Cities, AI, Design, & the Futurespatialcomplexity.blogweb.casa.ucl.ac.uk/files/... ·...

Centre for Advanced Spatial Analysis, University College LondonCentre for Advanced Spatial Analysis

Cities,AI,Design,&theFutureCanArtificialIntelligenceImproveDesignIntelligence?

MichaelBatty

http://www.spatialcomplexcity.info/http://www.casa.ucl.ac.uk/

m.batty@ucl.ac.uk@jmichaelbatty

March27th 2018

AnOutlineoftheTalk• ArtificialIntelligenceandDesignIntelligence

• BasicConceptsaboutComplexityTheory

• MakingSenseofUrbanDevelopment:KeyFactors

• RelatedConcepts:Geodesign,Networks,ABM

• DesignSolutionsasWeightedAveraging

• ActualDevelopmentusingNeuralNetworks

• ASimpleExample:AveragingbyOverlay

• Generalisation:ModellingattheVeryLocalScale

ArtificialIntelligenceandDesignIntelligenceBudhu askedmetospeakonAIandCities. Iaminwayanexpertbutletmethrowoutsomeideas

Iwillnottalkabouthowwegetholdofmassivedatasetsandsearchforunderlyingpatternbutaboutdesignintelligenceandhowthisdiffersfromartificialintelligence

Theproblemwehaveincitiesiswhatweseeisnotnecessarilywhatwewant.Inshortifweexplainhowthingsemergeandevolve– actualdevelopment – thisisusuallydifferentfromoptimal,idealdevelopment

Soinaway,AIasitisdevelopingtomakesenseofwhatweseeisnotsomethingweseeverymuchofsofar– wedoseealotofmodellingwhichinawayisakindofAI

BasicConceptsaboutComplexityTheoryThiswillbemythemethen– howwegeneratedesignintelligenceandthenhowwecanthinkofthisasartificialintelligence. Firstletmedescribesomebasicassumptions

• Citiesdevelop,grow&changefromthebottomup

• Countless ‘comparativelyuncoordinated’decisions(rationalwithintheirownframe)generate coordinationacrossmanyscales– AdamSmith’s InvisibleHand

• Thismanifestsitselfspatiallyasorderandpatternwhichissaidto‘emerge’athigherscalesfromthatwhichtheforcesthatdeterminethemoriginate.

• Formanyyearswehaveacceptedthatwemightbeabletosimulatethiskindofemergence

• Thesimplestexamplesarefractals– thedendriticpatternofstreetsincitiesthatdetermineoptimalspatialpatternsofhowcitiesareresourced,howthehierarchyofcentralplacesisorderedandsoon

• Therehasbeenplentyofthinkingaboutcitiesintheseterms.MyownworkonFractal Cities whichdatesfromthemid1980sisonestream

• Inthissense,ourmodelsembodyadegreeofintelligence–artificialtoanextentalthoughtheassumptionisthatsuchintelligenceshouldmirrorhowthesystemactuallydevelops.

• Inshortourmodelsshouldnotbeaboutartificialprocessesbutreal.Thistalkisaboutthetensionbetween real andartificial butalsobetweenorganic anddesigned.

• Buttherehasbeenverylittlethinking intermsofhowplansaremade.Wetendtothinkoftheseasbeingsomehowimposedonthecityastopdown,yetplansusuallyemergefromthebottomup

• Theclearesttheoriesofdesignreflectthisnotionthataplanissuccessivelydeveloped fromasimpleseedbyadesignerwhoworksawayatitrecursively.

• Inthissensethendesignisaboutakindofartificialintelligencebutmoreimportantaboutintelligencethatleadstobettersystems,solutions

• Infact,designoftenconflictswithAIinthatAIdoesnotnecessarilyproducebetterresultsinanysense– fortoreplicatewhatwedo,doesnotmeanthatwhatwedoisbest.SointhistalkIwillquestionAIinhelpingustodesign.

MakingSenseofUrbanDevelopment:KeyFactors• Letmereturntourbandevelopment. Thecomplexity

modelofemergencesuggeststhatmanyfactorsdeterminethepatternofurbandevelopment thatoccurs,andweneedtoknowthese

• Thereisthussomesensethatwemightbeabletoproducemodelsthatcombineaseriesofindependent variables–factors– thatcanbeusedtopredictsuchpatterns.IndeedoururbanmodelstendtoattemptthissuchasCAmodels

• Recentlydevelopments inAIsuggestthatwemightbeabletofindthepatternsthatleadtoactualdevelopment butthisisnotnecessarilythebestplan

• SoIamgoingtobeginwithshowingyouhowwecangenerateaplanwhichisbestbutwhatIwilldohereisgeneratetheplanasaprocessofgroupdecision-making–againfromthebottomupinsuchawaythattheplanemergesfromdifferentandoftenconflictingindividualplans.Inasense,mymodelwillbebasedonakindofintelligencebutnotonewhichnecessarilyleadstooneactuallyhappens

• Themodelisbasedonanoldideaofpoolingopinionsbutithassurfacedoverthe last60yearsinmanycontexts

• Iwilldevelop ithereforaverysimpleexampleandthenpointthewaytomoreinformationaboutit

• IthasaquitewelldefinedformalrepresentationbuthereIwilldeveloptheideaherevisually

RelatedConcepts:Geodesign,Networks,ABM• Geodesign:groupdecision-making:…designingforchange

cannotbeasolitaryactivity.Rather,itinevitablyisateamendeavorwithmanyparticipants(fromthedesignprofessionsandgeographicsciences)…

CarlSteinitz(2012)AFrameworkforGeodesign,ESRIPress,p.ix

• AgentsandActors:amodelofhowagentscombinetheirconflictingviewsofadesignsolutiontoaconsensus;anagentbasedmodel(ABM)

• GraphsandNetworksbutnon-spatialnetworks– socialnetworks:asocialpowerstructure

• BuildingModelsinvolvesmanyformsofintelligence

Factor1AccesstoHousing

Factor2AccesstoRetailing

Factor3AccesstoHealthCare

Factor4AccesstoEducation

DesignSolutionsasWeightedAveraging

AddingorSynthesisingPhysicalInfluences

BooleanOperations

Factor1

Factor2

Factor3

Factor4

Solution

DesignSolution?

Factor1

Factor2

Factor3

Factor4

ObservedOutcome

ActualDevelopmentusingNeuralNetworks

ASimpleExample:AveragingbyOverlayThelistoffactors:• accessibilitytoexistingurbanservices,• costsofspatialcongestion,• accessibilitytorecreationalamenities,• areasofacceptablemicro-climate,• areasofwatercatchmentandpoordrainage, institutional

constraintsimposedbygovernment,• accessibilitytoexternalurbanmarkets,• subsidenceandextensive industrialpollution,• areasofsuitabletopography,• ruralamenityareas,• historicurbanareas,and• conservationofhighqualityagriculturalquality.

Generalisation:ModellingattheVeryLocalScale• Thenetworkofrelationsbetweenfactors:actors&agents

• Theproblem– theresolutionofconflictoverachangeinuseoflandinadenseurbanarea– designmaybe,decision

• Theagentsinthemodels– actors,stakeholdersversussites/buildings

• Thewaytheagentsinteractacrossthemapsofwhattheyconsidersignificanttochangeofuse

• Thewaytheagentseffectcompromise– twoproblemswhicharedualsofoneanother– rathertechnicalbutasketchofhowwemightproceed

• Alongpreamble Iknowbutletmebeginwiththeproblemfirstandthen Iwillsketchthemodel

• Theproblemisoneofreconcilingdifferentinterestsinlanddevelopment intheheartofaworldcity:London

• ItisasclosetotheheartofthecityaspossibleforitcentresonthepostcodeEC1A1AAwhichistheoldGeneralPostOfficeandisnowadjacenttothenewLondonStockExchange(whichisalmostvirtualnow)–

• Averyhistoricareawithenormousdevelopment pressures

• It’saTOYMODELwith6agentsoractorsand8sites– letusseehowitworks

• Ofcoursetomake itrealwecanscaleitinmanyways– manyactorsmanymorebuildingsetc.andalotofdataonprocesses

Actors/Stakeholders1CityCorporation

2Residents

3HospitalNHS

4Developers

5PropertySpec

6Banks

Sites/Buildings/Locations1AldersgateComplex

2StBotolph’s

3NomuraHouse

4MiltonHouse

5Postmans’Park

6BankofAmerica

7BartsNewBuilding

8BartsOldBuilding

1 2 3 4 65 7 8

1CityCorporation

2Residents

3HospitalNHS

4Developers5PropertySpeculators6Banks

Agents0 0 1 0 0 1 0 1

0 0 0 0 0 0 0 1

0 0 1 0 0 1 0 1

Sites/Buildings

001100001011111111110100101001001000000010100100

=M(ap)

1CityCorporation

2Residents

3HospitalNHS4Developers

5PropertySpec

6Banks

1AldersgateComplex

2StBotolph’s

3NomuraHouse4MiltonHouse

5Postmans’Park

6BankofAmerica

7BartsNewBuilding

8BartsOldBuildingAgentsSites/Bu

ildings

001100001011111111110100101001001000000010100100

011111001000111101111000010000011101010000010000

222101274313245313133313011111133313

ThePrimal:Interactionsbetweenactorswrt sites

001100001011111111110100101001001000000010100100

011111001000111101111000010000011101010000010000

5142141111110100415314012133121110111111414214111011111110111111

S=MT M

TheDual:Interactionsbetweensiteswrt actors

2/82/82/81/801/82/207/204/203/101/203/202/184/185/183/181/183/181/143/143/143/141/143/14

01/51/51/51/51/51/143/143/143/141/143/14

001100001011111111110100101001001000000010100100

TheNetworkAveragingXSetofMaps

yields

ANewAveragedSetofMaps

1.000.750.250.750.250.250.950.650.350.850.350.350.940.610.220.830.220.220.930.500.210.860.210.210.800.400.200.800.200.200.930.500.210.860.210.21

AndthenweaveragethemagainusingthesamenetworkAndthisyieldsanewmap,Andsoonuntilallthedifferencesbetweentheactorswithrespecttotheirmapsareironedoutandwegetthefollowingmap

0.25 0.25 0.84 0.25 0.58 0.940.25 0.25 0.84 0.25 0.58 0.940.25 0.25 0.84 0.25 0.58 0.940.25 0.25 0.84 0.25 0.58 0.940.25 0.25 0.84 0.25 0.58 0.940.25 0.25 0.84 0.25 0.58 0.94

Wecandothisonthedualproblem,onthesitesandironoutthedifferencesbetweensiteswithrespecttotheiractors

Agents

1CityCorporation 17%

2Residents6%

3HospitalNHS17%

4Developers 23%

5PropertySpec25%

6Banks10%

Buildings

NextSteps

Realproblems– verylargenetworks,typesofconnection

Intensityordesirabilitymaps;spatialaveragingasdevelopedquitewidelyinoverlayanalysisinGIS

Rationalaveraging,simpleaveraging,weightingaveraging,dominance,andotherstrategiesofcompromiseornot;networksthatdon’tleadtosolutions

Themodelislongstanding– notnew,whatisnewisthedualprimalandtheembeddingofmapsintoit

ReferencesoverManyYearsFrench,J.R.P.(1956)AFormalTheoryofSocial

Power,PsychologicalReview,63,181-194.

Batty,M.(1971)AnApproachtoRationalDesign:Part1:TheStructureofDesignProblems,Part2:DesignProblemsasMarkovChains,ArchitecturalDesign,41,436-439,498-501

Batty,M.(1984)PlanDesignandCommitteeDecision-Making,EnvironmentandPlanningB,11,279-295.

Blondel,V.D.,Hendrickx,J.M.,Olshevsky,A.,andTsitsiklis,J.N.(2005)ConvergenceinMultiagent Coordination,Consensus,andFlocking,InProceedingsoftheJoint44thIEEEConferenceonDecisionandControl,EuropeanControlConference,Seville,Spain,December12-15,2005,

Batty,M.(2013)TheNewScienceofCities,MITPress,Cambridge,MA,inpress http://www.mitpress.mit.edu/

Thankshttp://www.spatialcomplexity.info/

http://www.complexcity.info/http://blogs.casa.ucl.ac.uk/

http://www.casa.ucl.ac.uk/

Cities, AI, Design, & the Futurespatialcomplexity.blogweb.casa.ucl.ac.uk/files/... ·...

Documents

AI: Less Artificial, More Intelligence

SMART CITIES Equipped with “Intelligence”

Artificial Intelligence (AI) AI for Earth for Earth · Artificial Intelligence (AI) AI for Earth for Earth Brant Zwiefel Managing Director, Innovation Microsoft Worldwide Industry

Is Artificial Intelligence (AI) possible?

Natural intelligence Artificial intelligence (AI) History of AI Characteristics of AI system AI tasks AI roots and applications

AI - Artificial Intelligence€¦ · Web viewAI - Artificial Intelligence ... Úvod

Artificial Intelligence (AI) Challenges and Opportunitiesbiometrics.cse.msu.edu/Presentations/AI_in_Real_Time_Final_Jan12… · Artificial Intelligence (AI) Challenges and Opportunities

CleverCity - Location Intelligence for Smart Cities

CCSB354 ARTIFICIAL INTELLIGENCE (AI)

Artificial Intelligence (AI) & Fifth Generation (5G) Networksnetworking.khu.ac.kr/html/lecture_data/2019_03_spring_future_intern… · Artificial Intelligence (AI) & Fifth Generation

Artificial Intelligence (AI) · Artificial Intelligence Era AI is everywhere now! 3 Artificial Intelligence and its applications -Lecture 1: Introduction Artificial Intelligence Era

Artificial Intelligence (AI) and Ada: Integrating AI with

Infusing Intelligence with AI & ML

What is Artificial Intelligence (AI)?

Artificial Intelligence - AI LAB

Overview of Artificial Intelligence (AI)tcolburn/cs3111/slides/ai/overview.pdf · Overview of Artificial Intelligence (AI) •What is AI? -- Four views •AI Ancient History •AI

Bayesian AI - Bayesian Artificial Intelligence ... AI Introduction - Kevin Korb.… · Bayesian AI - Bayesian Artificial Intelligence Introduction IEEE Computational Intelligence

Accelerating Urban Intelligence€¦ · 5 United Smart Cities website. 14 Accelerating urban intelligence People, business and the cities of tomorrow The Economist Intelligence Unit

Smarter cities and Artificial Intelligence

Τεχνητή Νοημοσύνη (ΤΝ) - Artificial Intelligence (AI ...cgi.di.uoa.gr/~takis/AI.pdfΤεχνητή Νοημοσύνη (ΤΝ) - Artificial Intelligence (AI) Παρελθόν,