Research Article KD-ACP: A Software Framework for Social Computing in Emergency Management

28 Pages·2015·9.6 MB·English

Hindawi Publishing Corporation
Mathematical Problems in Engineering
Volume 2015, Article ID 915429, 27 pages
http://dx.doi.org/10.1155/2015/915429
Research Article
KD-ACP: A Software Framework for Social Computing in
Emergency Management
BinChen,LaobingZhang,GangGuo,andXiaogangQiu
ResearchCenterofComputationalExperimentsandParallelSystemTechnology,CollegeofInformationSystemandManagement,
NationalUniversityofDefenseTechnology,Changsha410073,China
CorrespondenceshouldbeaddressedtoBinChen;[email protected]
Received4June2014;Accepted23August2014
AcademicEditor:PraveenAgarwal
Copyright©2015BinChenetal.ThisisanopenaccessarticledistributedundertheCreativeCommonsAttributionLicense,which
permitsunrestricteduse,distribution,andreproductioninanymedium,providedtheoriginalworkisproperlycited.
Thispaperaddressestheapplicationofacomputationaltheoryandrelatedtechniquesforstudyingemergencymanagementinsocial
computing.WeproposeanovelsoftwareframeworkcalledKD-ACP.Theframeworkprovidesasystematicandautomaticplatform
forscientiststostudytheemergencymanagementproblemsinthreeaspects:modellingthesocietyinemergencyscenarioasthe
artificialsociety;investigatingtheemergencymanagementproblemsbytherepeatcomputationalexperiments;parallelexecution
betweenartificialsocietyandtheactualsocietymanagedbythedecisionsfromcomputationalexperiments.Thesoftwareframework
iscomposedofaseriesoftools.Thesetoolsarecategorizedintothreepartscorrespondingto“A,”“C,”and“P,”respectively.Using
H1N1epidemicinBeijingcityasthecasestudy,themodellinganddatageneratingofBeijingcity,experimentswithsettingsofH1N1,
andinterventionmeasuresandparallelexecutionbysituationtoolareimplementedbyKD-ACP.Theresultsoutputbythesoftware
frameworkshowsthattheemergencyresponsedecisionscanbetestedtofindamoreoptimalonethroughthecomputational
experiments.Intheend,theadvantagesoftheKD-ACPandthefutureworkaresummarizedintheconclusion.
1.Introduction individual behavior, communications in agents, and evolu-
tion rules of agent organizations. It is worth notifying that
Emergency management attracts the attention of scientists themodellingofagentdoesnotemphasizetheintelligenceof
from social computing because the whole process of emer- individual.Largescale,communicationsandtheemergence
gent events is deeply coupled with human society and the phenomena are the objects of agent-based modelling and
emergency response decisions need an approach to testify simulation.TheagentorientedplatformssuchasBiowar[3],
their effect without the reappearance of emergent events in GASM [4], and EpiSims [5] to study emergency problems
thesociety.Asanewparadigmofcomputingandtechnology have been proposed in many fields. Biowar developed by
development,socialcomputinghelpsscientiststounderstand Carnegie Mellon University is used to study the bioattacks
and analyze individual and organizational behavior and in city with the ability of scalable agent modelling. GASM
facilitateemergencymanagementresearchandapplicationin (Global-Scale Agent Model) by Epstein simulates a global
manyaspects[1]. H1N1 epidemic with 6.5 billion people. EpiSims from Los
Based on the fruitful development of computational Alamosnationallabisusedtotestifytheinterventionmea-
methodology on emergency management research over the sures in epidemics of smallpox from United States Depart-
lastdecade,lotsofworkhasbeendonetosolvetheproblems mentofHealthandHumanServices.
insocietydomain.Boththeconceptualframeworksinmul- With the help of agent-based modelling, simulation
tiple discipline and the technological platforms developed techniqueandtheconceptofartificialsociety[6],anovelcon-
for the domain requirements are more and more popular ceptualframeworkbasedonartificialsystemsisintroduced
in the research on emergency management, especially the in the social computing. The conceptual framework called
agent-based modelling and simulation [2]. The bottom-up ACP(ArtificialSociety,ComputationalExperiments,Parallel
technique describes the society in microview by modelling Execution)approachisproposedbyWangin2004[7–9].Itis
2 MathematicalProblemsinEngineering
anovelapproachinsocialcomputingtosolvetheproblems ofemergencyparameters,thesettingsoflargesamplesexper-
in society domain. ACP approach is categorized into three iments,andtheparallelexecutionwithlooseconnectionof
aspects: representing and modelling society with artificial realsocietyshouldbeconsideredinsidethesoftwareframe-
systems, analysis and evaluation by computational experi- work.
ments,andcontrolandmanagementofrealsocietybyparallel As a result, the purpose of this paper is to propose
execution.Undertheinstructionofconceptualframeworkof a software framework called KD-ACP applying the ACP-
ACPapproach,awidespectrumofcomplexsystems,suchas basedcomputationaltheoryandcorrespondingmethodsin
transportation,medicine, finance, and environment,can be studyingemergencymanagementproblems.KDisshortfor
studied in the computational manner. Actually, many real- the Chinese phonetic alphabets of China National Univer-
worldapplicationsusingACPapproachhavebeendeveloped sity of Defense Technology. KD-ACP means the software
tosolvetherealproblemsindomains.Forinstance,complex framework is developed by National University of Defense
socioeconomic system [10] and the research framework for TechnologytoimplementACPapproach.Theremainderof
e-commerce system [11] are the good applications of ACP thispaperisorganizedasfollows.Section2summarizesthe
approach in the economic area. The ACP-based frame- existing agent-based modelling and simulation platforms.
work for integrative medicine [12] is proposed to solve the Section3 introduces ACP approach first and proposes the
problems in medicine. An overall framework of emergency KD-ACP platform. Section4 illustrates the modelling of
rescue decision support system of petrochemical plant [13] BeijingcitywithKD-ACP;boththeagentmodelsandinitial
is proposed based on ACP theory to study environment dataareconsidered.Section5showshowtodoexperiments
riskaccidentsofpetrochemicalplant.ParallelBRToperation withKD-ACPusingtheH1N1casestudyinartificialBeijing.
management system [14] based on ACP approach has been Intheend,thepaperisconcludedinSection6.
constructedtodetectthequantityofpassengersonstations
real-time, traffic flow on stations or at intersections, and 2.RelatedWorks
queuinglengthofvehiclesontheroad.Anovelparallelsys-
temforUrbanRailTransportation(URT)[15]basedonACP Therehavebeenmanyeffortsonsocialcomputing,especially
approach is proposed to address issues on safety efficiency ontheemergencymanagement.Agent-basedmodellingand
and reliability of the operation of URT. An artificial power simulation are popular in the implementation of social
system [16] is set up on the models of power systems and computation.Therelatedworksaremainlycategorizedinto
complex power grids to provide a feasible approach for the SWARM-like agent-based modelling and simulation plat-
controlandmanagementofthemodernpowersystem. forms and agent-based platforms for emergency manage-
Althoughalotofworkhasbeendoneontheconceptsand ment.
theoryframeworktostudyproblemsbysocialcomputing,the
following problems of computational experiments in emer- 2.1. SWARM-Like Agent-Based Modelling and Simulation
gencymanagementarestillnotsolvedfromtheperspectiveof Platforms. SWARM [19] originally proposed by Santa Fe
modellingforsocialsystemstheoryandsoftwareframework institute is widely used in many research areas such as
ofplatformimplementation. biology,ecology,andsociety.Thetoolprovidesasimulation
environmentforsimulatingagentwiththesupportofaseries
(i)The modelling and simulation of emergency man-
ofclasslibraries.ItisworthnotingthatSWARMisthepre-
agement are not given the special consideration.
cursorofmultiagentsimulationtool;itinfluenceslotsofmul-
The representationofsociety focuses on thegeneric
tiagent simulation platforms such as NetLogo [20], RePast
modelling of agent (represented by Repast [17, 18]).
(REursive Porus Agent Simulation Toolkit), MASON [21],
The description of environments is too simple to
andSOARS(SpotOrientedAgentRoleSimulator)[22].
meet the requirements from research on emergency
NetLogo is a multiagent programming language and
problems,suchasthebuildingsize,theplacerelated
modelling environment for simulating natural and social
agentcontactfrequency.
phenomena.Itisparticularlywellsuitedformodellingcom-
(ii)Theexistingtoolsandplatformscannotsupportthe plexsystemsevolvingovertime.Thelanguageiseasytostudy
design of experiment. Computational experiments andtheagent-basedcomplexsystemscouldbebuiltrapidly;
cannotbedonesystematicallyandautomatically. RePast is a software framework for agent-based simulation
createdattheUniversityofChicago.Anextensiblesimulation
(iii)The existing applications of ACP-based frameworks
packagemakesRePastbecomeagenericmultiagentsimula-
are still domain specific. A generic workflow and
tionplatforminsocialscienceresearchcomputing;MASON
integrated toolkit are needed to implement ACP
designedbyGeorgeMasonUniversityisusedtoserveasthe
approach,especiallyintheapplicationofemergency
basisforawiderangeofmultiagentsimulationtasksranging
management.
fromswarmroboticstomachinelearningtosocialcomplex-
Therefore,itisnecessarytodevelopanACP-basedsoftware ityenvironments.Thetoolisafastdiscrete-eventmultiagent
frameworkfortheresearchonemergencymanagement.The simulation toolkit in Java; SOARS is designed by Tokyo
artificialsystemistheprojectionofrealworldintheemergent InstituteofTechnologytodescribeagentactivitiesunderthe
scenario.Themodellingofthesystemincludingtheemergent rolesofsocialandorganizationalstructure.Decompositionof
eventsmodellingandinterventionmeasuresmodelling,the multiagentinteractionisthemostimportantcharacteristics
designofcomputationalexperimentsconsideringthesettings inthisframework.
MathematicalProblemsinEngineering 3
All the SWARM-like agent-based modelling and simu-
Actual societies Artificial societies
lation platforms provide a portable, lightweight, and easily
extensible environment for simulating agents in arbitrary
researchareas.However,theheterogeneityinspecificsocial
Testing Testing
computingdomainisnotconsidered.Furthermore,mostof Operation
theplatformscannotwellsupportlargescaleagentsimulation
Management Experimentation Training and
becauseofthelightweightengine.Theenginecannotafford and control and evaluation learning
thesimulationofsupercitieslikeBeijingandNewYorkwhich
havemillionsofpeople. Figure1:TheparallelexecutionofACPapproach[8,33].
2.2. Agent-Based Platforms for Emergency Management.
Biowar proposed by Carnegie Mellon University simulates (ii)Utilizeinnovativecomputingtechnologiestoevaluate
the impact of background diseases, bioterrorism attacks and analyze various factors in emergency manage-
within a city. 62 diseases are modeled in this platform to ment quantitatively; the computers are regarded as
simulatetheoutbreaksonthepopulation’sbehavior.GASM theexperimentalsociallaboratoriesforinvestigating
(Global-ScaleAgentModel)isdesignedtostudythespread- emergencymanagementproblems.
ing of H1N1; 6.5 billion population is modeled with the
(iii)Provide an effective mechanism for the control and
supportofofficialstatisticaldata.AglobalH1N1spreadfrom
managementofcomplexactualsocialsocietythrough
Tokyo is simulated in GASM. EpiSimS proposed by Los
comparison, evaluation, and interaction with artifi-
Alamos lab simulates the spread of disease in regions such
cialsociety.
as cities, allowing for the assessment of disease prevention,
intervention, and response strategies. The daily movements
It is worth notifying that “P” here is not the “parallel” in
and interactions of synthetic individuals are represented
“parallelsimulation”buttherepresentationof“parallelexe-
explicitly.BurkeandEpsteinproposeacomputationalmodel
cution.”Theideaofparallelexecutionistobuildtheparallel
of smallpox epidemic transmission and control [23]. The
scenarios by paralleling the actual societies and artificial
agentsinthismodelinteractlocallywithoneanotherinsocial
societies.Consequently,parallelcontrolandmanagementof
unitssuchashomes,workplaces,schools,andhospitals.
actualsocietiesareimplementedwiththehelpofinteractions
However,theseplatformscannotprovideagenericsoft-
between parallel scenarios. The goal of parallel execution
wareframeworktostudyemergencyproblems.Biowaronly
is to find the best plans to adjust the methods of control
focusesonsocialnetworks;individualsareallmodeledasthe
and management based on the comparison and analysis of
nodesofsocialnetworks.AgentsinGASMandEpisimSare
differences between actual and artificial societies. Artificial
isomorphic,withoutconsideringtheheterogeneityinspecific
societiesprovidepossiblesimulatedresultsofevolutionsby
domains.
repeated computational experiments. The simulated results
Tosumup,thissectionbrieflyreviewstheexistingmul-
provideevidencesfortheadjustmentplans.Theseplansare
tiagent simulation platforms including SWARM-like agent-
usedinthecontrolandmanagementofactualsocieties,such
based modelling and simulation platforms and agent-based
as emergency management. After the application of these
platforms for emergency management. However, they can-
plans,theobservationsfromactualsocietiesarecollectedfor
not satisfy the requirements of simulation performance,
thecomparisonwithexpectation.Thedifferencesareusedto
adaptability of software framework, and heterogeneity of
feedbacktoartificialsocieties.Thenewturnofcomparison
individualsinresearchofdifferentemergencyscenarios.
andanalysistofindbestadjustmentsofcontrolandmanage-
mentisrepeated.
3.KD-ACP Themechanismof“parallelexecution”hasbeenproved
tobeeffectiveforuseinnetworkedcomplextrafficsystems
KD-ACPisanintegratedsoftwareframeworkdesignedand andiscloselyrelatedtoemergingtechnologiesincloudcom-
implemented based on the principle of the ACP approach puting,socialcomputing,andcyber-physical-socialsystems
showninFigure1. [24].Inordertopromotethedevelopmentofparallelcontrol
and management in emergency management, the artificial
societyisproposedinACPapproachwhichistheexpansion
3.1.TheACPApproach. ACPapproachisasocialcomputing-
of“artificialtrafficsystems.”
basedresearchparadigm.Itiscomposedofthreecomponents
Instructed by the ACP approach, KD-ACP is also com-
as its name: artificial society for A, computational experi-
posed by three components. The details of the architecture
ments for C, and parallel execution for P. The basic idea of
andimplementationofKD-ACParediscussedbelow.
ACPapproachislistedasfollows.
(i)Modelthecomplexsocietiesinvolvinghumanbehav- 3.2. The Software Architecture of KD-ACP. The architecture
iorandsocialorganizationsasartificialsocietiesusing ofKD-ACPisshowninFigure2;thesoftwareframeworkis
multiagent modelling techniques in a “bottom-up” composed of a series of tools. These tools are grouped into
fashion.Artificialsocietiesareregardedasaresearch threepartstosupportartificialsocietymodelling,computa-
platformtostudyemergencymanagement. tionalexperiments,andparallelexecution.
4 MathematicalProblemsinEngineering
Generic Modeling
A part Environment (GME)
FSM based models
Population and Geospatial
Environment Generation Tool (PGET) Model Development
Tool (MDT)
Initialize
Artificial society population and C++ code of models
geospatial environment database /dll of models
C part
Artificial Society Scenario of
Editor (ASE) artificial society Agent and emergency Experiments Design Experiment plans
Tool (EDT)
model repository
Initialize Initialize
Initialize
Emergent Events
Settings of
Configuration Tool (EECT) emergent events
Intervention Measures Experiments Management
Configuration Tool (IMCT) Settings of intervention measures Tool (EMT)
/emergency response plans
Artificial society
Population data, emergent events runtime database
data, and intervention measures
y Artificial Society
Open source data ciet Situation Tool (ASST)
o
Registration Tool (OsdRT) ual s Oprtiemspaol nesme eprlgaenncy P part
ct
A
Internet
Figure2:ThesoftwarearchitectureofKD-ACP.
Inthe“A”part,GenericModelingEnvironment(GME) emergencydecisionorganizations.Partsoftheinfluencesof
[25] and Model Development Tool (MDT) are the kernel emergencyplansarereflectedonInternet.Opensourcedata
tools in the modelling of artificial society. GME is an RegistrationTool(OsdRT)isusedtoregistertheopensource
opensourcemodellingtoolwhichsupportsdomain-specific datafromInternettoartificialsociety.
modelling. The domains of artificial society are created by KD-ACP is developed using the Browser/Server archi-
GME in our work. Models such as agent, environment, tecture, the tools are integrated in the home page of KD-
emergent event, and intervention measure are described in ACPasshowninFigure3.Eachtoolisactivatedbytheclick
specific domains first in GME. With the help of model onthelink.Forexample, ArtificialSocietyEditorisstarted
transformation,thesemodelsarealltransformedtotheFinite when the link of ASE is clicked. The working environment
StateMachine (FSM) models. Meanwhile, code generations andprogramminglanguagesoftoolsinKD-ACParelistedin
aresupportedbyMDT,andthesemodelsareallimplemented Table1.
in C++. Artificial Society Editor (ASE) is used to describe
Moreover, the implementation of KD-ACP is mainly
the concrete scenario of actual society, which defines the
composed of modelling phase and computational experi-
scope of models set for artificial society; Population and
mentsphase.Itwillbediscussedinthenextsection.
Geospatial Environment generation Tool (PGET) generates
the population and geospatial environment data with the
supportofstatisticaldatafromactualsociety. 3.3. The Modelling of Artificial Society in KD-ACP. It is a
In the “C” part, Emergency Events Configuration Tool criticalproblemtofocusonthekeypartsofsocietyinsocial
(EECT) initializes the models of emergent events while computing. Based on the ACP approach, the bottom-up
InterventionMeasuresConfigurationTool(IMCT)initializes modelling is used to build the artificial society. As a result,
themodelsofinterventionmeasures.Experimentsplansare modellingofartificialsocietyiscomposedofthreebasicele-
generatedbyExperimentsDesignTool(EDT).Basedonthese ments:agents,environments,andrulesforinteractions.How-
plans,ExperimentsManagementTool(EMT)isusedtorun ever, we stillmeet theproblem thatspecific featuresshould
and manage the computational experiments to study the be supported in artificial society. For example, emergent
emergencyproblems. events and intervention measures are required in artificial
In the “P” part, Artificial Society Situation Tool (ASST) society for emergency management. The modelling of only
seemed as the monitor of running artificial society. The basicelementscannotcoverthespecificfeaturesindomains.
statisticaldataandsituationareshownbyASSTatruntime. Therefore, domain-specific modelling [26] is introduced to
Inthemeantime,theemergencyresponseplansaremadeby solvetheproblemsinmodellingartificialsociety.
MathematicalProblemsinEngineering 5
Generic Modeling Emergent Events
Environment (GME) Artificial Computational Parallel Configuration Tool (EECT)
society (A) experiments (c) execution (P)
Model Development
Intervention Measures
Tool (MDT) Configuration Tool (IMCT)
Open Source Data
Registration Tool (OsdRT)
MDT EECT IMCT
Artificial Society
Editor (ASE)
ASST
OsdRT
ASE PGET EDT EMT
Population and Geospatial Experiments Design Experiments Management Artificial Society
Environment Generation Tool (PGET) Tool (EDT) Tool (EMT) Situation Tool (ASST)
Figure3:TheimplementationofKD-ACP.
Table1:Theworkingenvironmentandprogramminglanguagesoftools.
Programming Development
Tools Workingenvironment Usertype
languages platform
GenericModeling Generalcomputer(desktop
NULL NULL Domainexperts
Environment(GME) application)
ModelDevelopmentTool Generalcomputer(desktop
C++ VisualStudio Modeldevelopers
(MDT) application)
ArtificialSocietyEditor Generalcomputer(desktop
C# VisualStudio Domainexperts
(ASE) application)
PopulationandGeospatial
Generalcomputer(desktop
Environmentgeneration C# VisualStudio Domainexperts
application)
Tool
Generalcomputer(with
EmergencyEvents
InternetExplorer,client C# ASP.NET Domainexperts
ConfigurationTool(EECT)
side)
Generalcomputer(with
InterventionMeasures
InternetExplorer,client C# ASP.NET Domainexperts
ConfigurationTool(IMCT)
side)
Generalcomputer(with
ExperimentsManagement
InternetExplorer,client C# ASP.NET
Tool(EMT)
side)
Nodesinsupercomputer
Usersofcomputation
ServerofEMT (ConsoleProgram,server C++ VisualStudio
experiment
side)
Nodesinsupercomputer
RuntimeInfrastructureof
(ConsoleProgram,server C++ VisualStudio
EMT
side)
ArtificialSocietySituation Generalcomputer(desktop
C++ VisualStudio Domainexperts
Tool(ASST) application)
Generalcomputer(with
OpenSourceData
InternetExplorer,client Java JSP Domainexperts
RegistrationTool(OsdRT)
side)
6 MathematicalProblemsinEngineering
GME: Generic Modeling Environment
Metamodeling
Metamodeling for artificial society
Domain-specific metamodels
Semantic well defined
metamodels
Agent Environment
M2M transformation
metamodel metamodel
FSM DAE DEVS
Emergency event Intervention
metamodel metamodel Petri Net State charts
Modeling in domain Instance of
Modeling of artificial society Semantic well defined models
Domain-specific models FSM based agent models
Agent Environment
model model M2M transformation FSM based environment
models
Emergency event
Intervention FSM based emergent and
model model intervention models
M2T transformation
MDT: Model Development Tool
Implementation
Domain-specific models relevant codes
Agent model code Environment model code Disease model code Intervention model code
Basic population model Basic environment Disease spreading model Vaccination
information model
Agent behavior model Infectors isolation
Roadnet model Disease state transform
Agent mentality model model Contactors isolation
Agent social network Agent contact model Close work offices
model
Figure4:FrommetamodellingandmodellingbyGMEtoimplementationbyMDTofartificialsociety.
3.3.1. The Principle of the Modelling of Artificial Society. in Figure4, agent metamodel, environment metamodel,
According to the principle of domain-specific modelling, emergent event metamodel, and intervention metamodel
the modelling of artificial society contains the following compose the metamodel of artificial society. The second is
steps: first, metamodelling the basic elements of artificial the construction of the metamodels described by typical
society;second,modellingthespecificfeaturesindomainof modelling formalisms such as FSM, DAE, DEVS, and Petri
emergencymanagement;third,implementingthemodelsof Net[27].Theseformalismsareallsemanticallywelldefined.
artificialsocietyincodes.Thewholeprocessisillustratedin The third is the definition of the model transformation
Figure4.ThefirstandsecondstepsareimplementedinGME fromdomain-specificmetamodelstometamodelsoftypical
whilethethirdstepisimplementedinMDT. modelling formalisms. The transformation standardizes the
Thefirststepismetamodelling,whichmainlyfocuseson metamodels of artificial society by typical modelling spec-
constructingthemetamodelsofartificialsociety.Metamod- ifications. The fourth is the definition of the transforma-
ellingtriestostudythecommonpatternsofartificialsociety. tion templates from metamodels to code framework. The
Theoutputsofmetamodellingaremetamodels,whichrepre- templateslistthebasicabstractinterfacesofmetamodelsof
sentstheabstractionofthewholesystem.Thebasicelements artificialsociety.Theseabstractinterfacesareimplementedin
ofartificialsocietyaredescribedinmetamodel.Theprocess thespecific-domainmodellingandcodegenerations.
ofmetamodellingisdividedintofourphases.Thefirstisthe The second step is modelling; the models of artificial
constructionofthedomain-specificmetamodels.Asshown societysuch asagent model,environmentmodel,emergent
MathematicalProblemsinEngineering 7
Figure5:ThemetamodelsofartificialsocietyinGME.
event model, and intervention model are built. Actually, includesboththeemergenteventmetamodelandinterven-
the models are the instantiation of metamodels in the last tionmetamodel.
step.Differentfromthegeneralmodellingenvironmentlike Fromtheperspectiveofmodelling,thedetailsfromspe-
UML[28],thedomain-specificmodellingprovidesafamiliar cificdomainsareconsideredinthemodelsbytheinstantia-
modelling environment for the domain experts in artificial tionfrommetamodelsofartificialsociety.Forexample,social
society.Forexample,emergencyresponseexpertsonlycon- relationshipsbasedoncomplexnetworksareaddedinagent
cernemergenteventmodelandinterventionmodelinherited modeltosupportthecommunications.Agentactivityisalso
from metamodels. After constructing the domain-specific usedtoquantifytheagent activityunderdifferentscenario.
modelsbasedondomain-specificmetamodels,domainusers Environmentmodelsarelinkedwiththehelpoftransporta-
execute the model transformation defined in the first step. tionservices;subwaysandroadsaremodeledwhilethepath
AllthemodelsofartificialsocietyaretransformedintoFSM searchisencapsulatedintheservices.Emergenteventmodel
models.Asaresult,themodelsareimplementedinthisuni- andinterventionmodelarealsothedomain-specificmodels.
fiedmodellingformalism(FSM).Themodeltransformation ThemodellingofartificialBeijinginGMEwillbediscussed
makesthesimulationofthemodelspossible. indetailinnextsection.
The third step is the generation of executable codes of
models. The executable code framework is generated by 3.3.3. The Implementation of Models of Artificial Society by
mapping template from metamodels to code framework MDT. As mentioned before, MDT is used to implement
defined in the first step. Moreover, domain developers also models such as agent, environment, emergent event, and
add necessary codes to the framework to integrate the intervention.Accordingtothetemplateofcodeframework,
dynamic semantics of the models. The code framework the implementations of models are generated by MDT. The
outputsthedynamiclinklibrariesbycompiling.Thedynamic implementations are classified into two categories: FSM
link libraries are loaded in the large scale artificial society models and services. FSM models such as agents and
runtimeinfrastructureincomputationalexperiments. environments are built under the specification of Finite
State Machine (FSM) [29] in MDT, while all the services
such as transportation are encapsulated under the Public
3.3.2. The Metamodelling and Modelling of Artificial Society
ServiceStandard.Thisstandardprovidesagenericinterface
by GME. GME is used to build metamodels and models
specification for modelers to encapsulate public common
in our work. As mentioned before, the abstraction and
servicesinartificialsociety.FSMmodelslikeagentarebuilt
commonpatternsofsocietyarerepresentedinmetamodels.
statistically from the quantitatively analyzed characteristics,
Accordingtothebottom-upmodellingstyle,metamodelsof
suchasdemographicattributes,socialbehaviors,emergency
agent, environment, and communications are described in
behaviors,andsocialnetworks.Socialbehaviorsdescribethe
GME.Figure5showspartofmetamodelsofartificialsociety.
daily behaviors of individuals while emergency behaviors
The features of an agent metamodel are extracted from the describe the individual behaviors in emergent events. For
censusfiguresandstatisticaldata.Environmentmetamodel example, infected individuals are all isolated in hospital in
simulates the geospatial places for the behaviors of agents. SARS.Isolationismodeledasatypicalemergencybehaviorin
Themetamodelofcommunicationsamongagentsismodeled ourwork.Correspondingly,servicesareusedtosimulatethe
to simulate the interactions such as infection in epidemics macroactualsociety.Taketransportationserviceforinstance;
andrumorpropagationinpublicopinionformationevents. the path search is needed by almost every agent during
Itisworthnotifyingthatthemetamodelofcommunications movingfromspottospot.
8 MathematicalProblemsinEngineering
Artificial society description in emergency Statistical data of real society
Agent role set Geospatial statistical data
Agent status set
Population statistical data
Description Agent behavior set Data
of artificial Agent relationship set Social relationship statistical data collection
society Environment set
Environment statistical data
framework
Artificial
+ Society Editor Geospatial and Social Actual
(ASE) Population behavior Environment Generation society
Emergent event set statistical data
Tool (GSeT)
Agent status in emergency
Emergency statistical data
Emergency management
Description of
emergency Emergency organization statistical data
Agent model repository and
population database
Agent model and
service repository Artificial society
+emergencymodel Mapping population and
Terminal of agent modeling and geospatial database
repository
artificial society describing
RSetCalatuitsimsotJStioDEmneocebmxasamt,ho,li a eAallimdymoiprgAzgge+ce omeeoaeldgnidtndoneicoe ldteyagnnelgilnetMn gmtooddeBeleihlnaIERDvSgnimeaootseirecplr yrimovgameneoln·sn doret·cieedoyl·lnealtiinognship EmEbmbemoehmedrehageorvaelgdiivBneoneiuncorglyicrlydimbbnmeSeSgohohodoacacedvivilaeaiiiolonllrrg odel Development Tool (MDT) CmCsleliirommvCdiacatee+etle+ GGeeoseogmrrgvaiprochaeCdipceah+lli+cEanlInctsaeerprvvesincuteiolnaTteranTmrsapsnoeosrdpCvroietcr+aeltatt+iioonn OOrgrmsgaeanrnoviiizdczCaeeatilt+oino+n Public service standard
Encapsulate M
Agent modeling Public service modeling
Figure6:TheeditingandinitializationofKD-ACP.
MDTprovidesdomainexpertswithatooltoobtainthe events, the emergency organization, and emergency related
code implementations of models. With the help of compile behaviorsofagents.
environment like visual studio, MDT also supports the According to the requirements of the editing, these
further programming development of the specific domain statisticaldataarecollectedfromactualsocietymanuallyby
detailswhichcannotbedescribedinmodellingstep. the domain experts. Based on these statistical data, PGET
BoththeFSMmodelsandservicesaredevelopedbythe generates the artificial society population and geospatial
MDT first and then stored in the agent model and service environment database. The database supports the instanti-
repository.Therepositorymanagesthemodelsaccordingto ation of artificial society at individual level. For example,
therequirementsfromemergencyproblemsandprovidesthe the attributes such as age and gender of each agent can be
modelsforEDTtomaketheexperimentplans. found in the database. With the support of the database,
FSM models, and service repository discussed before, it
3.4. The Editing and Initialization of Artificial Society by is sufficient for domain experts to study the emergency
ASE and PGET. As shown in Figure6, ASE is used to edit problemsbycomputationalexperiments.
the scenarios of artificial society within emergent events.
The editing is composed of two parts: (1) the statistical 3.5.TheComputationalExperimentsandParallelExecutionin
informationofartificialsocietyindailylife,suchastheroles KD-ACP. Thetoolsof“C”partand“P”partinKD-ACPare
ofagents,therelationshipsofagents,andthetypesofenviron- used to support the process of computational experiments
ments,and(2)thestatisticalinformationofartificialsociety and parallel execution. The working process is shown in
in emergency, including the statistical data of emergent Figure7;EECTandIMCTareboththestartingandending
MathematicalProblemsinEngineering 9
∙Input: models of emergency
e∙vOenuttsput: settings of agent Aprotpifiucliaatli osno cainetdy Load s∙oIcnipeutyt:+ deasrctirfiipctiaiolnso ocfi eatryti fipocipaullation
Emergency Events geospatial and geospatial environment society
CTooonlfi (gEuErCatTio)n b+esheatvtiinogr sino fe emmeergrgeennt te evveenntsts environment database i+nteermveerngteionnt emveeanstusrceosn cfiognufirgautiroantio+n
Experiments ∙Output: experimentplan+artificial
design tool (EDT) societymodels+artificialsociety
∙Input: models of intervention initial data file
measures
∙Output: settings of agent Load
Intervention Measures behavior in intervention
Configuration measures+settingsof
Tool (IMCT) intervention measures Output Output Output
Load Load
Agent model and Input
service repository
+emergencymodel Artificial society Artificial society Experiment
repository models initial data file plan
Output
Output
Load Load
∙Input: data from internet
networks
∙Output: populationdata+
emergency eventsdata+ Deploy ∙Input: experimentplan+
intervention measures data TIANHE-1A artificial societymodels+
Open Source Data super computer Experiments Management artificial society initial data file
Registration Tool (OsdRT) Deploy Tool(EMT)+ ∙Output: artificial society
Emergency large scale artificial society runtime state data
response plans runtime infrastructure
Cluster
Runtime infrastructure
Internet
Output
The most optimal
emergency Output ∙∙OInuptuptu: tr:u gnrtaipmhei csst acthisatritcsa lo dfata
response plan artificial society statisticaldata+ Load
Artificial society
situation of artificial society
Emergency decision runtime database
Artificial Society
Actual society organization Situation Tool (ASST)
Figure7:ThecomputationalexperimentsandparallelexecutionofKD-ACP.
point. The emergent events and intervention measures are DefenseTechnology(NUDT)inChinain2010.Accordingto
configured by EECT and IMCT, respectively. The configu- the plan, the experiments are done repeatedly on the large
rations of emergent events are used to simulate both the scale artificial society runtime infrastructure [31, 32] by the
real emergencies like SARS and H1N1 and the supposed multisamplesettings.Theworkprocessistheimplementation
emergencies for experiments. Similarly, the configurations ofcomputationalexperimentsinACPapproach.
of intervention measures are also used to reproduce the Traditionally, emergency response plans are made by
real one and simulate the supposed one. The repeat of the emergencymanagementtheoriesandexperiences.Theonly
emergency is used to verify the models while the supposed waytotesttheeffectiveofplansisthefeedbackresultsofreal
configurationsareusedtoobtaintheoptimizeddecisionplan world. ACP approach provides a novel method to support
totheresponseofthepossibleemergencies. emergency response plan making by parallel execution. As
With the input of artificial society model and service showninFigure7,theworkprocessofKD-ACPiscomposed
repository, artificial society population and geospatial envi- of two loops. The inner loop composed by red arrows
ronment database, and the configurations discussed before, describestheprocessofcomputationalexperimentswhilethe
EDT generates the experiment plans to meet the require- outerloopofyellowarrowsillustratestheprocessofparallel
ments of research on emergency management. The output execution. During the runtime of computational experi-
of EDT includes artificial society models, artificial society ments,thestatisticaldataofartificialsocietyiscollectedand
initial data files, and experiment plan. The models are storedintheartificialsocietyruntimedatabase.Basedonthe
downloaded from the repository while the data file is the database,ASSToutputsthecustomizedsituationofrunning
collectionofdatafromthedatabasetoinitializethemodels. artificial society by graphics charts and situation maps.
When the models and data files are ready, EMT loads the The information is sent to the organizations of emergency
experiment plan and deploys the models and data to the decision to support making the emergency response plans.
cluster or TIANHE-1A supercomputer [30] which was the Withthehelpofcomputationalexperimentsloop,theseplans
world’sfastestsupercomputerbuiltbyNationalUniversityof aresimulatedrepeatedlytofindthemostoptimalone.
10 MathematicalProblemsinEngineering
Emergent Events
Configuration Tool (EECT)
Internet
Actual society
Intervention Measures
Open source information Configuration Tool (IMCT)
in social media Knowledge
Data acquisition Data extraction Data standardization
∙Web information collecting ∙Basic element extracting ∙Domain knowledge description
∙Individuals and organizations ∙Information fusion and collision
∙Deep mining in social media extraction detection
∙Web information denoising ∙Sentiment analyzing and ∙Domain knowledge construction
∙Information filtering o∙pSionciioanl nmeitnwionrgk analyzing ∙Social media information
standardization
Open Source Data Registration Tool (OsdRT)
Figure8:ThecompositionsofOsdRT[34].
Moreover,themostoptimalplanisusedtotheresponse models based on the knowledge of their own. Meanwhile,
of emergency in actual society. According to the idea of GMEsupportshierarchyforbuildinglargescalesystems.The
parallel control in [24], the feedback of actual society is syntaxsymbollistedinGUIcanbeextendedinnewtabby
partlycollectedfromInternetnetworksbyOsdRT.Asshown doubleclicking.Takeagentforexample;themodelofagent
in Figure8, OsdRT is composed of three components: data canbedetailedbyeditioninanothertabpageofagent.
acquisition, data extraction, and data standardization. Data As shown in the center of Figure10, the models of
acquisition collects, mines, and filters information from artificial Beijing consist of five parts: models of agent and
social sensing networks. Data extraction includes basic environment,domainmodelsofpublichealthevents,inter-
element extracting, individual and organization extracting, ventionmodels,controllermodels,andservices.Agentmodel
sentiment analyzing, and social networks analyzing. Data describes individuals in society; it is composed of basic
standardizationspecifiestheusefulknowledgeandsentitto populationinformation,action,socialrelationships,activity
configurationtoolsinKD-ACP. schedule,anddiseaserelatedinformation.Activityschedule
ByprocessinginOsdRT,theknowledgeaboutemergent representsindividual’sphysicalactionmodel,focusingonthe
events and intervention measures are analyzed first and dailyactionofagents.Environmentmodelincludesphysical
registeredintheEECTandIMCT.Theregistrationupdates entities such as buildings, playground, transportations, and
the settings of emergent events and intervention measures. agentscontainedinenvironment.Domainmodelsofpublic
Thisloopcomposedofyellowarrowsimplementstheparallel healtheventsarecomposedofthepropagationmodelofdis-
executioninACPapproach.Theimplementationofparallel ease,diseasestatetransitionmodel,andsoon.Intervention
control and management provides a data-driven approach modelsincludethesettingsofvaccination,isolation,andso
that considers both the engineering and social complexity on. The models mentioned before are all FSM models. The
for modelling, analysis, and decision making in emergency mechanismsofthesemodelswillbedetailedinnextsections.
management. Controllermodelsandservicesarethepublicservicemod-
ules;theyareimplementedinthedevelopmentinMDT.
4.ModellingBeijingCitywithKD-ACP
4.1.2.ModellingAgentsandEnvironments. Underthespeci-
4.1. How to Build the Artificial Beijing. According to the fication of FSM, agent and environment models are imple-
modelling of artificial society in KD-ACP discussed before, mented in two parts: the state space and state transitions.
theBeijingcityismodeledasfollows. The state space is composed of the demographic attributes
To meet the requirements of emergency managements, andbehaviorrelatedattributes.Thetransitionsaretriggered
the basic elements of artificial society are extended. As when the conditions of states are satisfied. As shown in
showninFigure9,sixelementsarerequiredtosimulatethe Figure11(a), the action of agent is changed when the “next
city:agents,environments,transportation,activityschedule, time” condition is satisfied in agent model while the agents
communication,andagentactivity. listischangedwhentheagentarrivalconditionissatisfiedin
theenvironmentmodel.
4.1.1. Modelling Artificial Beijing. Figure10 shows the main
GUI of GME for the modelling of artificial modelling in 4.1.3. Modelling Activities. Agent activities come from the
publichealthevents.Metamodelsarelistedintheleftareaof agent state transitions of actions such as movements and
Figure10;thelistprovidesbasicsyntaxelementsfordomain communications.Theactionsofagentsareinstructedbythe
experts to model artificial society. Domain experts build activity schedule shown in Table2. Activity schedule lists