Creating the highly automated environment that network operators and digital service providers will need in the near future requires the support of intelligent agents that are able to work collaboratively. At Ericsson, we believe that the most effective way to create such intelligent agents is by combining machine reasoning and machine learning techniques. This Ericsson Technology Review article explains the role that these two cognitive technologies play in the creation of intelligent agents that have a detailed semantic understanding of the world and their own individual contexts. It also includes two proofs of concept that help demonstrate how the combination of machine reasoning and machine learning techniques makes it possible to create intelligent agents that are able to learn from diverse inputs, and share or transfer experience between contexts.

1. ERICSSON
TECHNOLOGY
COGNITIVE
TECHNOLOGIES
ANDAUTOMATION
C H A R T I N G T H E F U T U R E O F I N N O V A T I O N | # 6 ∙ 2 0 1 8
Induced
models
Inferred
knowledge
Knowledge
transfer
Knowledge
extraction
Training
examples
Expert
knowledge
Predictions
Features
Actions
Reasoning
Planning
Actions
Machine
learning
(Numeric)
Machine
reasoning
(Symbolic)
Induced
models
Inferred
knowledge
Knowledge
transfer
Knowledge
extraction
Training
examples
Expert
knowledge
Predictions
Features
Actions
Reasoning
Planning
Actions
Machine
learning
(Numeric)
Machine
reasoning
(Symbolic)

2. ✱ COGNITIVE TECHNOLOGIES
2 ERICSSON TECHNOLOGY REVIEW ✱ JUNE 28, 2018
JÖRG NIEMÖLLER,
LEONID MOKRUSHIN
The need to support emerging technologies
will soon lead to radical changes in the
operations of both network operators and
digital service providers, as their businesses
tend to be based on a complex system of
interdependent, manually-executed
processes. These processes span across
technical functions such as network
operation and product development, support
functions such as customer care, and
business-level functions such as marketing,
product strategy planning and billing.
Manually-executed processes represent a
major challenge because they do not scale
sufficiently at a competitive cost.
■Automationisanessentialpartofthesolution.
AtEricsson,weenvisionanewinfrastructurefor
networkoperatorsanddigitalserviceprovidersin
whichintelligentagentsoperateautonomouslywith
minimalhumaninvolvement,collaboratingtoreach
theiroverallgoals.Theseagentsbasetheirdecisions
onevidenceindataandtheknowledgeofdomain
experts,andtheyareabletoutilizeknowledgefrom
variousdomainsanddynamicallyadapttochanged
contexts.
Cognitivetechnologies
Softwarethatisabletooperateautonomouslyand
makesmartdecisionsinacomplexenvironmentis
referredtoasanintelligentagent(apractical
Forward-looking network operators and digital service providers require an
automated network and business environment that can support them in the
transition to a new market reality characterized by 5G, the Internet of Things,
virtual network functions and software-defined networks. The combination
of machine learning and machine reasoning techniques makes it possible to
build cognitive applications with the ability to utilize insights across domain
borders and dynamically adapt to changing goals and contexts.
Cognitive
IN NETWORK AND BUSINESS AUTOMATION
technologies

3. COGNITIVE TECHNOLOGIES ✱
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Figure 1: The model of mind
Sensing Thinking Acting
Knowing
Known
facts
Previous
experience
implementationofartificialintelligenceand
machinelearning).Itperceivesitsenvironmentand
takesactionstomaximizeitssuccessinachievingits
goals.Thetermcognitivetechnologiesreferstoa
diversesetoftechniques,toolsandplatformsthat
enabletheimplementationofintelligentagents.
ThemodelofmindshowninFigure1illustrates
themaintasksofanintelligentagent,andthusthe
mainconcernsofcognitivetechnologies.Themodel
describestheprocessofderivinganactionor
decisionfrominputandknowledge.
Anintelligentagentneedsamodelofthe
environmentinwhichitoperates.Technologiesused
tocaptureinformationabouttheenvironmentare
diverseanduse-casedependent.Forexample,
naturallanguageprocessingenablesinteraction
withhumanusers;networkprobesandsensors
delivermeasuredtechnicalfacts;andananalytics
systemprocessesdatatoproviderelevantinsights.
Thepurposeofintelligentagentsistoperform
Terms and abbreviations
CPI – Customer Product Information | eTOM – Enhanced Telecom Operations Map |
SID – Shared Information/Data | SLI – Service Level Index | TOVE – Toronto Virtual Enterprise

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4 ERICSSON TECHNOLOGY REVIEW ✱ JUNE 28, 2018
actionsandcommunicatesolutions.Acting
complementssensingininteractionwiththe
environment.Thechoiceoftechniquesandtools
isequallydiverseanduse-casedependent.
Forexample,speechsynthesisenablesconvenient
communicationwithusers,roboticsinvolves
mechanicalactuation,andanintelligentnetwork
managercanactbyexecutingcommandsonthe
equipmentorchangingconfigurationparameters.
Thethinkingphaseinthemodelofmindisthe
sourceoftheintelligenceinanintelligentagent.
Thinkingcanbeimplemented,forexample,asa
logicprograminProlog,inanartificialneural
network,orinanyothertypeofinferenceengine,
includingmachine-learnedmodels.
Thethinkingphasederivesitsdecisionsfrom
factsandpreviousexperiencesstoredina
knowledgebase.Thekeyisamachine-readable
knowledgerepresentationintheformofamodel.
Graphdatabasesandtriplestoresarefrequently
usedforefficientstorage.Formalknowledge
definitioncanbeachievedusingconceptsofRDF
(theResourceDescriptionFramework)or
descriptionlanguages,suchasUML(theUniversal
MarkupLanguage)orOWL(theWebOntology
Language).
Machinelearningandmachinereasoning
Therearetwotechnologicalpillarsonwhichan
intelligentagentcanbebased:machinelearningand
Figure 2: Machine reasoning and machine learning [1]
Induced
models
Inferred
knowledge
Knowledge
transfer
Knowledge
extraction
Training
examples
Expert
knowledge
Predictions
Features
Actions
Reasoning
Planning
Actions
Machine
learning
(Numeric)
Machine
reasoning
(Symbolic)

5. COGNITIVE TECHNOLOGIES ✱
JUNE 28, 2018 ✱ ERICSSON TECHNOLOGY REVIEW 5
machinereasoning(illustratedinFigure2).Both
involvemakingpredictionsandplanningactions
towardagoal.Eachhasitsownstrengthsand
weaknesses.
Machinelearningreliesonstatisticalmethods
tonumericallycalculateanoptimizedmodelbased
onthetrainingdataprovided.Thisisdrivenby
wantedcharacteristicsofthemodel,suchaslow
averageerrorortherateoffalsepositiveornegative
predictions.Applyingthelearnednumericalmodel
tonewdataleadstopredictionsoraction
recommendationsthatarestatisticallyclosest
tothetrainingexamples.
AnexampleofalearnedmodelistheService
LevelIndex(SLI)[2][3]implementedinEricsson
ExpertAnalytics,whichpredictsauser’slevelof
satisfaction.Thetraininginputismeasurements
fromnetworkprobesthatshowtheQoSdeliveredto
theusercombinedwithsurveysinwhichusersstate
theirlevelofsatisfaction.Thelearnedmodelpredicts
thissatisfactionlevelfromnewQoSmeasurements.
Machinereasoninggeneratesconclusionsfrom
symbolicknowledgerepresentation.Widelyused
techniquesarelogicalinductionanddeduction.
Itreliesonaformaldescriptionofconceptsina
model,oftenorganizedasanontology.Knowledge
abouttheenvironmentisassertedwithinthemodel
byconnectingabstractconceptsandterminologyto
objectsrepresentingtheentitiestobeusedand
managed.Forexample,“customersatisfaction,”
“user”and“quantifies”areabstractconcepts.Based
onthese,wecanassertthat“Adam”isauserand“4”
istheSLIvaluerepresentinghissatisfaction.Wecan
furtherassertinferencerules:“SLIquantifies
satisfaction,”“SLIbelow5islow,”“lowsatisfaction
causeschurn”.Basedonthisknowledge,amachine-
reasoningprocesswouldlogicallyconcludethat
Adamisabouttochurn.Itwouldtracethereasonto
thelowSLIvalue.
Hybridapproachestosymbolicneuralnetworks
alsoexist.Thesearedeepneuralnetworkswitha
numericandstatistics-basedcoreandanimplicit
mappingofthemodel’snumericvariablestoa
symbolicrepresentation.
Designingintelligentagents
Autonomousintelligentagentssupporthuman
domainexpertsbyfullytakingovertheexecutionof
operationaltasks.Doingthisconvincinglyrequires
themtoreactandexecutefasterthanhumansandbe
abletoovercomeunexpectedsituations,while
makingfewererrorsandscalingtoahighnumberof
managedassetsandtasks.
Intelligentagentsaredevelopedanddeployedina
softwarelifecycle.Assuch,theyprofitfromthe
encapsulationprovidedbyamicroservice
architecture,comprehensiveandperformantdata
routingandmanagement,andadynamically
scalableexecutionenvironment.Theabilitytocreate
anoptimalthinkingcoreforanintelligentagent
requiresagoodunderstandingofthefundamental
characteristicsofmachinelearningandmachine
reasoning.
Theroleofabstraction
Apersonusesabstractiontodistillessential
informationfromtheinputpresented.Abstraction
providesfocusandeasier-to-graspconceptsasa
baseforreasoninganddecisions.Italsofacilitates
communication.
AUTONOMOUSINTELLIGENT
AGENTSSUPPORTHUMAN
DOMAINEXPERTSBYFULLY
TAKINGOVERTHEEXECUTION
OFOPERATIONALTASKS

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Interactingwithapersonorwithanother
intelligentagentrequiresanintelligentagenttohave
theabilitytooperateonthesamelevelofabstraction
withasharedunderstandingofconceptsand
terminology.Thisincludes,forexample,howgoals
areformulatedandhowtheintelligentagents
presentinsightsanddecisions.
Machine-learnedmodelsarenumerical.They
manageabstractionbymappingmeaningto
numericalvalues.Thisconstitutesanimplicit
translationlayerbetweenthenumerical
representationandtheabstractsemantics.
Ontology-basedmodelsaresymbolic.Withinan
ontology,objectsareestablishedandlinkedtoeach
otherusingpredicates.Machinereasoningdraws
inferencefromthisrepresentationbylogical
inductionanddeduction.
Thesymbolicrepresentationassignedtoobjects,
predicatesandnumericvaluesisconvention.Itis
chosentousethesameabstractionandthesame
terminologyasthedomainitreflects.Thisfacilitates
anintuitiveexperiencewhenuserscreateand
maintaintheknowledgebase.
Businessstrategyplanningisagoodexampleofa
highlyabstractdomain.Itdealswithconceptssuch
asgrowth,churn,customers,satisfactionandpolicy.
Numericaldataneedstobeinterpretedtodelivera
meaningfulcontributionatthislevel.Anintelligent
agentperformingthisinterpretationofdataisa
valuableassistantinbusiness-levelprocesses.
Theintroductionofintelligentagentswillnot
makedomainexpertsunnecessary.Instead,thetask
oftheexpertshiftsfromdirectinvolvementin
operationalprocessestomaintenanceofthemodels
thatdictatetheoperationofautonomousagents.
Theabstractionofthemodelscontributestothe
efficiencyofthedomainexpert.Apracticalexample
isthedesignofdecisionprocessesofexpertsystems
proposingactions.Thesesystemsreachananswer
bycheckingatreeofbranchingconditions.Even
withasmallnumberofvariables,manuallydesigning
theseconditionsisatime-consumingand
unintuitivetask.Anintelligentagentcancompile
thetreefromknowledgeaboutthereasonsfor
proposinganaction.Managingtheabstractrules
isaconsiderablymoreintuitivebecause the
abstractionrisestotheleveltheexpert
isusedtothinkingat.
Obtainingandmanagingknowledge
Theintelligentdigitalassistantexample(seeproof
ofconcept#1onpage8)demonstratesanautomated
processthatcontributesknowledge.Theassistantis
generatedfromproductmanualswritteninnatural
languagebyadocument-crawlerapplication.Based
onexistingknowledge,itidentifiesandclassifiesthe
informationprovidedinthedocuments.Itasserts
thisinformationasadditionalknowledge.
Furthermore,sitedatastoredincatalogsand
inventoriesisautomaticallyandcontinuously
assertedintheknowledgebase.Thiskeepsthe
knowledgeup-to-date,andthereasoningresults
adaptdynamicallytochangedfacts.
Theintelligentdigitalassistantalsousesimage
recognition.Itidentifiesphysicalelementsandthe
currentsituationfromimagesandassertsitsfindings
intheknowledgebase.Thisdemonstratesa
transformationofnumericdataintosymbolic
knowledge.Deep-learningbasedneuralnetworks
areparticularlysuccessfulatthistaskofidentifying
BUSINESSSTRATEGY
PLANNINGISAGOOD
EXAMPLEOFAHIGHLY
ABSTRACTDOMAIN

7. COGNITIVE TECHNOLOGIES ✱
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patternsindataandclassifyingthemsymbolically.
Theintelligentdigitalassistant’suseofimage
recognitionanditsabilitytoreadnaturallanguage
documentsshowthatnotallknowledgeformachine
reasoningneedstooriginatefromahumandomain
expert.Machine-learning-basedprocessescanadd
knowledgeandkeepitup-to-datebasedonwhatis
learnedfromdata.
Inthisrespect,itisimportanttodifferentiate
betweendataandknowledge.Dataisvaluesas
providedbytheenvironment.Knowledgeisthe
interpretationofthesevalueswithrespecttothe
semanticsthatareappliedtogivethedataits
meaning.Dataandinformationmodelscategorize
dataobjects.Analyticscreatesfurtherknowledge
frommultipledataelementsandthedomaincontext.
Aknowledgebasepreservesthisknowledgefor
reasoning.Whenfacingcontinuouslychangingdata,
aswarmofspecializedintelligentagentscankeep
theknowledgeup-to-date.
Inmachinelearning,thelearnedmodelisthe
knowledge,andtrainingexamplesarethemain
source.Domainexpertsareinvolvedinselecting
variablesanddatasources,andinconfiguringthe
learningprocessesaccordingtouse-casegoalsand
constraints.Thesuccessoflearning–and
consequently,theperformanceofalearning-based
intelligentagent–mainlydependsontheavailability
andqualityoftrainingdata.
Reinforcementlearningisavariantofmachine
learningthatlearnsfromasetofrulesanda
simulationoftheenvironment.Therefore,itdoes
notnecessarilydependonexampledata.However,
thelearnedmodelisalsonotbasedonexperience.
Themanualdesignofknowledgebydomain
expertsremainsamajorsourceofknowledgefor
machinereasoning.Thedomainexpertscreatea
stablecoreframeworkofassertedterminologyand
concepts.Basedonthis,theyexpresstheirdomain
expertisebyassertingfurtherconceptsand
inferencerules.Theyalsodesigntheapplications
thatassessdatasourceandautomaticallyassert
knowledge.Thisrequiresstafftobewelltrained
inknowledgemanagement,withefficientprocesses
andtoolsforknowledgelife-cyclemanagement.
Awell-designedmetamodelestablishesa
standardforconsistentknowledgerepresentation.
Anyknowledgemanagementcompetencegapcan
usuallybefilledbyknowledgeengineers,whocan
listentothedomainexpertsandtransfertheir
knowledgeintoamodel.
Amajortaskinmodelingisassemblinga
knowledgebaseaccordingtouse-case
requirements.Ontologiescanintegrateand
interconnectanyformallydefinedmodelallowing
extensivereuse.Forexample,dataandinformation
modelsusedinapplicationprogramminginterface
designconstituteafoundationforassertingdata
objects.eTOM[4]andSID[5]areindustry-standard
modelscontributingcommontelecommunication
terminology.TOVE[6][7]orEnterpriseOntology[8]
cancoverbusinessconcepts.Theywereusedinthe
businessanalyticsorchestrationexample[9]
(seepage9)forinterpretingbusiness-levelquestions.
Animportantpartoftheknowledgeof
autonomousintelligentagentsistheirgoals.
Thedomainexpertusesgoalstotelltheintelligent
agentwhatitissupposedtoaccomplish.Ideally,
theyareformulatedasabstractbusiness-levelgoals
deriveddirectlyfromthebusinessstrategyofthe
organization.Thisrequiresbroadknowledgeand
adaptabilitytobebuiltintotheintelligentagents,
butitpromisesahighlevelofautonomy.
ITISIMPORTANTTO
DIFFERENTIATEBETWEEN
DATAANDKNOWLEDGE

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Sensing Thinking Acting
Knowing / Modeling
Query &
answer dialog
Inference &
planning Presentation
Expert rules
Document
crawler
Linked data
adaptation
Context
data
Object
detection
Knowledge
base
CPI
store
Site
data
Theintelligentdigitalassistant(seeFigure3)is
designedtoassistfieldtechnicianswhoservice
basestations[10].Thetechnicianinteractswiththe
assistantthroughamobiledevice.Theassistantuses
augmentedrealitytoderivethebasestationtype,
configurationandstatethroughobjectdetection
andvisiblelightcommunication.Forexample,itcan
readthestatusLEDofthedevice.Theassistant
providesinstructionsandvisualguidancetothe
technicianduringmaintenanceoperations.
Itdownloadscontextualdataaboutthesiteand
requestsanyadditionalinformationthatcould
notberetrievedautomaticallythroughaquery
andanswerdialogue.
Theintelligentdigitalassistantiscurrently
aproofofconceptimplementedbyEricsson
Research.Wehaveimplementedanddeployedthe
machine-reasoningsystemonbackendservers.
Thesystemcollectssensedinput,analyses
symptomsandpresentscorresponding
maintenanceproceduresasaproposedseriesof
actions.Domainexpertshavemanuallydesigned
theproceduralknowledgeforproblemresolution.
Additionally,adocumentcrawlerautomatically
readsoperationaldocumentation,whichallowsthe
assistanttopresentdocumentsthatarerelevantfor
thecurrenttaskstothetechnicianforreference.
#1: INTELLIGENT DIGITAL ASSISTANT
Proofofconcept
Figure 3: Intelligent digital assistant

9. COGNITIVE TECHNOLOGIES ✱
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SensingThinkingActing
Sensing
Knowing
Thinking Acting
Knowledge
base
Query
interface
Query
analysis
Analytics
orchestration
Explanation
of results
Results
analysis
Assertion
of insights
Processing
Business domain concepts
Analytics service descriptions
Data interpretation rules
Thebusinessanalyticsorchestrationusecase
(see Figure4)wasimplementedatEricssonasa
proofofconceptwithinamasterthesisproject[9].
Itdemonstrateshowtheabstractlevelofbusiness
conceptscanbelinkedwiththetechnicallevelof
data-drivenanalytics,sothatintelligentagentscan
operateacrossthelevels.Theusecasestartswith
abusinessquestionthatcanbesolvedthrough
analytics.Anintelligentagentactsasabusiness
consultant,providinganalytics-basedassistance
toauser.Itanalyzesthequestion,planstheneeded
analyticsandorchestrates theexecutionofsuitable
analyticsapplications.Whentheresultsareavailable,
theintelligentagentreasonsabouttheirmeaningin
thecontextofthequestionandexplainstheanswer
totheuser.
Theinferenceisbasedonaknowledgebase
thatcontainsacombinationofabusinessconcept
ontologyandabstractservicedescriptionsof
analyticsapplications.Itwasbuiltusingexistingand
freelyavailablebusinessontologiescombinedwith
manually-designedknowledge.
#2: BUSINESS ANALYTICS ORCHESTRATION
Proofofconcept
Figure 4: Business consulting through analytics

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Machinelearningandmachinereasoning
hybridsolutions
Gooddecisionsandplansareoftenbasedon
understandingmultipledomains.Forexample,
expertsinnetworkoperationknowaboutnetwork
incidentsandtheappropriateprocedurestosolve
them.Theycananalyzetechnicalrootcausesand
applycorrectiveandpreventiveactions.Thesame
expertsusuallyalsoknowsomefactsaboutthe
broaderbusinessenvironment.Knowingabout
financialgoalsandServiceLevelAgreementshelps
themtoprioritizetasks.Byunderstandingthe
applicationdomainofadeviceortheconcernsofa
user,theycancustomizethesolution.Theymight
alsoknowaboutmarketingeffortsorproductsin
developmentandproactivelyprovideconsulting.
Allthisknowledgeallowsanexperttomaketheright
decisions.Forintelligentagents,itisachallengeto
operatewiththesameamountofdiverseknowledge
andtoprovideanequallydiverserangeofactions.
Theroleofmachinereasoning
Theknowledgeusedinmachinereasoningispure
datadecoupledfromtheimplementationofthe
inferenceengine.Changesinbehaviorand
extensionsofscopemustthereforebereached
bychangingthemodeldataratherthanthe
implementationoftheintelligentagent.Therefore,
machine-reasoningmodelsarewellsuitedto
integratingontologiesandinferencerulesfrom
multipledomains,ifformalandsemantic
consistencyispreserved.
Ideally,alayerofcoreconceptsandterminology
commontoalldomainsshouldbeusedtoanchor
domain-specificmodels.Thisallowsinference
enginestotraverseacrossdomainbordersanddraw
conclusionsfromallconstituentdomainmodels.
Ifthemodelsfromdifferentdomainsalreadyuse
similarconcepts,butdefinethemdifferently,a“glue”
modelcanrelatethembyintroducingknowledge
aboutthedifferences.
Thedrawbacksofthemulti-domainknowledge
basedescribedherearethecomplexityof
maintainingmodelconsistencyandtheperformance
oftheinferencegenerationduetothenumberof
knowledgeelementstoprocess.
Theroleofmachinelearning
Inmachinelearning,eachadditionaldomain
contributesyetanothersetofvariablesadding
furthernumericaldimensionstothemodel.
Thisintroduceschallengessuchastheneedfor
trainingexamplesthatcontainconsolidateddata
samplesfromalldomains.Thereisalsoanincrease
inthenumberofdatapointsrequiredtoreach
acceptablestatisticalcharacteristics.Thecombination
ofmoredimensionsandhigherdatavolumeincreases
theprocessingcost.Furthermore,eachchangein
scoperequiresafulllife-cycleloopincludingdata
selection,implementation,deploymentandlearning
untilanewmodelisavailableforproductiveuse.
Consideringthesechallenges,machine-learned
modelsarebestsuitedtobespecialistsinconfined
tasks.Asecondarylayerofmodelscanthenbuildon
thespecialistinsightsandevaluatetheminabroader
context.Thesecondtieroperatesonhigher
abstractionwithconceptsfrommultipledomains.
However,sincetrainingexamplesatthislevelare
broadinscope,theytendtobehardtoobtain.
Domainexpertsarestillavailable,though,sousing
machinereasoningisalwaysfeasible.Ingeneral,
machinelearningexcelsatinferencethatresults
fromprocessinglargeamountsofdata,while
machinereasoningworksverywellindrawing
conclusionsfrombroad,abstractknowledge.
Hybridsolutions
Theresultisanenvironmentcomprisedof
orchestratedorchoreographedintelligentagents.
Coordinationandcollaborationisdonethroughthe
knowledge.Amachine-learnedmodelcan
contributeitsfindingsthroughasynchronous
assertion.Amappingapplicationisdesignedto
monitorthenumericoutputofamachine-learned
modeloranalyzethelearnednumericmodelitself.
Whennewoutputisgenerated,oranewversionof

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themodelisavailable,themappingapplication
interpretsitinthedomaincontext,determinesits
meaningandgeneratesarespectivesymbolic
representation.Thisconstitutesnewknowledge
thatisassertedintotheknowledgebase.
Alternatively,anapplicationincorporatinga
machine-learnedmodelcanbelinkeddirectlyinto
theknowledgebaseactingasaproxyfora
knowledgeobject.Areasoningprocesswouldcall
thelinkedapplicationwhentherespective
knowledgeisneeded.Theapplicationgeneratesa
replybasedonallcurrentlyavailabledata.
Bothmethodscreateahybridofmachinelearning
andmachinereasoningthatenablesdynamic
adaptationofthereasoningresultsbasedon
learningandthelatestdata.Asynchronousassertion
actslikeadomainexpertcontinuouslyupdating
knowledge.Aknowledgeproxyapplication
synchronouslygeneratesknowledgeondemand.
However,thiscomesatthecostofdelayingthe
reasoningprocess.
Symbolicneuralnetworks
Symbolicneuralnetworksspecializeinlearning
abouttherelationshipsbetweenentities.They
implicitlyabstractfromanunderlyingstatistical
model,whichallowsthemtoanswerabstract
questionsdirectly.Oneexampleisimageprocessing
combiningmultiplemachine-learnedmodels.
Onemodelidentifiestheobjectsseen.Another
learnsabouttherelationshipbetweentheobjects.
Athirdhaslearnedtointerpretquestionsasked.
Duetotheimplicitabstractionanduseofsymbolic
representation,theinsightsgeneratedbythese
modelswouldintegrateseamlesslyintoaknowledge
baseandfurtherreasoning.However,getting
referencedataforlearningisachallengeinthis
scenarioandwouldusuallybedependentonhuman
expertscreatingsamples.Asthissetuphasmachine
learningatitscore,italsodoesnotscalewelltoahigh
numberofconcernsandvariables.Nevertheless,
itcanfindandcontributeknowledgeaboutnew
relationshipsthatwashithertounknowntoexperts.
Tieredimplementation
Thetieredimplementationapproachusesmachine
learningonthelayerofspecialistmodelsand
machinereasoningforconsolidationacross
domains.Thisassignmentofrolesreflectsstrengths
ofthetechnologyfamilies,althoughadifferent
selectionispossibledependingontheusecaseand
environment.Forexample,machinelearningmaybe
successfullyappliedforcross-domainconsolidation
iftrainingdataisavailable.Andmachinereasoning
canimplementaspecialistintelligentagent,for
example,ifitincorporatesthemanually-designed
rulesofahumandomainexpert.
Conclusion
Intelligentagentswiththeabilitytowork
collaborativelypresentthebestopportunityfor
networkoperatorsanddigitalserviceprovidersto
createtheextensivelyautomatedenvironmentthat
theirbusinesseswillrequireinthenearfuture.
Cognitivetechnologies–andinparticulara
combineduseofmachinereasoningandmachine
learning–providethetechnologicalfoundationfor
developingthekindofintelligentagentsthatwill
makethisflexible,autonomousenvironmenta
reality.Theseagentswillhaveadetailedsemantic
understandingoftheworldandtheirownindividual
contexts,aswellasbeingabletolearnfromdiverse
inputs,andshareortransferexperiencebetween
contexts.Inshort,theyarecapableofdynamically
adaptingtheiractionstoabroadrangeofdomains
andgoals.
COGNITIVETECHNOLOGIES
WILLMAKETHISFLEXIBLE,
AUTONOMOUSENVIRONMENT
AREALITY

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tove/
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DBB2BAA9F8398
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availableat:https://www.ericsson.com/en/mobility-report/reports/june-2018

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JUNE 28, 2018 ✱ ERICSSON TECHNOLOGY REVIEW 13
Further reading
〉〉 CIO, Artificial intelligence is about machine reasoning – or when machine learning is just a fancy plugin,
November 3, 2017, Rene Buest, available at: https://www.cio.com/article/3236030/machine-learning/
artificial-intelligence-is-about-machine-reasoning-or-when-machine-learning-is-just-a-fancy-plugin.html
〉〉 Microsoft Research, From machine learning to machine reasoning – An essay, February 13, 2013, Léon
Bottou, available at: https://www.microsoft.com/en-us/research/wp-content/uploads/2014/01/mlj-2013.pdf
Jörg Niemöller
◆ is an analytics and
customer experience expert
in solution area OSS. He
joined Ericsson in 1998
and spent several years at
Ericsson Research, where
he gained experience
of machine-reasoning
technologies and developed
an understanding of their
business relevance.
He is currently driving
the introduction of these
technologies into Ericsson’s
portfolio of Operations
Support Systems / Business
Support Systems solutions.
Niemöller holds a degree in
electrical engineering from
TU Dortmund University
in Germany and a Ph.D. in
computer science from
Tilburg University in the
Netherlands.
Leonid Mokrushin
◆ is a senior specialist in
cognitive technologies
at Ericsson Research.
His current focus is
on investigating new
opportunities within artificial
intelligence in the context
of industrial and telco use
cases. He joined Ericsson
Research in 2007 after
postgraduate studies at
Uppsala University, Sweden,
with a background in real-
time systems. He received
an M.Sc. in software
engineering from Peter
the Great St. Petersburg
Polytechnic University,
Russia, in 2001.
theauthors

14. ✱ COGNITIVE TECHNOLOGIES
14 ERICSSON TECHNOLOGY REVIEW ✱ JUNE 28, 2018
ISSN 0014-0171
284 23-3316 | Uen
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