Withitsdiversificationinproductsandservices,today’smarketplacemakescompetitionwildlydynamicandunpredictable forindustries.Insuchanenvironment,dailyoperationaldecision-makinghasavitalroleinproducingvalueforproducts andserviceswhileavoidingtheriskoflossandhazardtohumanhealthandsafety.However,itmakesalargeportionof operationalcostsforindustries.Themainreasonisthatdecision-makingbelongstotheoperationaltasksdominatedby humans.Thelessinvolvementofhumans,asalesscontrollableentity,inindustrialoperationcouldalsofavorablefor improvingworkplacehealthandsafety.Tothisend,artificialintelligenceisproposedasanalternativetodoinghuman decision-makingtasks.Still,someofthefunctionalcharacteristicsofthebrainthatallowhumanstomakedecisionsin unpredictableenvironmentslikethecurrentindustry,especiallyknowledgegeneralization,arechallengingforartificial intelligence.Tofindanapplicablesolution,westudytheprinciplesthatunderliethehumanbrainfunctionsindecision- making.Therelativebasefunctionsarerealizedtodevelopamodelinasimulatedunpredictableenvironmentfora decision-makingsystemthatcoulddecidewhichinformationisbeneficialtochoose.Themethodexecutedtobuildour model'sneuronalinteractionsisuniquethataimstomimicsomesimplefunctionsofthebrainindecision-making.Ithas thepotentialtodevelopforsystemsactinginthehigherabstractionlevelsandcomplexitiesinreal-worldenvironments. Thissystemandourstudywillhelptointegratemoreartificialintelligenceinindustrialoperationsandsettings.Themore successfulimplementationofartificialintelligencewillbethesteeperdecreasingoperationalcostsandrisks. KEYWORDS:Artificialintelligence,Decision-making,Brain-inspiredsystems,Unpredictableenvironments, Dynamictasks,Knowledgegeneralization 2 1.INTRODUCTION Today’smarketplaceisseeingmoreandmorediversificationinproductsandservices,asituationthathasgenerated unpredictableanddynamiccompetitionforindustries,whichforcedindustriestochallengetheemergedcomplex operationalenvironments.Oneofthemainchallengesinsuchenvironmentsismanagingtaskslikedecision-making, which,despitegrowingindustrialautomation,requiremorehumaninvolvementintheprocess.So,thesetaskscause higheroperatingcostsandalesscontrollableenvironment.Tosomeextent,theoccurredproblemhasbeenmitigated byexecutingartificialintelligence(AI)inindustrialoperations[1].However,currentAIsystemsareflawedby algorithm-complexity,unpredictablebehaviors,anddifficultyworkingatabstractlevels[2,3].Theylacktheability toadapttochangingenvironments,generalizeknowledge,anddocausalreasoning[2,4].Theycannotgeneralize acquiredknowledgeforanyfurtherchangesintheenvironmentthattranscendtheirlimitations[5]. AnAIsystemcouldfunctioninanunpredictableenvironmentifitlearnsfromexperienceandgeneralizes knowledgetochooseanalternativeandtakeaction[6].Thesefunctionsarewhatthebraindoesinthedecision- makingtasks.Thebraincouldseparatelyabstractastate'sfeaturesoritsinterrelationwiththeotherstatesofan environment.Thebraindoesnotmakecomplexdecisionsbycalculationbutthroughcooperationbetweenthe neurobiological,neuro-structural,neurochemical,andpsychologicalmechanisms[7].Itadequatelynotperfectly makesdecisionsbasedonimperfectinformationreceived[8].Itrealizesdatarapidlymostofthetime[8,9]inan episodeofsequentialactions[10]withinavastdecentralizednetworkofneurons[11,12]throughunconsciouslayers [13].Here,thesuccesskeyisknowledgegeneralizationandlearningthroughtheabstractionofsimilaritiesbetween experiencedstates’featuresandtheirinterrelations.Thebrainreceivesinformationfromitssensorysystem.Ifit discoverssimilarityinsomeaspectsbetweenthedetectedinformationandanyexperiencesrecords,itgeneralizes thatfortherealizationofthenewstate[13,14].Hence,toovercomeourproblem,webuildamodelfordecision- makinginspiredbythebrainfunctioningeneralizingknowledge,learningfromexperiences,andimproving knowledgeonimperfectinformationinunpredictableenvironments.Thesystemseeksbenefitsintheunpredictable statesofasimulatedinformationdevelopmentenvironment.However,therearesomekeyquestionsbeforebuilding thissystemthatweshouldanswer:Howdoesthebrainfindsimilaritiesbetweenexperiencedstatesandgeneralize theirvaluesfornewstates?Whatisthemethoditusestojudgevaluesandmakedecisions?Finally,whatisits fundamentalfunctiontolearnfromexperiencingnewstateandvaluesimilarexperiencedstates? 3 Tothisend,weplanourstudyintosixsections.Thefirstsectionintroducestheproblemwithabriefbackground andoutlineofthestudy.Sectiontwoinvestigatestheprinciplesunderlyingthebrainfunctionsongeneralizing knowledgefordecision-makingandlearningthroughexperiences.Thederivedprinciplesarethebasisfor developinganintelligentdecision-makingmodel,whichwerealizeinthethirdsection.Then,sectionfourdescribes anexperiment,whichwedotoevaluateoursysteminfindingsimilaritiesbetweenexperiencedstatesand generalizingknowledgefordecision-making,judgingalternatives,andlearningthroughnewexperiences.The resultsofourexperimentsarepresentedinthefifthsection,andweconcludewithouranalysisofourresultsand suggestionsforfutureworkinsectionsix. 2.BRAINFUNCTIONPRINCIPLESFORDECISION-MAKING Studiesshowthatthebrainusessimpleprinciplestointerpretinformationsignalsreceivedonitssensorysystems[15, 16].Helmholtz(1867)discussedthatthebraininterpretsthesensorydataintheconvergenceofinformationflow fromthebrain’saccumulatedknowledge[17].Forexample,thebraininterpolatesknowledgefromthevisualcortex intothedatastreamcomingthroughtheretinatodeterminethefinalvisualinformation[18];then,throughthe realizedvisualinformation,anactioncouldbeinitiated[19].Thefamiliaritybetweenthereceivedinformation (currentstate)andknowledge(thoseexperienced)isextractedforselectingaproperalternative[8,18].Thebrain regulatesthemotivationvaluesappliedtoalternativesbysettingthelossandlikelihoodofachievement[15,20].In thisway,differentneurotransmitters(chemicalmessengersinthebrain)associatingwiththemotivationand nurturingofexcitatoryandinhibitoryneuronalcircuitstolocatebeneficialinformation,supportalternativesfor action,anddevelopknowledge[21].Inaneuronalcircuit,aneuronreachesitsactionthresholdandthenactivatesthe nextneuronsinthepath.Theexcitatoryandinhibitoryconnectionstoaneurondeterminewhenitdoesactionand activatesthenextneuronsinaneuronalcircuit[22].Excitatoryandinhibitoryneuronalcircuitsarecultivated betweenneuronalclusters(groupedsame-characteristicneurons)inthebrain[23].Everyneuronherewouldlinkinto severalneurons[24](anaverageof255±13connections[25]).Inthedecision-makingprocess,eachcircuit normallysupportsonedefinitechoice[23].Thewinnercircuitinducesturningofftheotheractivecircuits[26].The rewardofthemadedecisionmodifiesandstrengthensthewinnerneuronalcircuit[27].Tothisend,neuronalcircuits recallinformationpatternsexperiencedwithrelativealternativesandachievedvalues[28]andthebrainestablishes Knowledgeovertime[5,29]. 4 Wehavefoundtwostudies,whichexplicitlydescribedthebrain'sprocessofvaluingthestatesofanenvironment, judgingchoices,choosinganalternativefordecision-making,andrevaluatevalues.Botharethemainbodypartsof ourdecision-makingmodel.Inthefirst,Groman(2019)showsthebrain’sperformancewhentakingoravoiding choicesintheinformationreceivedfromastate.Theyshowthatthebrainactivatestheneuronalcircuitswithsimilar experiencestothedetectedstateforfindingthemostandlessrewardedalternatives.Thebestandtheworstonesthen gotothelastmatchofdecision-makingtochooseoravoidachoice[30].Inthesecondstudy,Liu(2019)revealsthe modificationofalternative-valuesthroughtheachievedrewardafterthemadedecisiononinformationreceived.A setofsequentialexperimentswasbuilttostudythebrain'sfunctionduringactionselectionandalternative revaluation.Theyfoundthatthebraincontinuouslyrevaluatesandreorganizesitsalternativesthroughexperiencing newstates[14]. Thisworkreliesonthestudiesdiscussedaboveanddoesnotaimtostructurethebrain’sexactfunctions.Weseek toexamineifourmodelcouldmakedecisionsthroughrealizingcommonpatternsbetweenexperiencedstates.The modeledsystemaimstogeneralizeknowledgefordecision-makingthroughtheworstandbestvaluesachievedby commonpatternsofstatesexperienced.Then,thisstudyverifiesifthemodellearnsandupdatesknowledgethrough reevaluatingthebestandworstachievedvalues. 3.DEVELOPMENTOFABRAIN-INSPIREDMODELFORDECISION-MAKING Followingtheinvestigationofthebrain’sdecision-makingfunctions,wedevelopedasystemtomimicbrain functionsduringthedecision-makingprocess.Thesystemshouldprovideadequatedecisionsinresponseto unpredictablestates.Thestateswereinasimulatedinformationdevelopmentenvironment.Suchanenvironment experiencesunpredictableinformationflowfromtheresourceprovider’s(newlyavailable,effective,andineffective resourceinformation)andthecustomer’ssides(thedataflowaboutsatisfiedorfailedinformation)[31,32]. Specifically,thesystemmakesdecisionstoproceedwithoravoidthedevelopment("developinformation"or"skip information")ofnewinformationreceivedfromtheresourceproviderside.Table1presentsourmodelrealization stepswiththerelativeprinciplesinspiredbybrainfunction. 5 Table1.Thestepstodevelopabrain-inspiredintelligentdecision-makingsystem Brainfunctions Aspectsrealizedinthedecision-makingsystem Chainingtheevaluationandrevisionepisodesof neuronalcircuitstodevelopalternatives→1.Buildanepisodicprocessofrealizingpatterns,choosingbetween alternatives,decision-making,andalteringthealternativeby achievedvalues. Triggeringcurrentinaneuronalcircuitby detectingsensorydataandendingbyselectinga choice→2.Structureadecision-makingprocess,whichbeginsfromthe informationsensorygate,continuesintheinformationpattern realizationstage,andendswiththealternativerealizationstage beforedecision-making. Thesamecharacteristicneuronscontributetothe sameneuronalcluster→3.Placethesamecharacteristicneuronsinthesamematrixateach stageofthedecision-makingprocess. Eachmatrixrealizesacertainlevelofinformationcommonality betweenstates. Generalizinginformationpatternssimilaritiesfor anynewstateanddiscriminatingthemtofindthe mostsimilarone→4.Arrangeneuronalmatricesinthreelevels:vague,approximate,and explicit,togeneralizeknowledgefordetectedpatternsand discriminatethemostsimilaronesfordecision-making. Eachneuronisinterconnectedwith255±13of neuronsinotherneuronalclusters→5.Aneuronineachmatrixisconnectablewithallneuronsoftheother levelstorealizealargernumberofpatternsandalternatives. Motivatinganddemotivatingneuronalcircuitsto contrastthebenefitsanddisadvantagesofchoices.6.Buildneuronalcircuitsbetweenmatricesthroughtheirvalues’ similaritiesanddifferencestofindalternativesanddiscriminate them. Aneuroninaneuronalcircuitactivatedbyits upstreamneuronsandreachestheactionthreshold thenactivatesitsdownstreamneurons→7.Defineaprocessofactivationandactioninitiationforneurons duringtheinformationtransportation. Gleaninginformationfromdatareceived“the environment’sstates”→8.Defineinformationfeaturesoftheenvironment’sstatesonthe information-sensorygate. Accumulatingknowledgeovertimethroughthe similaritiesbetweeninformationpatternsand achievedrewards→9.Recordandclassifyreceivedsignalsaccordingtotheirfrequencies’ commonpatternsandcorrespondingvaluesachievedonthevalue- addingsensorygate. Interconnectingthesensoryinformationwith achievedknowledgetorealizechoices Generalizingexperiencedalternativesfornew data→10.Comparethedetectedsignalpatternswiththecommonpatternsof experiencedstatesonthepatternrealizationstagetofindsimilar onesanddetermineaproperalternative. Controllingneuronalcommunicationbythe motivationvaluesofexcitatoryandinhibitory circuitstodeterminewhichneuronscommunicate whenandwithwhom→11.Consolidatetheneuronalcircuitsatthepatternrealizationstage withhighermotivatedalternatives. Realizingalternativesbyacquiredknowledge 12.Realizealternativesthroughtheexperiencedstates. Makingthesamereferenceforrewardandloss. Attachingthebestandtheworstrewarded alternativesforsimilarstates13.Putforeachpatternmatrix’sneuronatthesamelevelthebestand worstalternatives. Comparingthebestandtheworstrewarded alternativesfromsimilarstatesfordecision- making→14.Decideon“do”or“skip”the“developmentofinformation” accordingtothebestandtheworstalternatives. Learningthroughupdatingthemotivationlevelof theinvolvedneuronalcircuits→15.Giveavaluetoeachalternativebasedontherelativeachievedvalue andmodifyitbynewexperiencesandvalue-addedachievement. Neuronsinvolvedinoursystemareclusteredinfourclassesofmatrices(informationsignalrepresentation,pattern 6 labelrealization,alternativerealization,andvalue-addrealizationmatrices)asidefromdecision-makerneurons (Figure1).Thesematricesrealizeandclassifyinformationintothreelevelsofsimilarities(vague,approximate,and explicit).Ateachlevel,everyexperiencedpatternlinksuptotwoalternativeswiththebestandworstachievements, whichmotivatethedecision-makerstodooravoidinformationreceived.Thevalue-addingsensorygatereceives valuingsignalsfromthecustomersideformadedecisions.Thesystemthenrecordsvalueswiththenew information'spatternandmodifiesoneoftheexperiencedpattern'slinkedalternativesifitgotahigherscore.The followingdiscussionsintroducethestagesofthesystem. 7 Figure1.Thediagramofourdecision-makingsystem.Theinformation-sensorygatereceivesanewinformationsignal ofaresource,derivesthecommonpatternsofinformationfeatures,andsendsthemtothepatternrealizationstage.Thepattern- realizationstagelabelsthosepatterns.Ifalabelhashadtheexperience,itactivatestheattachedalternativesatthealternative realizationstage.Thosealternativesdeterminethemotivationvaluesfortherelativedecision-makingneurons.Aftermakingthe decision,thevalue-addingsensorygategetstheresultedvalue-addingdataandrecordeditforthelabel.Ifthisnewvalueproposes tothelabelanewoptionontopofoneofthebestorworstalternatives,theoptionreplacesthatbest(orbest)alternative. 8 . 3.1.Theinformationsensorygate Theinformation-sensorygatedetectssignalswhoserepresentthepatternsofinformationfeaturesoftheresource- provider.Here,eightfrequenciesrepresenteightfeaturesofinformation.Theliteraturepresentseightfeaturesfor informationsuchassource,accuracy,validity,completeness,consistency,accessibility,uniqueness,andtimeliness [33].Eachfrequencyandtherelativeintensityrespectivelyrepresentafeatureofinformationandthefeaturevalue (Figure2).Thesystemprojectsthesignalpatternstothreelevels,thepatternswithvaguecommonalities(inthelow andhighvalues),approximatecommonalities(inthelowest,low,high,andhighestvalues).Inthelastlevel,explicit- commonpatternshavealleightintensitylevels.Therefore,theinformationsensorygateisamatrixof64neurons basedonthefrequencies'possiblevariations.Fromthereceivedinformation,theturned-oninformationsensory neuronsturnonthecounterpartneuronsatallpatternrepresentativelevelsofthepatternrealizationstage(thevague level(level-I),approximatelevel(level-II),andexplicitlevel(level-III))(theleftsideofFigure3)andtriggerthe neuronsinthepattern-realizationmatrix-I.Thetriggeredneuronsbeginacountdownprocessatacertaintime.The mostmotivatedneuroninthematrixistheonethatfinishestheprocessfirstanddoestheactionandrespectively triggersorturnsonitsconnectedneurons. Figure2.Anexampleoftheinformationpatternontheinformationsensorygate.Asignalontheinformation-sensory 9 gatemakesapatternofeightdifferentfrequenciesrepresentingtheembeddedinformationfeatures.Theintensitiesintheverticalaxisshowthe valueofeachfeature. Figure3.Theschematicofthepatternrealizationstage.Thepatternrealizationstageisintwoparts,patternrepresentative matrices(left)andpattern-labels’matrices(right). 3.2.Patternrealizationstage 10 Thepatternrealizationstagecomparesstatesrepresentedbytheinformationsensorygatewithpatternsexperienced. Itabstractssimilarpatternsatthreerealizationlevels(fromvaguetoexactcommonalities)tovaluestates(Figure3). Matrix-Irecognizesadetectedsignalthroughsimilarpatternswithvaguefeature-commonalities.Matrix-IIfinds oneswithapproximatefeature-commonalities.Matrix-IIIthendiscriminatesthesignalpatternfromthemost-similar one.Arealizedpattern(apatternlabeledbeforeforastateatleast)hasatleastalinkagetoaneuronatthealternative stageandoneatthevalue-addingsensorygate. Eachmatrixcomprises256neurons(themaximumnumberoflabelsforthecommonpatternspossibleateach level).Therealizationprocessbeginsfromthevague-commonalitiesatlevel-Iandendsupwiththemost- commonalitiesatlevel-III.Fromlevel-I,on-neuronsatthesignal-representativematrixmotivatetheconnected neuronsatthepattern-realizationmatrix-Ifortheidenticalpatternandmost-similaroneswithlinkedalternatives(the bestorworstlink).Allfeaturesofanidenticalpatternarerivaltotheircorrespondingrepresentativeones.Themost motivatedneuron(thatwiththeidenticalpatternlabel)doestheactionandtriggersneuronsinmatrix-IItodothe same.Themostmotivatedonewithalternative-linkagedoesactionandturnsonthelinkedneuronsinmatrix-Iatthe alternativestage.Thisprocesscontinuestilllevel-IIIthelastmatrix).Atmatrix-III,themostmotivatedneuron triggersthedecision-makingneuronsatlevel-IIItodotheaction.Bydeterminingallthreelevels’interrelations,the pattern-realizationstagecouldrealizeupto16,777,216differentpatterns.Level-Icouldfindamaximumof65536 similarpatternsforeachdetectedsignal.Level-IIcouldcatchupto256similarpatterns.Then,level-IIIcould discriminatemaximumbetweenthe256mostsimilarpatterns.Addinganotherlevelaugmentsthisabilityuptothe realizationof4,294,967,296differentpatterns. 3.3.Alternativerealizationstage Eachlabeledneuroninapattern-realizationmatrixcouldlinkatthesameleveltotwosame-levelalternatives(the bestandtheworst)atthealternativerealizationstage.Eachalternativerealizationmatrixisdividedintotwoareas Figure4.Theareaabovewithgraycolordisplaysthebestpossiblealternativesandtheonebelowrepresentsthe worstpossiblealternatives.Thebestandtheworstalternativesmotivatethedecision-makingneuronssatatthesame levelfor“develop”and“skip”decisions.Themostmotivatedonebetweenthetwoisthefirstonethatdoesthe actionandtriggersthesame-objectiveneuronatthenextlevel(theleftsideofFigure4).Atthefinallevel,thefirst onewhodoestheactionnailsthefinaldecision.Then,thesystembringsoutitsoutputresult. 11 Figure4.Theschematicofthealternativerealizationstage.Theleftsideshowsthedecision-makingneuronsmotivatedbythe same-levelbestandworstalternativesinthemiddle(thegrayarearepresentsthebestalternatives).Thosealternativesarelinkedtothesamelevel realizedpattern-labels.Theycouldbereplacedbyreceivedvalueachievements.Therightsidedepictsthematricesthatrecordreceivingsignalson thevalue-addingsensorygatefortherealizedpattern-labels. 3.4.Value-addingsensorygate Foreachmade“Develop”decision,thesystemreceivesvaluedatabythevalue-addingsensorygate.Thatgate transfersthevaluetothethreecorrespondingvalue-addedmatrices(rightsideofFigure4).Theneuronwith receiveddataconnectstotheon-neuroninthepatternrealizationmatrix.Theon-neurontemporarilyassignsan alternativetothenewvalue.Ifthisalternativepinsanewrecordmoresignificantthantheoldone(thebestorthe worstalternative),itreplacesthatalternative. 12 4.EXPERIMENTTOTESTTHESYSTEM’SPERFORMANCE Westructuredanexperimentalsetuptotestifthesystemlearnsthroughexperiencesandgeneralizesknowledgefor decision-making.Thefirststeptoevaluatethesystemonknowledgegeneralizationanddecision-makingmethodwas toverifyifthesystemcoulddodecision-makingbyitself(non-defaultdecisions)throughitsrealizationprocess. Second,weaimedtojustifyifthesystemachievesknowledgetomakedecisionswithmorethan50%correctresults throughexperiences.Moreover,wemeasuredtheeffectofeachrealizationlevelonmakingsuccessfuldecisions.To verifythesystemonlearningandupdatingknowledge,weevaluatedifthesystemlearnsinformationfeatures throughexperiencingdifferentstates. Theinformation-sensorygatewasreceivingsignalswithrandomfeaturesbetween1and8.Thosesignalsdidnot representanyrealresourceprovider'sinformation.Theywereonlygeneratedtoevaluatethesystem.Likewise,the madedecisionsachievedvalueswerenotfromtherealcustomerside.Weestimatedeachvalueaccordingtothe ranksoffeaturesandtheintensitiesoffrequenciesbeforeapplyingit.WehadrankedeachfeatureaslistedinTable3. Weshouldsaythattheranksofinformationfeaturesarecombinatoryinrealenvironments.However,weignored thattoallowasimpleconfigurationofinputdata.Here,asignalvaluewasthesumoftheranksmultipliedinthe intensitiesofthefrequencies. Thevaluesachievedforeachpatterndeterminedtherelativealternativesmotivationfor"Develop"and“Skip” decisions.Iftherewerenonotablealternativesforadetectedpattern,thesystemmadeadefaultdecision,whichwas deliveringa"Develop"decision.Thedecisionsmadewereevaluatedbycomparingthemtotheexpectedresults (estimatedinformationvaluesforthenon-default"develop"decisions).Here,anon-defaultdecision(certain-decision) wasmadebasedonacquiredknowledge.Theexperimentwasdesignedtoproveordisproveifourbuiltsystem performsastheprinciplesderivedfrombrainfunction.Ineachexperiment'sstate,arandomsignalwasappliedtothe information-sensorygate.Thesystemthenrealizedtheinformationpatterntoaddressthebestandtheworst alternativesineachrealizationlevel.Then,thosealternativesmotivatedthedecision-makersfromlevelthreetolevel one.Thelastonedecidedtodeveloporskiptheinformationsignal.Finally,anestimatedvalueforthenon-default “develop”decision,ifmade,wasappliedtothevalue-addingsensorygatetoevaluatethedecisionandmodifythe alternatives. 13 Table3.Definedinformationfeatures’ranksforourexperiments Frequencies ranks Accuracy(f1) 5 Completeness(f2) 1 Consistency(f3) 3 Uniqueness(f4) 4 Validity(f5) 6 Accessibility(f6) 4 Timeliness(f7) 1 Source(f8) 2 Toanalyzetheexperiment’sresults,wefirstmeasuredthenumberofnon-defaultdecisionsmade.Wethen comparedthevaluesofthenon-default“develop"decisionsmadeatallrealizationlevelswiththeestimatedvaluesto definehowmanyofmadedecisionsweresuccessful.Tocompletetheverificationofknowledgegeneralizationand decision-making,welookedforifthesystemcouldmakesuccessfuldecisionswitharateofmorethan50%. Moreover,weevaluatedtheeffectofeachrealizationlevelonthesuccessofdecisions.Then,weexaminedthe distributionofthesuccessfuldecisionsbasedontheinformationfeaturestocomparewiththeranksconsideredfor theinformationfeaturestoverifythesystemlearningprocess. 5.RESULTS Fromthefirststateoftheexperimenttostatenumber1000,thetendencyofthesystemtomake“certaindecisions” (non-defaultdecisions)increasedcontinuouslyto94%.Thesystemkeptthatpercentageofcertaintyroughlythe sameuntiltheexperiment'slaststate(Figure.6,theredcurve).Nonetheless,atthatpoint,only4of10non-default “develop”decisionsweresuccessfulatthestate1000(Figure.6,thepurplecurve).Wecontinuedtheexperimentto determinethatifthenumberofsuccessfuldecisionsincreasesornot,ifyes,whereisthepointthatthesystempasses 50%success.Afterstate1000,thepercentageofsuccesswasgrowingwithasmoothslopetothelaststate.Around state37000,thesystempassedthethresholdof50%ofsuccessfuldecisions.Atthispoint,thesystemhad experienced0.2%ofthetotalpossiblestates.Thefailed"Develop"decisionsdecreasedfrom60%afterstatenumber 1000to40%attheendoftheexperiment(Figure.6,thepurplecurve). 14 Figure.6.Thesystemtrendinnon-defaultdecision-makingandsuccessfulmade-decisions.Fortheredcurve:The horizontalaxisrepresentsthenumberofstates,theverticalaxisshowsnon-defaultdecisionspercentages("0"and"1"arerespectivelyfor“default decisions”and“certaindecisions”).Fortheothercurves:Theverticalaxisdepictssuccessful-decisionsratios.Thetendencyofthetotalsuccessful decisions(purpleline)iscomparedwithlevelsI,II,andIII(theblue,brown,andgraylines,respectively).Level-I(vaguesimilaritylevel)showsa quickadaptationtotheenvironment.Theimpactingpercentagesofthelevel-IIandlevel-IIIonthesuccessfuldecisionscontinuouslyincreasedat aconstantrate. Thethreerealizationlevelsprocessedthedetectedinformationsignalsstepbystep,fromroughlysimilartomost familiarones,tohaveenoughalternativesforthefulfillmentofanynewstates.Levelthreewasfordiscriminating betweenpatternstorecognizethemostsimilarpatternsbetweenmoresimilarones,whichactuallythislevelwasnot testedinthisexperiment.Weevaluatedtheeffectofthefirstandsecondlevelsonthesuccessofeachstate’sfinal non-default“develop”decision.Inthisregard,Figure.6showsthat46%ofsuccessfuldecisionsinthethresholdof 50%successwererelativetothelevel-Ifunctioning.And,4%ofsuccessfuldecisionswererelativetolevel-II.The effectsoflevel-IIandlevel-IIIhadthesamevaluebecauseofthelowrateofpatternrepetitionsatleveltwo.We continuedtheexperimenttotestthetrendconsistencyofthesuccessfuldecision.Theresultsshowthatthesystem continuedthesametrendformakingsuccessfuldecisionsuntiltheend.Figure.6comparesthetendencyofthetotal successfuldecisionswithlevelsI,II,andIII.Level-Ishowsaquickadaptationtotheenvironment.Beforestate 69000,thesystemmade60%successfuldecisions,49%relativetolevel-I.Elevenpercentofsuccessfuldecisions wererelativetotheotherlevels.Here,thesecondlevelfoundonaveragetwosimilarpatternsforeachnewstate. 15 Level-Iwasresponsiblefor100%ofthesuccessfuldecisionsmade,atstatenumbers1000,decliningtoabout82%at statenumber69,000,theotherlevelsuptoatotalmaximumofabout18%. Figure.7-ashowsthatthelevel-Ireachesanaverageof4repetitionsoneachpatternbeforestate1000,which,by theway,wasresponsibleforall40%ofthemade-decisionsuccess.Increasingtheaveragerepetitionthenslowly affectedthesuccessofdecisions.Accordingly,inthepointof50%successfuldecisions,level-Iwithanaverageof 147repetitionsoneachpatternleadtoonly46%success.Attheendoftheexperiment,level-Iwith271repetitions ofeachpatternaccompanied49%ofthesuccessfuldecisions.Figure.7-bshowsthatthenumberofrepetitionsof eachpatternafterstate100hadadirectrelationtothepercentageofmade-decisionsuccess. Figure.7.Theeffectsofalllevelsonsuccessful“develop”decisionsforexperimentson70,000states.a-Thetrend ofpatternrepetitions,blueline,iscomparedwiththetrendofsuccessfuldecisions,redline,forlevel-I.b-Thetrendofpatternrepetitions,green line,iscomparedwiththetrendofsuccessfuldecisions,redline,forlevel-II. 16 Toevaluatethesystem’slearningfromitsexperiences,wemeasuredtheaccumulationofsuccessful“Develop” decisionsoneachfrequencythroughoutallstates.Weverifiedthesystemonrecognizingthedifferencebetweenthe valuesofinformationfeatures.Figure8comparesthosevalueswiththeranksappliedtoinformationfeatures.Value oneinthegraphleftofFigure8representsthefrequencywiththemaximumaccumulationofsuccessful“Develop” decisions.Theotherfrequenciesinthisgrapharecomparedwiththisfrequency.Thehighestaccumulationwasforf5, whichshowsacorrelationwiththehighestrankofinformationfeatures.Thelowestaccumulationvaluewasforf2 andf7,likewise,correlatedwiththeminimuminformationrank. Figure8.Leftside,theaccumulationofsuccessful“Develop”decisionsonthefrequenciesin70,000states, comparedtothefeatures'givenranksontherightside.Theratiosoftheaccumulatedsuccessfuldecisionsareonthevertical axisintheleft-sidegraph,andtheranksofinformationfeaturesareontheverticalaxis(between1,2..,and8).Intheright-sidegraph.These graphsshowthesystem'sabilitytodifferentiatebetweenbeneficialandnon-beneficialfeatures. 6.DISCUSSION Wethendevelopedadecision-makingsystembasedonbrainfunctionsinfindingsimilarsimilarpatternsbetween experiencedstates,generalizingknowledge,actionselection,andlearningfromexperiencingnewstates.The evaluationofourmodelwasfiguredthroughprogressingthestatesoftheexperiment.Ineachstate,thesystem receivedinformationwitharandompattern.Then,itdecidediftodeveloporskipthat.Foreach"Develop"decision, wecalculatedavalueandappliedittothevalue-addingsensorygate.Thesystemperformancewasmeasuredusing theresultsfromtherealizationofeachstate,determiningrelativealternatives,decision-making,andvalue-added 17 achievements.Theresultsallowedustoreachthefollowingconclusions Forthegeneralizationofknowledgeanddecision-making: Weevaluatedtheeffectoflevel-Iandlevel-IIondecision-making.However,wedidn'texaminethesystem'sability todiscriminatemostsimilarstatesinlevel-III,whichcouldbeimportantforcomplexenvironments.Itneedstobe investigatedinthefuture.InFigure.6,thebluelineshowsthatlevel-Ibyabstractinghighernumbersofstatesin commonpatternscouldhelpthesystemtoadaptfastertounpredictableenvironments.Level-Iprovidedthrough previousexperiencesfurtheralternativespossibleforrespondingtonewstates.Level-Iprovidedonaverage147 valuescomparingtolevel-IIwithanaverageof1.5valuesforeachcommonpatternatthebrinkof50%success. Level-IIandlevel-IIImademorequalifieddecisionswithhigherachievementrates,however,scarcealternativesfor decision-making.Bothlevelsshowedaslowinclination,whichsuggestsalargenumberofstatesrequiredtobe experiencedtomakehigh-valuedecisions. Fortheabilityoflearningandmodifyingalternatives: Thesystemshowedthatbeabletoimprovethedecision-makingprocess.Itimprovedtheprocessofrealizing alternativesthroughtheabstractionofdifferentpatternsoftheinformationfeaturesofexperiencedstates.Acommon pattern'svaluewasgeneralizedforallrelevantnewstates.Weevaluatedtheabilityoflevel-Iandlevel-IIfor generalizingknowledgeondecision-making.However,wedidn'texaminetheabilitytodiscriminatemostsimilar states.Level-IIIwasforrealizingthischaracteristic,whichcouldbeimportantforcomplexenvironmentsandneeds tobeinvestigatedinthefuture.Thedecision-makingbehavioroflevel-IisrepresentedinFigure.6,theblueline.It showsthatlevel-Ibyabstractingmoresimilarfeaturesfromexperiencescouldhelpthesystemtoadaptfasterto unpredictableenvironments.Atthebrinkof50%success,level-Iwasresponsiblefor92%ofsuccessfuldecisions. Thislevelprovidedthroughpreviousexperiencesfurtheralternativespossibleforrespondingtonewstates.Level-I providedforeachcommonpatternonaverage147valuescomparingtolevel-IIwithanaverageof1.5values.The othertwolevelsincreasedtheprecisionofdecision-makingandtherateofhigherachievements,however,fora shorterscopeofsimilarities.AccordingtoFigure.7,thesuccessfuldecisioninclinationformorethan50%success correlatedwiththelevel-IIandlevel-IIIparticipationindecision-making.Theeffectsofbothlevelswerethesame becauseofthelowpossiblerepetitionsforlevel-IIduringourexperiment.Nevertheless,bothlevelsshowedaslow inclination,whichsuggestsalargenumberofexperiencesrequiredformakingmoreprecisedecisions. 18 Weverifiedtheabilityofthesystemtolearninformationfeaturesanddifferentiatethembyevaluatingthe accumulationofsuccessfuldecisionsondetectedsignalfrequencies(Figure8).Thesystemdevelopedthelearning processthroughthemodificationofthealternativemotivation-scores.Itcouldrecognizethefeaturevaluesin detectedfrequenciesthroughlearningfromexperiencesandgeneralizeknowledgefornewunpredictablestates. Inthisstudy,thepatternandalternativesrealizationstagesmimickedthebrainprinciplesindecision-making.The similaritiesbetweenvaluedstateswerederivedfromthebuiltneuronalcircuitsbetweentherealizationmatrices.The systemgeneralizedknowledgefordecision-makingthroughexperiences.Thealternativerealizationstagemodified alternativesbasedonthevalue-addedachievementsofmadedecisions.Thisstudycouldbedevelopedfor implementationinreal-worldenvironmentswithrealunpredictableinformationsignalsandrealvalue-added achievementsconsideringunknowninterrelationsbetweeninformationaspects.Ournextstudywillbedevelopinga decision-makingsystemondynamictasksconsideringhazardousmaterialsinanunpredictableenvironment.We shouldnotethatthesystemcouldbeusedasabasisfordevelopingartificialgeneralintelligence(AGI)to configuringcomplextasksderivedfromacombinationofdifferentdataaswhatisrealizedbythebrain. ACKNOWLEDGMENT TheauthorswouldliketoacknowledgethefinancialsupportprovidedbyNSERCforthisresearchundergrant RGPIN2015-06253.