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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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Selectanalternative:
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
Selectanalternative:
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Selectanalternative:
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
1Selectanalternative
3Selectanalternative
True
False
True
False
False
True
False
True
Maximummarks:2
23 23ReinforcementLearningInreinforcementLearningTherewardtellsuswhichactionweshouldhavetaken.
Selectanalternative:
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Selectanalternative:
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.
Selectanalternative:
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Selectanalternative:
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
Words:0
Maximummarks:8
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43 43UnsupervisedLearningK-meanclusteringalgorithm43a.(4%)Listthemainstepsinthek-meanclusteringalgorithm.43b.(2%)Brieflysuggestawaytomakethek-meanclusteringalgorithmcapableofhandlingoutlierdatapoints.Fillinyouranswerhere
Words:0
Maximummarks:6
