Abstract. Animportantcharacteristicofthesoftwaredevelopmentprocessisthedegreeofreusabilityofsoftware.Simplyspeaking,knowledgeonceencoded

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1 Largeheterogeneousknowledgebases RoyalInstituteofTechnology,Sweden EnnTyugu 1Softwarereusability tionlanguageoflargeknowledgebasesareconsidered.higherorder Userrequirements,architectureandinternalknowledgerepresenta- computability. menttoolswhichsupportthecreativityofprogramminginthelarge. constraintnetworksareproposedforrepresentingknowledgeabout Thispaperdiscusseslargeknowledgebasesassoftwaredevelop- Abstract Animportantcharacteristicofthesoftwaredevelopmentprocessisthedegreeofreusabilityofsoftware.Simplyspeaking,knowledgeonceencoded withtheexceptionofasmallnumberofspecicapplications,thesoftware ofsoftwaredevelopmentandreliabilityofthesoftwareproduced.however, intheformofprogramsmustbereusableeverytimeitwouldbeneededin programmingnewproblems.anaturalwaytoreuseprogramsistoapply largesoftwarelibraries.itisexpectedthatthisincreasestheproductivity inselectingsuitablesoftwarefromsoftwarelibraries(devanbu1991). arebeingmadetobuildknowledgebaseswhichcouldprovideaguidance booksdonthavetitlepages,andtheyarestoredinarandomorderand point,softwarelibraryislikeanordinarylibraryofliteraturecontaininga largenumberofbooks,exceptthatithasnocomprehensivecatalogue,the documentation.onecanusethefollowinganalogy.fromausabilitystand- librariesoftodaytendtobeverydiculttouse.theylackcomprehensive userinterface,andrequirefromtheuserstoomucheortsofstudyingof areaccessibleonlybynumberswhicharetheirformaladdresses.attempts lems.thisknowledgeisvisibletousers(softwaredevelopers)anditis thelayerofknowledgeabouttheirapplicabilityforsolvingdierentprob- directlyvisibleinthesoftwaredevelopmentprocess.theyarecoveredby librarywhichcontainstwolayersofknowledge,figure1.1. theirdeclarativespecications.roughlyspeaking,weshallbuildasoftware basewhichwouldsupportautomaticconstructionoflargeprogramsfrom Thegoalofthepresentworkistoproposeadesignforaknowledge programs.besidesthat,wedistinguishbetweentheinternalknowledge representedintermsofconceptsofaproblemdomains,notintermsof Thelowerlayerisarepositoryofprograms.Theseprogramsarenot 1

2 2 Largeheterogeneousknowledgebases representationlanguage,whichisessentiallyalanguageforrepresenting Programs:Fig.1.1.Layersofprogrammingknowledge User interface knowledgerepresentationlanguageforspecifyingconceptsandforreasoningaboutthem. inseveralknowledge-basedprogrammingenvironments(tyugu1991).the noveltyofthepresentworkistheapproachtoaknowledgebaseastoa knowledgeaboutcomputability,andauserlanguagewhichisahighlevel andtolerances,arenotapplicablefordevelopingalargegeneralpurpose ofcomputersforcircuitanalysis,designofmachinepartsorselectingts generalpurporsesoftwaretool,notanarrowlyproblemorientedtool.this approachchangestherequirementstothedesignofaknowledgebaseasit inrestrictedproblemdomainslike,forinstance,engineeringapplications willbeshowninsection3.theknowledgetoolswhichworkedsatisfactorily Thiskindofkowledgerepresentationhasbeenalreadysuccessfullyused afterwepresentrequirementstolargeknowledgebasesandpresentsome knowledgebase. architecturalsolutionsoflargeknowledgebases.weshallnotdiscusshere theuserlanguagebuiltontopoftheinternalknowledgerepresentationlanguage,asithasbeenrepresentedinfulldetailsinseveralpapers(tyugu computabilitywhichisthekeyissueofsoftwareknowledgebases.there- Westartwiththeexplanationoftherepresentationofknowledgeabout 1991). language.thisisalanguageusedforrepresentingsemanticsofconcepts 2Representationofknowledgeaboutcomputability Inthissectionwedescribebrieytheinternalknowledgerepresentation ofthe\realworld"intermsofprogramsavailableintheknowledgebase. Thislanguageitselfisdividedintotwoparts:alanguageofconstraintsfor Concepts:

3 EnnTyugu 3 Fig.1.2.Functionalconstraints b describingcomputationsandalanguageofrulesformetareasoningabout concepts. guageofhigherorderfunctionalconstraints.anyconstraintsinitcanbe representedbyaprogram.however,someconstraintsappearfromdeclarationswhicharenotprograms,butstillhavefunctionalsemantics.an whichcanbeinterpretedasasourceofthefollowingthreeprograms: Thelanguageofconstraintswhichweusecanbedescribedasalan- exampleofsuchkindofaconstraintisequation,forinstance, dependingonwhatmustbecomputed:u,iorr. u:=ir i:=u=r u=ir programsaandbwhichrepresenttheconstraintsshowninfigure1.2. Toexplainhigherorderfunctionalconstraints,weshallconsidertwo r:=u=i (1.1) (1.2) assumethatthisparametercantakevalueswhichhaveinputuandoutput aproceduralparametergwhichisalsoaninputofthisprogram.letus Thesecondprogramhasaninputvariables,anoutputvariablet,and Therstprogramhasinputvariablesx,yandanoutputvariablez. (1.3) y,zands,t,u,vrespectively.arrowsshowpossibledirectionsofdataow duringthecomputations. andoutputdata(i.e.thevaluesofthevariablessandt)andthedata v.figure1.2showstheseprogramsasconstraintsbetweenthevariablesx, Observingthedataowofthesecondprogram,wedistinguishitsinput x y a a) z s u b) v g t

4 parameterg.theprogrambproducesinputoff(thevalueofthevariableu)andgetsbackthevalueofthevariablevwhichisoutputoff. Thishappenseverytimewhenfiscalled,andthat,initsturndepends passedbetweenthisprogramandthepogramfwhichisthevalueofits 4onthecomputationsperformedbytheprogramb.Wemustdistinguish thisdataow,whichoccursduringthesubcomputationsperformedbythe Largeheterogeneousknowledgebases programb,fromthe\ordinary"inputandoutputofprograms.thisisre- ectedinmarkingsofarcsbindingthevariablesu,vwhichdierfromthe proceduralparameter,aswellasseveralinputandoutputvariables. thesamesubcomputation.ingeneral,aprogramcanhavemorethanone constraintsasitsnodes.theconstraintsarefunctionalorhigherorder arcsbindingothervariableswithprograms.theroleofthehigherorder variablegisbindingthevariablesuandvasinputandoutputofoneand dependenciesrepresentedbyprograms(orevenbyequationsetc.).its arcsbindvariableswithconstraintsasithasbeenshowninfigure1.2. Itisremarkable,thatthisgraphisabipartitionedgraphwiththenodes dividedintononintersectingsetsofvariablesandconstraints(procedural beusedasaprogramspecication,providedagoalisgivenintheformof parametersarenotrepresentedasnodesinthisgraph).anexmpleofa higherorderconstraintnetworkisshowninfigure1.3.suchanetworkcan Ahigherorderconstraintnetworkisagraphwhichhasvariablesand listsofinputandoutputvariablesofthedesiredprogram.forinstance, Figure1.3becomesaprogramspecicationassoonasrealisationsofits hasbeenappliedinseveralprogrammingenvironments(tyugu1991;mints possibleschemasareshowninfigure1.4a,bwherewecanseethatthe theprogramdesired. constraintsaregivenandwesaythatxistheinputandyistheoutputof representingknowledgeaboutthecomputability.butitisstilltoore- 1988). dandeinanothercase.programsynthesisfromspecicationsofthiskind programimplementingtheconstraintchasasubcomputationwhichis applicationoftheconstraintsa,b,dandeinonecaseandtheconstraints Theknowledgerepresentationlanguagedescribedaboveissuitablefor Itisquiteeasytobuildaschemaoftheprogramaskedabove.Two strictedtorepresentgeneralknowledgerequiredinuniversalsoftwareknowl- conceptc.thepredicatesassociatedwithconceptsgiveusapossibility canbeuniedwithcorrespondingobjectsofanyparticularinstanceofthe aconstraintnetwork.letaconceptchaveaspecicationwhichcontains relationbetweenthesevariables,andshallintroduceapredicatecforexpressingthisrelation.variablesx;...;yintheatomicformulac(x;...;yguageaboutconstraintnetworksedgebases.weproposeanextensionofthislanguagewhichisametalan- variablesx;...;y.weshallconsiderthisspecicationasadescriptionofa Letushaveaknowledgebaseofconceptseachofwhichisspeciedas

5 EnnTyugu 5 x c a y b Fig.1.3.Higher-orderfunctionalconstraintnetwork g s d u v w n e

6 6 Largeheterogeneousknowledgebases a c Fig.1.4.Synthesizedprogramschemas b a d b e c a) b) d e

7 aboutconceptswhichcanbeecientlyimplemented.thishasbeendone restrictustohornclauses,wegetaknowledgesystemformetareasoning inthenutprogrammingenvironmentdescribedin(tyugu1991).actually,themetareasoningcomponentofthenutsystemdoesmorethan EnnTyugu 7 thesituationsdescribedintermsoftheseconcepts.inparticular,ifwe torepresenttheoriesabouttheconceptsintheknowledgebaseaswellas thescopeofpossibleapplicationscannotbemadepreciseinadvance.this describedabove.itworksalsowithpartsofconstraintnetworks,notonly withseparateconcepts. 3Requirements Alargeknowlegdebaseisintendedforapplicationsintheconditionswhere featuresofsuchaknowledgebase.mostoftheexistingknowledgebases meansthatconformity,extendibility,reusabilityetc.mustbeinherent edgebasesinknowledgeareaslikebasicgeometryandphysics(tyugu1988) powerfulenoughtoenableonetobuildandtoapplygeneralpurposeknowl- theapplicationdomain. inaparticulareldofcad,managementdecisionmaking,medicineetc. thisfacilitatesthedesignofaknowledgebaseconsiderably,enablinga designertomakereasonablecommitmentsonthebasisofinformationabout havebeendesignedforapplicationsinsomewell-denedproblemdomain: engineeringoftypicaldevices(lters,transformersetc.)inelectricalengineering.ourgoalwillbetomaketheseknowledgebasesapplicablein variouscombinationsand,ultimately,tounitethemintoasingleknowledge bodyasisoperationalinhumanbeings.actually,theanalogywithhuman Fromtheotherside knowledge-basedtechniquesoftodayarealready aswellasingeneralengineeringeldslikemachinepartsinmechanical reasoningand,inparticular,withteachingpeopleismuchdeeperhere: toabsorbandapplyanykindofknowledgesystematicallypresentedto dentsareconsideredtobe\universal"inthesensethattheymustbeable them.theknowledge-basedtoolsoftodayarestillveryrestrictedandwe inputofknowledgebases. principallynotdierentfromputtingtogetheracurriculumofstudiesfor students.thedierencesappearatthestageofimplementation.thestu- donotexpecttobeabletopresentknowledgeinanaturallanguageatthe conceptualdesignofaheterogeneousmultidisciplinaryknowledgebaseis ofnewknowledge.thiswillgivethecumulativeeectwhentheknowledge knowledgebase. investigated.thisrequirementmeansthatalltheknowledgeavailableina theprojectcyc(lenat1990)wherethisrequirementhasbeenthoroughly knowledgebasemustbe,rstofall,applicablefordeclarativespecications Thefollowingisasummaryofrequirementstoalargeheterogeneous Supportofknowledgeacquisitionisarequirementcloselysharedwith

8 isacquired.inthecaseofnaturallanguageinput,thisrequirementnecessi- itrequiresawell-developedinheritancemechanism. Analogywithlargedatabases,andinparticular,understandingthereasons aknowledgebaseassoonasthelattergrowsoutofeasycomprehension. Transparencyofknowledgefortheuserbecomesacrucialfeatureof Largeheterogeneousknowledgebases 8tatesassociativereasoning.Forformalknowledgerepresentationlanguages, isavailablefortheusageandhowtouseit.invisibilityleadstoerrors ofintroductionofdatadictionariesinthethesaurusformisusefulhere.in thecaseofthelargeamountofknowledge,ausermustdiscoverwhat causedbymisunderstandingofknowledgeandtothenecessitytosolvethe \discoverytask"whichcantakeupto60%ofthedevelopmenttimeof proceduralknowledgebases(devanbu1991). ofall,concentratingattentiononasmallnumberofcomparativelysmall crotheories"inseparateknowledgemodules(tyugu1991b). enceengine.modularityenablesonetorepresentvariouskindsofknowl- edgeinasingleintegratedknowledgebasebyencapsulatingdierent\mi- knowledgeentitiesateachinferencestepimprovestheeciencyofaninfer- Modularityofknowledgeisrequiredforthefollowingreasons.First formofprecisemathematicalmodels.arepresentationlanguageforthis kindofknowledgemustbecloseenoughtousualmathematicalnotations. representedintheformofproductions,thisisnottrueforhardanddeep knowledge.inengineeringelds,forinstance,thelattercanexistinthe Thetwoknowledgerepresentationformalismspresentedintheprevious shallowordeepknowledge.whenshallowandsoftknowledgecanbewell onecandistinguishhardandsoftknowledge,orinanotherdimension lectasuitableformforrepresentingeachpieceofknowledge.forinstance, Heterogeneityofknowledgeisthefeaturethatrequirestheabilitytoseningofthispart.Becausealargeknowledgebaseisnotdesignedforany sectionareverymuchintendedtosupporttheheterogeneityofknowledge. predenedapplicationdomain,itmustbeadaptableandextendableduring evenwhenhavingbeenwrittenindierentlanguages(wileden1991). operability,i.e.theabilityofprogramstocommunicateandworktogether morerestrictedcontextofproceduralknowledge,conformitymeansinter- tasksandinvariousenvironments.oneexpectsthatalargeknowledge basewillbeapplicablewhenevertheknowledgeinitbecomesuseful.ina Openness extendabilityisthefeaturementionedalreadyatthebegin- Conformitymeansapossibilitytouseaknowledgebasefordierent ityisastrongrequirementinknowledgehandlingandthereareanumber oflogicalmodelsofit,see(genesereth1987). essentiallydierentfromopennessandextendability,becauseitimpliesthe itswholelife-cycle. nonmonotoniccharacterofknowledgeinaknowledgebase.nonmonotonic- ofapplicationsofaknowledgebaseatitsdevelopmenttime.thisfeatureis Changeabilityisanotherfeaturewhichfollowsfromtheundenedscope

9 alogicalframeworkitmeanspreservingconsistencyafterintroducingany permissiblechangeintoknowledge.practically,itcanbeonlypartially guaranteedbyintroducingintegrityconstraintswhichmustbecheckedat everychangeoftheknowledgebase. Integrityisafeaturewhichisknownfromdatabasemaintenance.In Sizeofalargeheterogeneousknowledgebasecanbeestimatedonly EnnTyugu 9 tionwithpracticalusageofknowledge-basedtechniques.initsessence,this containabouttheorderof10mbindings,asweshallseeinsection5. bolicnames)inaknowledgebase.alargeknowledgebasecan,possibly, sureofknowledgeisthenumberofsimplebindings(i.e.instancesofsym- rougy,becausethereisnoprecisemeasureofknowledge.apossiblemea- theprogramcouldbeappliedtotheproblem.whenaknowledge-based applyingaprogramtosolveaproblem,oneisalwaysexpectedtoknow preconditionoftheprogram,i.e.thepredicatewhichdetermineswhether meansgettingunsatisfactoryresponsestounexpectedsituations.when techniqueisbeingused,thepreconditionsarenotsoexplicit.ifknowledge isrepresentedintheformofrules,onestillcanlookattheconditionsof therulesaspreconditions.but,rstly,theseconditionsdonotapplytothe Brittlenessbottleneckistypicallytheproblemwhicharisesinconnec- problemasawhole.secondly,theymaybeexpressedinimpreciseterms themeaningofwhichremainshiddenfromauser.asaconsequence,the userwillnotbesatisedwiththeresponseshewillgetfromaknowledgebasedsystem.whentheamountofknowledgeincreasesandwhenthe knowledgedomainbecomeswider,thentheknowledgebaseappearstobe unreliabletousers itwillnotsatisfytheirexpectations,becausetheywill beunabletogettheinferencesexpectedtobedoneautomaticallybythe knowledgebase.thisisabriefreviewofrequirementswhichmustbetaken aknowledgerepresentationformalismfortheinternaluseinaknowledge basemust,rstofall,satisfytherequirementsofeciencyandneednotbe notbesatisedinaatandhomogeneousknowledgebase.besidesthat, bases. 4Architecture Requirementsofmodularityanddiversityofknowledgerepresentationcan- intoaccountindesigningthearchitectureoflargeprogrammingknowledge fromtheinternalknowledgerepresentationformalisms. principleforbuildingalargeknowledgebase: suitableforthehumanunderstanding.thisgivesustherstarchitectural Onewaytoachievethisistouseacalculusofinheritancefortranslating highlypreferablethatitstranslationwouldbestraightforwardandsimple. 1.Theinputlanguageforknowledgerepresentationshouldbedierent Aninputlanguagecansupportaframe-basedrepresentationanditis

10 fromaninputlanguageintoaformalinternallanguagewhichmustbe suitableformakinginferences(tyugu1991b). 10 sentation.experienceshowsusthatnouniversallyecientknowledge representationandhandlingtechniqueexists.onthecontrary anumberofverydierentmethodshavebeendevelopedforsolvingproblemsin Thesecondprincipleisconcernedwiththeinternalknowledgerepre- Largeheterogeneousknowledgebases variousknowledgedomains.alsoconsideringhumanintelligence,onecan thatadequatelyrepresentsknowledgeprocessinginthissystem. distinguishbasicallydierentknowledgehandlingmechanismsthatareassociatedwithlogicalandintuitivewaysofthinking.thisgivesusthe requirement,whichwewillnotdo.however,lookingatthenumerous inferenceenginecanberepresentedasaformalcalculuswithinterpretation followingprinciple: examlpesofknowledgesystems,wecanndgoodevidenceinfavourofthis Thisthesiscannotbeprovedformallywithoutformalisingtheadequacy 3.Anyknowledgesystem(KS)whichisknowledgerepresentationplus Elaboratingonthemeaningofaknowledgesystemgivesusthethird 2.Severalknowledgesystemsareneededinalargeknowledgebase. way.thepossibilityofformalisationofaksbecomesobviousassoonas representingks.thisprincipleliesinthegroundofinferenceenginesof weloosentherequirementofadequacy:onasucientlylowlevel,wecan thesis.firstofall,makinginferencesmeansusingknowledgeinadeductive knowledgesystems. knowledgedomain. usethewell-knownformalsystems TuringmachinesorPost'ssystemsfor Itcontainsallbasicknowledge-pathsforknowledgeacquisitionandapplicationtoexternalproblems.Itdoesnotshowtheknowledgeowfor self-referentialreasoningandforexplanations.attheknowledgeacquisitionstage,conceptsarebeingformedbyatranslator(1).aconcept Eveninoneandthesameknowledgedomainonemustbeabletouse verygeneralknowledgeaswellasspecicknowledge.therstkindof knowledge,seesectionrefrepr. knowledgecanbepresentedintheformofametatheoryaboutthedomain 4.Severallevelsofgeneralityarerequiredeveninoneandthesame Thefollowingprincipleconsidersthestraticationofknowledge: Theknowledgerepository(5)canbestructuredintoknowledgepackages hasalayeredstructureandthisstructureispreservedalsolater,whena Dierentlayersneeddierentinferenceenginesformakinginferences(3,4). representationofaparticularsituation(aproblem)isbeingconstructed. InFigure1.5,theoperationalpartofalargeknowledgebaseisshown. (6,7)andcanhaveself-referential(meta)knowledgeaboutitsowncontents. isintendedforprocessingusers'queriesabouttheknowledge.itprovides Figure1.6showsthereferentialpartofaknowledgebase.Thispart

11 EnnTyugu Fig.1.5.Operationalpartofaalargeknowledgebase language processor 2 - handler 3, 4 - inference engines 5 - repository 6,7 - packages

12 12 Largeheterogeneousknowledgebases Query processor Analyser Fig.1.6.Referentialpartofalargeknowledgebase Metaknowledge Main knowledge-

13 transparencyoftheknowledgeintheknowledgebaseandsupportsboth usersqueries: knowledgeacquisitionanditsusage.therearetwowaysofprocessing answeringonthebasisofthemetaknowledge. directanalysisoftheknowledge EnnTyugu 13 asawhole.thesecondmethodisanalogoustotheusageofdatadictionariesindata-bases.itrequirestheexistanceofmetaknowledgeabout thecontentsoftheknowledgebase,whichcanbepresentedintheformof athesaurus,invertedlesoranobject-orientedinformationsystem.the problemsarisingattheconstructionofinformationsystemsofthiskind havebeendiscussedin(devanbu1991). Intherstcase,metaknowledgeisnotneeded,butthismethodisapplicableonlytolimitedamountsofknowledge,i.e.nottotheknowledgebase givenusthecondencethatallkindsofengineeringknowledgecanbe Thispartisbasedonourexperiencegainedinengineeringapplicationsof knowledge-basedprogrammingtools.mostoftheapplicationsbelongto alreadypresentedincomputerbymeansofknowledgetoolsavailablenow. engineeringdesignandotherengineeringelds.theseexperimentshave 5Contentsandsize totheelectrical,mechanicalandcivilengineeringelds,aswellassome arealsoexamplesofsuccessfuldevelopmentofknowledgebasesincivil theelectricalandmechanicalengineeringelds(pahapill1985),butthere ingknowledgebases.itshowsseveralclustersofknowledgecorresponding generalknowledgeinphysics,mathematicsandchemistry. Figure1.7showsa\landscape"formedbyourdevelopmentofengineer- costs.therefore,itisreasonabletodesignourknowledgebaseforengineeringapplicationssimilarlytoacurriculumofatechnicaluniversity,taking takeintoaccountthecostsofknowledgeacquisitionaswellasmaintenance sumethatavailableknowledgehandlingtoolsaresucientlypowerfuland edgebaseandteachingpeople.atarstglance,whentheamountofstored Onceagain,weshalluseheretheanalogybetweenbuildingupaknowl- knowledgeisconsidered,itcouldseemthatthemorethebetter.but,even ifweassumeanunlimitedcapacityoftheknowledgerepository,wemust intoaccountrestrictionsontheresourcesforteaching.indoingso,weasresentationformsandinferenceengines)forrepresentingtheknowledgewe cansupportallkindsofknowledgeneededforapplications.inotherwords, wefeelfreefromtechnicalrestrictionsinpresentingaconceptualdesignof knowledgebase. anengineeringknowledgebaseinthissection.however,wearerestricted bytheresourcesavailableforintroducingtheknowledge,for\teaching"a Wemustrememberthatanumberofdierentknowledgesystems(rep-

14 14 Largeheterogeneousknowledgebases filters circuit analysis transf. wind load Fig.1.7.A\landscape"ofengineeringknowledgebases shafts drives bearings hydraulics physics geometry beams walls chemistry

15 aregoingtousemustbeavailable.thechoiceofaknowledgesystemfor instance,nonspecicknowledgewhich,ingeneral,isseldomlyusedbyan representingaknowledge-moduleisnotpurelyatechnicalquestion.for engineermustberepresentedinamoretransparentwaythanspecicand theformofknowledge.wecanstillmakeacommentontheoverallorganizationoftheknowledgebase.knowledgeisrepresentedthereinmodules, microtheoriesrefertooneanotherinthesamewayastextsinahypertext. generalengineeringknowledgeandspecicengineeringknowledge.nonspecicknowledgecorrespondstogenerallyknownknowledgeinscience whichisbeingusuallytaughtinschoolsfortechnicallyorientedstudents. Generalengineeringknowledgeismainlyknowledgefromhandbooksand standards.specicengineeringknowledgeisthemostproblem-oriented andsupportsparticulardesignsorengineeringsolutionsbycomposingdifferentknowledgemodulesintoasinglebodyandbyprovidingthedesign solutionsandplans. Weclassifyengineeringknowledgeintothreecategories:nonspecic, well-understoodknowledge.but,hereweshouldnotbeconcernedwith EnnTyugu 15 oftheengineeringeld.thesemicrotheoriescanbenested.but,ingeneral,theyconstituteametasemantic(conceptual)network,wherethese eachofwhichwecanconsiderasamicrotheoryaboutsomenarrowpart basesegmentationandthisdoesnotreectinwhichformtheknowledge isactuallystored(organized)intheknowledgebase. Thefollowingisabriefoutlineofauniversalengineeringknowledge Nonspecicknowledge: mathematics chemistry physics heatandheattransfer optics electricity mechanics geometry symboliccomputations mathematicalknowledgecouldnotbepresentedinahardanddeepform. shallowwaytoday,likeingeneralhandbooksforengineerswhereonlybasicdenitionsandsomeformulasaregiven.thisdoesnotmeanthat time,andspecialeortshavebeendirectedtowardsautomatingmathematicalreasoning(debruijn1980). Partofthisnonspecicknowledgecanberepresentedonlyinavery... Theresearchtowardsformalisingmathematicshasbeengoingonforalong Generalengineeringknowledge:

16 ingelds: 16engineeringgraphics generalstandards generalengineeringhandbooks(tsandtolerancesetc.) Specicengineeringknowledgewhichisdividedintodierentengineer- Largeheterogeneousknowledgebases 2.electricity 1.mechanicalengineering basiclaws machineparts analysisofalternatingcurrentcircuits devicesandmachines structuredandformalformwhichiswellsuitedforcomputerisedprocessing.wehavebuiltknowledgemoduleswhichcanbeusedseparatlyaswell ageneralengineeringknowledgebase.wesuggesttomeasureknowledge bythenumberofbindings.thisisthenumberofarcsinanetworkrep- must,rstofall,satisfyinteroperabilityrequirements. representationofknowledge.theestimateisthatauniversalengineering 6Concludingremarks knowledgebasewillcontainatleast10mbindings. Wehaveestimatedtheamountofknowledgewhichmustbeputinto isinarathergoodpositionwhenbuildinganengineeringknowledgebase. Essentialpartoftheengineeringknowledgeisalreadypresentedinawell 3.civilengineering Lookingatthecontentsabove,onecanseethataknowledgeengineer... interface,whereasinthecaseofintegratedknowledgebasetheinterface astogetherwitheachother.thearchitecturaldierenceisthatwhena knowledgemoduleisusedindependently,itmusthaveaninteractiveuser resentationorthenumberofinstancesofnamesinthecaseofsymbolic present,thisgoalhasbeenpartiallyachieved onecanbuildaknowledge couldbeusedasanaidinprogrammingagreatvarietyofproblems.at knowledgebasesintoalargeheterogeneousone.thistaskpresentsnew bytheneedforasucientlyuniversalprogrammingknowledgebasewhich basewhichisanoracleforarestrictedknowledgedomain,andthishas nicantbreakthroughinproblem-solvingbyunitingsmallerandspecialized beendoneformanyparticulardomains.however,weexpecttogetasig- Asithasbeenstatedatthebeginningofthepaper,thisworkwasprovoked

17 requirementstotheknowledgebasewhichwerereviewedinsection3ofthe paper. andcontentsofalargeengineeringknowledgebasewasoutlined.thisenabledustoestimatetheamountoftheknowledgeneeded,andanestimate forageneralengineeringknowledgebaseis10mbindings. Someinsightsintothearchitectureofknowledgebaseswereobtained EnnTyugu 17 timatedintwodierentways:byguessinghowmuchworkisneededfor particularknowledgemoduleswhichconstitutethecontentsoftheknowledgebase.thesecondisadirectwayand,presumablymoreprecise.our whichshowsthatmorethan140manyearsarerequiredforbuildinga generalpurporseengineeringknowledgebase. experienceinintroducingtheknowledgemodulesgivesusaroughestimate Theamountofworkneededforbuildingaknowledgebasecanbees- introducingthegivenamountofknowledgeorbysummingupestimatesfor peoplearebeingtaught.thecrucialquestioninthiscasewillbetheabilityofaknowledgebasesystemtoperceivetheknowledge,i.e.\tolearn" IwouldliketothankmyformercolleaguesAhtoKaljaandJaakPa- gradually. 7Acknowledgements Finally,itseemsthatarealisticwaytogetalargeprogrammingknowledgebaseisbyevolutionarydevelopment.Thisremindsusagainhow knowledge-basesandtiittiidemannforshapingalandscapeoftheengineeringknowledge. Bibliography 1.deBruijn,N.(1980).AsurveyoftheprojectAutomath.Essaysin hapillfromtallinnfordevelopingnumerousmicrotheoriesforengineering 2.Devanbu,P.,Brachman,R.J.,SelfridgeP.G.,BallardB.W.(1991). J.R.Hindley)AcademicPress,586{606. combinatorylogic,lambdacalculusandformalism.(ed.j.p.seldinand LaSSIE:AKnowledge-BasedSoftwareInformationSystem.Comm. 6.Pahapill,J.(1985).ProgrammpaketzurModellierungderHydromaschinenSysteme.6.FachtagungHydraulikundPneumatik,Magdeburg.609{ 3.Genesereth,M.R.,Nilsson,N.J.(1987).LogicalFoundationsofArticialIntelligence.MorganKaufmanPublishers. ofsymboliccomputations,no.5,359{ Mints,G.,Tyugu,E.(1988).TheProgrammingSystemPriz.Journal 4.Lenat,D.(1990).Cyc:TowardsProgramswithCommonSense.Comm. ACM,v.34,No.5,34{49. ACM,v.33,No.8,30{

18 9.Tyugu,E.(1991).ModularityofKnowledge.MachineIntelligence12. 7.Tyugu,E.(1988).Knowledge-BasedProgramming.AddisonWesley 18 8.Tyugu,E.(1991).ThreeNew-GenerationSoftwareEnvironments. (Ed.J.E.Hayes,D.MichieandE.Tyugu)ClarendonPress,Oxford, Comm.ACM,v.34,No.6,46{59. PublishersLtd.(TuringInstitutePress). Largeheterogeneousknowledgebases 10.Wileden,J.C.,Wolf,A.L.,Rosenblatt,W.R.,Tarr,P.L.(1991). 3{16. Specication-LevelConformity.Comm.ACM,v.34,No.5,72{87.