoptimalaction.Selectanalternative:

SARSAresultsinefficientbutriskysolutions.Selectanalternative

SARSAispureexploitationsearch.Selectanalternative

False

True

False

True

True

False

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Q-learninggeneratessaferresults.Selectanalternative

False

True

Maximummarks:2

25 25ReinforcementLearningTheQ-learningalgorithm:Alwaysassumetheoptimalaction.Selectanalternative:

Followsagreedysearchbehavior.Selectanalternative

Thevalueiscalculatedbyaveragingallpossibleactions.Selectanalternative

Followsastate-valuefunction.Selectanalternative

True

False

False

True

True

False

False

True

Maximummarks:2

26 26DeepLearningDeeplearningIsalwaysanunsupervisedlearningmethod.


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Isusuallyimplementedusinganeuralnetworkarchitecture.Selectanalternative

Networksneedatleastonelayerforeveryinputvariable.Selectanalternative

Requireslotsoftrainingdata.Selectanalternative

False

True

False

True

False

True

False

True

Maximummarks:2

27 27SupportVectorMachinesSupportvectorsFindtheclassificationlineswiththehighestmargin.Selectanalternative:

Canrepresenttheentiredataset.Selectanalternative

Shouldhavethemaximumdistancefromtheseparatorline.Selectanalternative

False

True

True

False

False

True

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Resultinthesmallestmarginalarea.Selectanalternative

True

False

Maximummarks:2

28 28SupportVectorMachinesSlackvariablesAreusedtominimizethenumberofsupportvectors.Selectanalternative:

Aimtominimizetheclassificationerror.Selectanalternative

Replacetheoriginalvariables(features)innon-linearlyseparableproblems.Selectanalternative

Areusedforlinearly-separableproblems.Selectanalternative

False

True

False

True

False

True

False

True

Maximummarks:2

29 29SupportVectorMachinesKernels

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Kernelsareusedtomaketheinputdatalinearlyseparable.Selectanalternative:

Theobjectiveistominimizethemarginalarea.Selectanalternative

Replacetheinputfeatureswithafunctionofthatfeature.Selectanalternative

Requireaminimalnumberofsupportvectors.Selectanalternative

True

False

True

False

True

False

False

True

Maximummarks:2

30 30EnsembleLearningAdaBoostalgorithmUpdatestheweightsbasedonpreviouserrors.Selectanalternative:

Theweightofeachdatapointisfixedduringthelearning.Selectanalternative

Initially,allweightsareequal.

Selectanalternative

False

True

True

False

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Selectanalternative

Initially,allweightsareselectedasrandomnumbers.Selectanalternative

True

False

False

True

Maximummarks:2

31 31EnsembleLearningInBaggingalgorithmsBootstrapsamplesareusedwithoutreplacement.Selectanalternative:

Eachdatapointisusedonlyinoneoftheclassifiers.Selectanalternative

Theweightofeachdatapointisbasedonitspreviouserror.Selectanalternative

Randomsamplingwithreplacementisusedfortraining.Selectanalternative

False

True

False

True

False

True

True

False

Maximummarks:2

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32 32DimensionalityReductionPrincipalComponentsAnalysisPrincipalcomponentisthedirectioninthedatawiththelowestvariance.Selectanalternative:

Increasesthedimensionofthetrainingdata.Selectanalternative

Sometimesremovesthenoisefromthedata.Selectanalternative

Reducesthecomplexityofthelearningproblem.Selectanalternative

False

True

True

False

True

False

False

True

Maximummarks:2

33 33UnsupervisedLearningInunsupervisedlearningThereisaspecificerrorfunctiontominimize.Selectanalternative:

Thealgorithmsusethedataitselftoguidethelearning.Selectanalternative

True

False

True

False

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Worksbyspottingsimilaritybetweenvariousdatapoints.Selectanalternative

Thedataislabelled.Selectanalternative

True

False

False

True

Maximummarks:2

34 34UnsupervisedLearningInk-meanclusteringalgorithmEachclustercenterisinitiallyselectedrandomly.Selectanalternative:

Thenumberofclustersisunknown.Selectanalternative

Theclustercentersremainfixedduringthealgorithm.Selectanalternative

Thealgorithmcontinuesuntileachdatapointisassignedtoacluster.Selectanalternative

True

False

True

False

False

True

False

True

Maximummarks:2

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35 35UnsupervisedLearningSelf-organizingmapsIsaneuralnetworkwithtopologicalmeaning.Selectanalternative:

Closeneuronsrepresentsimilardatapoints.Selectanalternative

Requiresalargenumberoflabelleddata.Selectanalternative

Eachneuronisonlyconnectedtoaninput.Selectanalternative

False

True

True

False

False

True

False

True

Maximummarks:2

36 36UnsupervisedLearningWhatarethemainessentialprocessesinSelf-OrganizingMaps?CompetitionSelectanalternative:

Addingahiddenlayer.Selectanalternative

True

False

True

False

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CooperationSelectanalternative

Weightadaptation.Selectanalternative

False

True

True

False

Maximummarks:2

37 37SwarmIntelligenceSwarmIntelligenceIsbasedonsimplelocalrulesbetweenagents.Selectanalternative:

Eachagentisindependentandisolatedfromotheragents.Selectanalternative

Theoverallcontrolstructureisdecentralized.Selectanalternative

Thereshouldbeaformoflocalinteractionbetweenagents.Selectanalternative

True

False

True

False

False

True

False

True

Maximummarks:2

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38 38EthicsAsimov'sEthicalGuidelinesforRobotsArobotmustprotectitsownexistenceeventhoughitimpliesharmingahumanbeing.Selectanalternative:

Thelawscanbeconflicting.Selectanalternative

Obeyingordersgivenbyhumanbeingsisthemostimportantlaw.Selectanalternative

ContainalawaboutallrobotshavingaserialnumberSelectanalternative

False

True

False

True

True

False

False

True

Maximummarks:2

39 39EthicsEthicalreasoningconsiderationsEthicalreasoningshouldbebuiltintoasystematdesigntime.Selectanalternative:

Ethicaldecisionsupportsystemscanreducetheriskofunwantedbehavior.Selectanalternative

False

True

True

False

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Nohumaninvolvementisneededifasystemisequippedwithanethicalreasoningengine.Selectanalternative

Designersneedtogiveattentiontopossibleethicalchallengeswhendesigningsystems.Selectanalternative

False

True

False

True

Maximummarks:2

40 40EthicsBlackboxvsglassboxsystemsAblackboxsystemisnormallypreferredcomparedtoglassboxsystem.Selectanalternative:

Thechosenmachinelearningalgorithmimpactswhatkindofboxitrepresents.Selectanalternative

Aglassboxsystemindicatesthatitisasystemthatcaneasilybreakorfail.Selectanalternative

Aglassboxsystemindicatesamoreinspectablesystem.Selectanalternative

True

False

False

True

True

False

True

False

Maximummarks:2

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41 41MachinelearningcategoriesWhichcategoryofmachinelearningiseachofthefollowingtaskssimilarto?Explainwhy,referringtotheavailabilityofdatalabels/reinforcingfeedback:41a.(2%)Trainingadogbygivinghimsnackswheneverheperformsatrick41b.(2%)MemorizingthecapitalsofallEuropeancitiesbystudyinganAtlas41c.(2%)LearningtotellthedifferencebetweenexoticfruitswhileonvacationtoSouthAmerica,butwithoutactuallylearningtheirnamesFillinyouranswerhere

Words:0

Maximummarks:6

42 42FuzzyLogicFuzzylogicsystems42a.(2%)BrieflyexplainthemaindifferencebetweenBooleanlogicandfuzzylogic.42b.(6%)ForthefollowingFuzzysystem,calculatethecrispoutput(y),whentheinputsare(x1=0.8)and(x2=0.3).Usethecenterofgravityfordefuzzification.Reachingthefinalequationof"centerofgravity"issatisfactory,andyoudon'tneedtocalculatethefinalnumericalanswer.

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Figure1.ThemembershipFunctionsforinputvariables(x1andx2)andoutputvariable(y).

Table1.Fuzzyrulesx1 x2 yClose AND Close Then LowFar AND Close Then AverageNormal AND Close Then HighFillinyouranswerhere

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43 43UnsupervisedLearningK-meanclusteringalgorithm43a.(4%)Listthemainstepsinthek-meanclusteringalgorithm.43b.(2%)Brieflysuggestawaytomakethek-meanclusteringalgorithmcapableofhandlingoutlierdatapoints.Fillinyouranswerhere

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