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Ericsson Technology Review: Tackling IoT complexity with machine intelligence

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IoT-based systems will require a high level of decision making automation both in terms of infrastructure management and within the logic of the IoT applications themselves. Decision support systems (DSSs) are an essential tool in this context on account of their ability to enhance human decision-making processes with machine intelligence. The cognitive automation framework that we have created at Ericsson speeds up the development and deployment of intelligent DSSs by reusing as much knowledge as possible, including domain models, behaviors and reasoning mechanisms. The framework substantially reduces operational costs for IoT-based system management by enabling DSSs to make decisions about how to adapt to changes in their context and environment with minimal or no human interaction.

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Ericsson Technology Review: Tackling IoT complexity with machine intelligence

  1. 1. THE IoT AND COGNITIVE AUTOMATION ✱ 1APRIL 6, 2017 ✱ ERICSSON TECHNOLOGY REVIEW ERICSSON TECHNOLOGY Observer Connectedvehicles& passengermobilephones S re Legend: Raw data streams Component under development Implemented component Component in place – open source software 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 | # 4 ∙ 2 0 1 7 COGNITIVE AUTOMATION ASANIoT ENABLER
  2. 2. ✱ THE IoT AND COGNITIVE AUTOMATION 2 ERICSSON TECHNOLOGY REVIEW ✱   APRIL 6, 2017 ANETA VULGARAKIS FELJAN, ATHANASIOS KARAPANTELAKIS, LEONID MOKRUSHIN, RAFIA INAM, ELENA FERSMAN, CARLOS R. B. AZEVEDO, KLAUS RAIZER, RICARDO S. SOUZA Internet of Things (IoT) applications transcend traditional telecom to include enterprise verticals such as transportation, healthcare, agriculture, energy and utilities. Given the vast number of devices and heterogeneity of the applications, both ICT infrastructure and IoT application providers face unprecedented complexity challenges in terms of volume, privacy, interoperability and intelligence. Cognitive automation will be crucial to overcoming the intelligence challenge. The IoT is built on the concept of cross- domain interactions between machines that can communicate with each other without human involvement. These interactions generate a vast number of heterogeneous data streams full of information that must be analyzed, combined and acted on. ■ Tobesuccessful,providersofICTinfrastructure andIoTapplicationsneedtoovercomeinterlinking challengesrelatingtovolume,privacy,interoperability andintelligence.Doingsorequiresamultifaceted approachinvolvingconceptsandtechniquesdrawn frommanydisciplines,asillustratedinFigure1. Researchin5Gradio,networkvirtualizationand distributedcloudcomputingprimarilyaddresses thevolumechallenge,byevolvingnetwork infrastructureandapplicationarchitecture designtoincreasetheamountofresources availabletoapplications(throughput,compute andstoreresources,forexample).Meanwhile, theprivacyandinteroperabilitychallengesare beingaddressedwithamixofR&Deffortsand standardizationactivitiesthatareleadingtonovel conceptsandtechniques,suchasdifferential privacy,k-anonymityalgorithms,securemultiparty computation,ontologymatching,intelligentservice discovery,andcontext-awaremiddlewarelayers. Addressingtheintelligencechallengerequires thedevelopmentanduseofintelligentdecision TacklingIoT complexityWITH MACHINE INTELLIGENCE
  3. 3. THE IoT AND COGNITIVE AUTOMATION ✱ 3APRIL 6, 2017 ✱ ERICSSON TECHNOLOGY REVIEW supportsystems(DSSs)–interactivecomputer systemsthatuseacombinationofartificial intelligenceandmachinelearningmethodsknown asmachineintelligence(MI)toassistandenhance humandecisionmaking.Inresponsetothis need,Ericssonhasbuiltacognitiveautomation frameworktosupportdevelopmentandoperation ofintelligentDSSsforlarge-scaleIoT-based systems.Theusecasesarenotonlythetraditional telecomones(suchasoperationssupportsystems/ businesssupportsystemsautomation),butnew onesindomainssuchastransportation,robotics, energyandutilities,aswell. Themainbenefitofourcognitiveautomation frameworkisthatitreducesDSSdevelopmentand deploymenttimebyreusingasmuchknowledge aspossible(suchasdomainmodels,behaviors, architecturalpatterns,andreasoningmechanisms). ItalsocutsoperationalcostsforIoT-basedsystem managementbymakingitpossibleduringruntime foraDSStodecideautomaticallyhowtoadapt tochangesinitscontextandenvironment,with minimalornohumaninteraction. WhileDSSspredominantlyaddressthe intelligencechallenge,theycanalsobeusedto addressprivacy,volumeandinteroperability challenges.Forexample,IoTdevicesneed intelligencetolearntotrusteachother. Managementofnetworkinfrastructuremayalso benefitfromintelligentsystems,tohandlethe largevolumesoftransmitteddatamoreefficiently. Finally,systemsthatusedifferentstandardsand/ orAPIscanuseMItointerfacewitheachother,by usinglanguagegames,forexample. Architecturalcomponentsandstructure oftheknowledgebase Ahigh-levelconceptualviewofthearchitectureof ourframeworkisdepictedin Figure2.Themain componentsaretheobserver,theknowledgebase, thereasonerandtheinterpreter. Theobserverisresponsibleforpushing knowledgefromtheenvironmenttotheknowledge base.Thisisdonebyfirstderivingasymbolic representationofdatausingmodeltransformation Figure 1 Key challenges (in gray) and approaches to addressing them (in pink) Volume challenge: large increase in data traffic Privacy challenge: sharing and processing sensitive data Interoperability challenge: talking the same language Intelligence challenge: sensing and making sense Standardization and common APIs Cloud computing models Machine intelligence Cloud computing models Distributed trust models
  4. 4. ✱ THE IoT AND COGNITIVE AUTOMATION 4 ERICSSON TECHNOLOGY REVIEW ✱   APRIL 6, 2017 rulesthatidentifytherelationbetweenrawdata comingfromtheIoT-basedsystemandasymbolic representationofthecaptureddata.Thenextstepis toperformETL(extract,transform,load),followed byasemanticanalysisoftheobtaineddata. Theknowledgebasecontainsaformalized descriptionofgeneralconceptsthatcanbeused acrossdomainstofacilitateinteroperability,as wellasdomain-specificconcepts(transportation, forexample).Inaddition,theknowledgebase containsthepossiblediscretestatesofthesystem, potentialtransitionsbetweenstates,meta- reasoningexpertiseandmodeltransformation rules.OntologiesinRDF/OWLsemanticmarkup languageareamongthepossibleformatsofthe knowledgestoredintheknowledgebase[1]. Domainexpertscanenterdomainandreasoning expertisedirectlyintotheknowledgebase.Since thebehavioroftheenvironmentisdynamicand unpredictable,theknowledgebaseiscontinuously updatedwithknowledgecomingfromtheobserver andthereasoner. Mostoftheframeworkintelligencecomesfromthe reasoner,whichcontainsgeneralpurposeinference mechanismsthatallowittodrawconclusionsfrom information(propositions,rules,andsoon)stored intheknowledgebase,andproblem-specifictools thatimplementvariousMItaskssuchasmachine learning,planning,verification,simulation,and soon.Byrelatingagoal/missionquerytometa- reasoningexpertise,theinferenceengineselectsan appropriatemethodorproblemsolver,andderives Figure 2 A high-level, conceptual view of the framework architecture Reasoner Interpreter Observer Inference Connectedthings Actuation Training set Domain expert User Generated knowledge Meta- reasoning expertise Model transformation rules Cross-domain concepts Reusable states and transitions Use-case-specific states and transitions Domain-specific concepts Symbolic data representations Raw data streams Explanation What-if analysis Property check Strategy Domain and reasoning expertise Mission/goal Planning Verification . . . Simulation Machine learning Knowledgebase Approver
  5. 5. THE IoT AND COGNITIVE AUTOMATION ✱ 5APRIL 6, 2017 ✱ ERICSSON TECHNOLOGY REVIEW relevantinputforitusingmodeltransformationrules. Theresultproducedbytheselectedproblemsolver canbepresentedtotheuser,senttotheinterpreter foractuation,orfedbackintotheinferenceengineto reinforcetheknowledgebasecontent. Agoalquerycanbeinitiatedbytheuseror theinferenceengineitselfbasedonacomparison betweenexpectedandactualsystemstates. Forinstance,ifthegoalqueryistoverifya certainsystemproperty(safety,forexample),the inferenceenginewilllookintheknowledgebase anddeducethataverificationmethodshould beusedtocheckthatthesystemsatisfiesthe requiredproperty.Similarly,ifthegoalqueryis toreachacertaingoal,theinferenceenginewill lookuptheknowledgebaseanddeducethata plannershouldbeusedtogenerateastrategy toreachthatgoal.Sincemostoftheplanners acceptPlanningDomainDefinitionLanguage (PDDL)[2]astheirinput,theinferenceengine looksupcorrespondingmodeltransformation rulestotranslatetheprobleminPDDL.The interpretertakesthegeneratedstrategyand transformsitintoactuationinstructionsforthe connectedthings.Whenexecutingthestrategy, theinterpreterworkscloselywiththereasoner. Intheeventofanychangesintheexpectedstate ofthesystem,thereasonersendsareplanning requesttotheplanner.Thegeneratedstrategy canbepresentedtotheuserforapprovalbefore actuation,ifrequired. Thereasonercanperformautomaticknowledge acquisitionusingmachinelearningtechniques thatmayextractinsights(suchascategorization, relationsandweights)fromtrainingsetsinthe formofdocumentation,databases,conversation transcriptionsorimages.Thereasonercanexplain thereasonsbehindrecommendingandperforming agivensetofactions,tracebacktotheoriginsofthe decisions,andinformtheuser(andothersystems, ifsodesired)aboutactionsplannedforthefuture. Theexplanationcanbemadeinauser-friendly mannerbyapplyingnaturallanguageprocessing, augmentedrealityandautomaticspeechsynthesis andrecognition,forexample. Applicationexamples Ourresearchandexperienceshowthat thereareawidevarietyofwaysinwhichour frameworkcanbeusedtoboostefficiencyin differenttypesofapplications.TestAutomation asaService(TAaaS),automatedschedulingof trainlogisticsservices,smartmetering,ticket booksystemsandEverythingasaService (XaaS)arejustafewexamples. TAaaS OurTAaaSprojectaddressedtheproduct developmentlifecycleandsoftware-testing activities.Testingforproductstypicallyinvolves numeroustoolsfortestdesign,testexecutionand testreporting,alongwithmanagementsoftware tools,puttogetherinatoolchain.Toolchain configurationisanexpensiveprocessthatoften takesonetotwodaystoprepare,dependingonthe productbeingtested.Reconfiguringatoolchain totestanotherproductisanevenmoretime- consumingactivity.TheTAaaSsystemautomated theconfigurationofthesetoolchainsbasedonuser requirementsandcreatedvirtualworkspacesin adatacenter,eliminatingtheneedforexpensive, manualconfiguration.Inthiscase,theframework containedaknowledgebaseofsoftwaretoolsand theirdependencies. THE REASONER CAN PERFORM AUTOMATIC KNOWLEDGE ACQUISITION USING MACHINE LEARNING TECHNIQUES SUCH AS CATEGORIZATION, RELATIONS AND WEIGHTS
  6. 6. ✱ THE IoT AND COGNITIVE AUTOMATION 6 ERICSSON TECHNOLOGY REVIEW ✱   APRIL 6, 2017 Automatedschedulingoftrainlogisticsservices Inthisproject,wecreatedaconceptforafully automatedlogisticstransportationsystem. Thesystemconsistedofseveralactors(suchas trains,loadingcranesandrailroadinfrastructure elements)equippedwithsensorsandactuators, andcoordinatedbyintelligentsoftware.Themain goalwastoinvestigatethepotentialbenefitsof combiningfactualandbehavioralknowledgeto raisethelevelofabstractioninuserinteraction.A user-specified,high-levelobjectivesuchas“deliver cargoAtopointBintimeC”isautomatically brokendowntosubgoalsbytheintelligent managementsystem,whichthencreatesand executesanoptimalstrategytofulfillthatobjective throughitsabilitytocontrolcorresponding connecteddevices. Smartmetering Weusedourframeworktocreateanintelligent assistanttohelpEricsson’sfieldengineers manageEstonia’snetworkofconnectedsmart electricitymeters.Itstaskwastoautomate troubleshootingandrepairproceduresbyutilizing knowledgecapturedfromthedomainexperts. Byseparatingknowledgefromthecontrollogic, wemadethesystemextendablesothatnew knowledgecanbeaddedtoitovertime.The representationcomprisesseveraldecisiontrees andworkflowsthatencodetherootcausesof variouspotentialtechnicalissues,processesfor diagnosingandidentifyingthem,andthestepsin thecorrespondingrepairprocedure.Ourproject revealedthatinthistypeofusecase,thebulkofthe workisinthemanualacquisitionandencodingof expertknowledge. Ticketbook Whenmanagingandoperatingmobilenetworks, supportengineersnormallycreatetickets(issue descriptions)forproblemsclassifiedasnon- trivial.Basedontheassumptionthatthesolution toanissuesimilartoanotherthathaspreviously beendealtwithwillbesimilar,wecreateda systemforsemanticindexingandsearching historicaltickets.Weusedavectorspacemodel tocalculateasimilarityscoreandsortedsearched ticketsaccordingtotheirrelevancetothecurrent case.Asimilartechniquewasusedtofind technicaldocumentationrelevanttothecase currentlybeingsolved.Thisusecasereliesonthe availabilityofhistoricaldata(previoustickets) toautomaticallybuildaknowledgebasethat facilitatessubsequentinformationretrievaland relevancyranking. XaaS InourXaaSproject,wedevelopedanMI-assisted platformforservicelifecyclemanagement.By leveragingtechnologiessuchassemanticweb andautomatedplanning,wepresentedhow consumerservicescanbedeliveredautomatically usingunderlyingmodularcomponentsknown asmicroservicesthatcanbereusedacross differentapplicationdomains.Thisautomation providesflexibilityinservicedeploymentand decommissioning,andreducesdeploymenttime frommonthsandweekstominutesandseconds. Theknowledgebasecomprisedaspecification ofthedomainusedforservicerequirement formulation,predefinedservicedefinitions,the metadataoftheservicefunctionalcomponents, andthedescriptionsofthemicroservicesthathad beenusedtoimplementthosecomponents. CONSUMER SERVICES CAN BE DELIVERED AUTOMATICALLY USING UNDERLYING MODULAR COMPONENTS KNOWN AS MICROSERVICES THAT CAN BE REUSED ACROSS DIFFERENT APPLICATION DOMAINS
  7. 7. THE IoT AND COGNITIVE AUTOMATION ✱ 7APRIL 6, 2017 ✱ ERICSSON TECHNOLOGY REVIEW Prototypeimplementation–transportation planning Theautomationofatransportationplanning processisanexcellentexampleofhowourcognitive automationframeworkcanbeusedtocreatean intelligentDSS.Inthisexample,theDSSisusedto automaticallydeterminehowtotransportpassengers orcargoatminimalcost(takingintoaccountthe distancetraveled,thetimeneededtotransport eachpassengerandpieceofcargo,orthenumberof busesrequiredfortransportation)usingconnected vehicles(busesortrucksrespectively).Theanswer foraparticulartaskcouldbeaplanthatconsists ofasequenceofstepsforthesystemtoperformto reachthegoalstate.Ifthesystemdeterminesthat thetaskcannotbeperformed,theanswerfromthe framework-basedDSScouldbethereasonwhy, aswellasapossiblesolutionsuchasincreasingthe numberofvehicles.Themotivationbehindusing theframeworkforthisparticularusecasewasto demonstratethedecreaseinthecostofdesignby buildingandreusingcross-domainknowledge specificallybetweenpeopleandcargologistics. Figure3providesanoverviewofthepartially implementedprototypeimplementationof ourframework-basedDSSfortransportation planning.(Workisstillongoingtoimplement missingcomponentsconnectingtheframeworkto theenvironment.)Theknowledgebasecontains modeltransformationrulesforPDDLandstates andtransitionmodelsthatarebasedonaset ofontologiescontaininginformationforthe particulartransportationdomain.Theontologies areorganizedinmultiplelayersofabstraction:one Reasoner Interpreter Observer Inference PDDLgenerator Connectedvehicles& passengermobilephones Actuation Symbolic data representations use case data + domain ontology (CPSs, ITSs) + transformation rules Knowledge input: Logistics task API Legend: Transport plan output: vehicle schedules Raw data streams PDDL transformation rules CPSs ontology States and transition library for transport logistics ITSs ontology Meta- reasoning expertise People logistics use case Goods logistics use case Planning PDDLplanner Component under development Implemented component Component in place – open source software Figure 3 Overview of partially implemented framework- based DSS for transportation planning
  8. 8. ✱ THE IoT AND COGNITIVE AUTOMATION 8 ERICSSON TECHNOLOGY REVIEW ✱   APRIL 6, 2017 whichiscross-domain(thecyber-physicalsystems ontologyinFigure3);andonewhichisspecificto intelligenttransportationsystems(ITSs),usedfor reasoningabouttransportationproblems. Thisimplementationassumesthattheinference enginehasalreadydeducedthataplannershould beusedtosolvethetransportationplanning problemusingmeta-reasoningexpertise(fromthe knowledgebase)andauser-specifiedgoal.Theother implementedcomponentisthePDDLgenerator, which–giventhePDDLtransformationrules,states andtransitionfilesasinput–generatesfilesthatare understandableforaPDDLplanner.Thesource codeoftheimplementationisavailableonline[3]. Asalways,thereisadirectrelationshipbetween theamountofknowledgetobeformalizedand theeffortrequiredtoformalizeit.Putsimply,this meansthatthemorenon-formalizedknowledge thereis,thehighertheoperationalcostswill beintermsoftime,humanresourceallocation andmoney.Inthecaseofatransportschedule generationprocess,thebenefitofusingthe frameworktolowerthecostofdesignisthat itreducestheeffortrequiredtoformalizethe knowledgeneededforthesystemtobeautomated. Figure4showsthedifferenttypesofknowledge usedtogenerateatransportationplanfortwouse cases:transportationofpassengersinbusesand transportationofcargointrucks.Thetransport networkisviewedasagraph,withpointsof interest(POIs)asverticesandPOI-connecting roadsasedges.The“transportableentities”canbe passengersorcargo,dependingontheusecase. Transitionsaresimilarregardlessoftheroute, numberandtypeoftransportagents(busesor trucks)andtransportableentities.Thismeans thatreusingtransitionsacrossdifferenttransport planningusecases,andstoringthemaspartofthe “transitionlibrary”(seeFigure3),canreducethe costofdesign. Figure 4 Knowledge for transport plan generation DOMAIN KNOWLEDGE Route Transport agents Transportable entities Initial state Transitions Goal state DESCRIPTION Specification of route(s) as graph(s), which includes vertices, edges and edge-traversal costs (such as travel time and fuel spent). Number of vehicles, vehicle IDs and vehicle capacity. Number and ID of transportable entities (passengers, cargo). The starting location of the transport agents and transportable entities. The transitions that transport agents can perform. Current prototype implementation contains three transitions: pickup, drop, and move-to-next-coordinate. Which criteria need to remain constant for the transport service to complete the specified route (usually this means that all transportable entities are picked up and dropped off at specific points along the route).
  9. 9. THE IoT AND COGNITIVE AUTOMATION ✱ 9APRIL 6, 2017 ✱ ERICSSON TECHNOLOGY REVIEW Weuseasimplemetriccalledthereusability indextomeasurereductionsinthecostofdesign. Thereusabilityindexistheratioofreusedentities versustotalentitiesintheknowledgeinput availabletothePDDLgeneratortogeneratethe plan.Theratiowas0.364forthebuscaseand0.251 forthetruckusecase.Thismeansthatoutofthe totalnumberofentitiescreatedforeachcase,36.4 percentwerealreadyavailableinthelibraryforthe buscaseand25.1percentforthetruckcase.The contrastbetweenthetwocanmainlybeattributed tothedifferenceoftheroutespecificationentities, asagentsonbothroutesfolloweddifferentpaths. Astheknowledgebasebecomesmorepopulated, thereusabilityindexwillincrease,whichwillin turnleadtoadecreaseinthedevelopmenttimeof furthercustomITSsolutions.Ourmeasurements alsoindicatethatthereusabilityindexcanriseifthe routespecificationispartofthePDDLgenerator, whichcanautomaticallygeneratethespecification usingdatafromamappingserviceinconjunction witharoutinglibrary.Theknowledgeengineer couldthenonlyspecifythedesiredwaypoints(bus stops,forexample),andtheroutesandgraphswould begeneratedautomaticallybythePDDLgenerator. Conclusion IoT-basedsystemsrequirealargenumberof decisionstobemadeinashorttime,whichin turnrequiresautomation–notonlyintermsof infrastructuremanagement,butalsowithinthe logicoftheIoTapplicationsthemselves.ADSSis anessentialtoolinthiscontext,owingtoitsability toenhancehumandecision-makingprocesseswith MIbasedonbehavioralmodelsandinformation containedintherelevantdatastreams. Anysystemthatrequiresautomationinthe decision-supportprocesscouldbeenhanced byourcognitiveautomationframework.Ithasa domain-agnostic,fixedarchitectureinwhichthe onlyvariablecomponentsaretheknowledgebase andthesetofreasoningmethods.Byseparating cross-domainknowledgefromuse-case-specific knowledge,theframeworkmakesitpossibleto maximizereuse,enablingsubstantialsavings intermsofdesigncost,andshorteningtimeto marketforcustom-developedsolutions.Theset ofreasoningmethodsisextensibleandcanbe populatedwiththemethodsandtoolsthatare bestsuitedtospecifictasks.Theframework’s self-adaptationissupportedbymeta-reasoning andcontinuousreinforcementoftheinternal knowledgeovertime.Inthefuture,weplanto extendtheframeworkwithanalysisofhypothetical situationsandanintuitiveuserinterfaceforboth expertsandlessexperiencedusers. Terms and abbreviations API – application programming interface | CPS – cyber-physical system | DSS – decision support system | ETL – extract, transform, load | IoT – Internet of Things | ITS – intelligent transportation system | MI – machine intelligence | OWL – Web Ontology Language | PDDL – Planning Domain Definition Language | POI – point of interest | RDF – Resource Description Framework | TAaaS – Test Automation as a Service | XaaS – Everything as a Service THE FRAMEWORK’S SELF-ADAPTATION IS SUPPORTED BY META- REASONING AND CONTINUOUS REINFORCEMENT OF THE INTERNAL KNOWLEDGE OVER TIME
  10. 10. ✱ THE IoT AND COGNITIVE AUTOMATION 10 ERICSSON TECHNOLOGY REVIEW ✱   APRIL 6, 2017 Aneta Vulgarakis Feljan ◆ has been a senior researcher in the area of MI at Ericsson Research in Sweden since 2014. Her Ph.D. in computer science from Mälardalen University in Västerås, Sweden, focused on component- based modeling and formal analysis of real-time embedded systems. Feljan has coauthored more than 30 refereed publications on software engineering topics, and served as an organizer and reviewer of many journals, conferences and workshops. Athanasios Karapantelakis ◆ joined Ericsson in 2007 and currently works as a senior research engineer in the area of MI at Ericsson Research in Sweden. He holds an M.Sc. and Licentiate of Engineering in communication systems from KTH Royal Institute of Technology in Stockholm, Sweden. His background is in software engineering. Leonid Mokrushin ◆ is a senior researcher in the area of MI at Ericsson Research in Sweden. His current focus is on creating and prototyping innovative concepts for telco and industrial use cases. He joined Ericsson in 2007 after starting postgraduate studies at Uppsala University focused on the modeling and analysis of real-time systems. He holds an M.Sc. in software engineering from St. Petersburg State Polytechnical University in Russia. Rafia Inam ◆ has been a researcher at Ericsson Research in Sweden since 2015. Her research interests include 5G cellular networks, service modeling and virtualization of resources, reusability of real-time software and ITS. She received her Ph.D. from Mälardalen University in Västerås, Sweden, in 2014. Her paper, Towards automated service-oriented lifecycle management for 5G networks, won her the best paper award at the IEEE’s 9th International Workshop on Service Oriented Cyber-Physical Systems in Converging Networked Environments (SOCNE) in 2015. Elena Fersman ◆ is a research leader in the area of MI at Ericsson Research and an adjunct professor in CPSs at the KTH Royal Institute of Technology in Stockholm, Sweden. She received a Ph.D. in computer science from Uppsala University, Sweden, in 2003. Her current research interests are in the areas of modeling, analysis and management of software- intensive intelligent systems applied to 5G and industry and society. theauthors
  11. 11. THE IoT AND COGNITIVE AUTOMATION ✱ 11APRIL 6, 2017 ✱ ERICSSON TECHNOLOGY REVIEW 1. PascalHitzler,MarkusKrötzsch,BijanParsia,PeterF.Patel-SchneiderandSebastianRudolph(December 2012)OWL2WebOntologyLanguagePrimer(SecondEdition).W3CRecommendation,availableat: https://www.w3.org/TR/owl2-primer 2. DrewMcDermott,MalikGhallab,AdeleHowe,CraigKnoblock,AshwinRam,ManuelaVeloso,DanielWeldand DavidWilkins(1998)PDDL–ThePlanningDomainDefinitionLanguage.TechReportCVCTR-98-003/DCSTR- 1165,YaleCenterforComputationalVisionandControl,availableat:http://homepages.inf.ed.ac.uk/mfourman/ tools/propplan/pddl.pdf 3. PDDLGeneratorforTransportationLogisticsScenarios,basedonCPSandITSontologies,availableat: https://github.com/SSCIPaperSubmitter/ssciPDDLPlanner References Further reading 〉〉 KMARF: A Framework for Knowledge Management and Automated Reasoning presented at a Development aspects of Intelligent Adaptive Systems (DIAS) workshop (February 2017), available at: https://arxiv.org/abs/1701.03000 〉〉 The Networked Society will not work without an automation framework, Ericsson Networked Society Blog (July 2015), available at: https://www.ericsson.com/thinkingahead/the-networked-society-blog/2015/07/09/the- networked-society-will-not-work-without-an-automation-framework/ Carlos R. B. Azevedo ◆ joined Ericsson Research’s Brazilian team in 2015. He is a researcher in the area of MI whose work is devoted to supporting and automating sequential decision processes by anticipating and resolving conflicts. He holds a Ph.D. in electrical engineering from the University of Campinas in Brazil, and his doctoral thesis was awarded the Brazilian Ministry of Education’s 2014 Thesis National Prize in Computation and Automation Engineering. Klaus Raizer ◆ is a researcher in the area of MI at Ericsson Research in Brazil. He joined the company in 2015. Raizer holds a Ph.D. in electrical and computer engineering from the University of Campinas in Brazil, where he is a founding member of the IEEE Computational Intelligence Chapter. His research interests include computational intelligence, machine learning, cognitive architectures, CPSs, robotics and automation. Ricardo S. Souza ◆ joined Ericsson Research in Brazil in 2016, where he is a researcher in the area of MI. He holds an M.Sc. in electrical and computer engineering from the University of Campinas in Brazil, and he is currently finalizing his Ph.D. at the same institution. His research interests include distributed systems, networked and cloud robotics, shared control and computational intelligence. theauthors
  12. 12. ✱ THE IoT AND COGNITIVE AUTOMATION 12 ERICSSON TECHNOLOGY REVIEW ✱   APRIL 6, 2017 ISSN 0014-0171 284 23-3299 | Uen © Ericsson AB 2017 Ericsson SE-164 83 Stockholm, Sweden Phone: +46 10 719 0000

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