IntelligentSystems
TDDD10AIProgramming
Introduction
Warehousetransport Robotictruckconvoy Automaticparking
CyrilleBerger
Dronedeliverysystem Longdistancedelivery Robotvacuumcleaner
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Intelligentsystems
Soccerteams(RoboCup)
RescueRobots(RoboCup)
Fastrobots(SICKChallengex)
AIProgrammingcourse
MultiAgentRescue
Simulation(RoboCup)
All-terrainnavigation(TechXchallenge)
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AIProgramming
Coursegoal
Programmingofintelligentautonomous
systems
ImplementationandintegrationofAI
techniquesintoworkingsystems
Focusonreal-worldenvironmentsusing
anagent-orientedparadigm
Gainexperiencewithsomeofthe
difficultieswhenbuildingreallarge-scale
AIsystems
Giveanintroductiontoagent-orientedAI
programmingthroughtheuseofasearch
andrescuesimulator(RoboCup).
Introducesfundamentalproblemsand
techniquesrelatedtoconstructingagentorientedAI-systems.
Gainpracticalexperienceinsolving
theseproblemsandimplementing,
integrating,andevaluatingthese
techniques.
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Courseorganization
Divisionoftime
Listoflectures
AIProgramming:
IntroductiontoRoboRescue
3 AgentsandAgents
4 Communication
5 MultiagentDecision
6 CooperationAndCoordination
7 CooperationAndCoordination
8 Machine
9 Knowledge
10 PuttingItAll
1
2
20hlectures(in10
60hlabs(in15
12hseminars(in4
68h
Workrepartition
Labsinagroupofthreefora
Implementationofamultiagentsystemsina
groupof8-10
StudyandimplementationofanAI
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Listoflabs
1
2
3
4
5
6
RoboRescueSimulator
PracticalIntroductiontoRoboCup
RescueSimulation
ImplementingefficientPathPlanning
CommunicationandContract
NetProtocol
Exploration
Task
MachineLearning
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Labs
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Deadlinesanddeliverables
Registeryourgroup(3studentsper
group)inWebReg:https://
www.ida.liu.se/webreg/
Followlabinstructionsat:https://
www.ida.liu.se/~TDDD10/info/
labs.en.shtml
Onceyouaredonewithalab:
Commitinsubversion
Presentyourresultstothelab
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October,10th:labsare
October,29th:individualassignmentproject
isselected
November,19thor/and26th(preliminary):
presentationofindividualprojectduringa
December,15th(preliminary):presentationof
theteam
December,15th(preliminary):draftofreports
aresubmited
January,6th:finalteamandindividual
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Courseexamination
Requirements
Thesimulatorisnotrunningverywell.
Goodnews:wegotnewlab
Badnews:someoftheroomsarestillequipedwiththeold
Sinceitisjava,theyshouldrunonWindowsandOSX,instructions
tocomelater
TheAPIisbigandconfusing
newlecture(on
Ittakestoomuchtimetogetresults
livewithit,intherealworlditcantakedaysbeforeyougeta
butinthenewlecture,wewillseehowtocreatesimple
Ihavespenttoomuchtimeontheindividualassignmentanddid
nothavetimeforthegroupassignment,andvice-versa
Individualassignmentispartofthegroupassignment.
Labs1to4
Individualreport
Groupreport
Grade
grade=0.75*individual_grade
+0.25*group_grade
+0.5*lab_5_done
+0.5*lab_6_done
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CourseMaterial
AnIntroductionto
MultiAgentSystems
byMichael
Wooldridge,ISBN:
9780470519462
MultiagentSystemsby
GerhardWeiss,ISBN
978-0-262-01889-0
andthelectures
slides(eventually)
Courseimprovements
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TeachingPhilosophy
Iamnotperfect,Idon'tknoweverything.Iam,
alas,human.
Icannotteachyouanythingbyforce.However,I
cangiveyouaframeworkforyourownlearning
experiencebysettingboundariesanddefining
requirements.
Withoutyourfeedbacknoimprovement.
Learningisafunadventure!
Thecourseisgoingtobewhatyouwant
tomakeofit.Participate,beactiveand
involved.
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WhatareMulti-AgentSystems?
Multi-AgentSystems
AnMAScanbedefinedasalooselycouplednetworkof
problemsolversthatinteracttosolveproblemsthatare
beyondtheindividualcapabilitiesorknowledgeofeach
problemsolver(DurfeeandLesser1989)
Theseproblemsolvers,oftencalled
agents,areautonomousandcanbe
heterogeneousinnature.
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CharactericsofMulti-AgentSystems
1
2
3
4
Eachagenthasincomplete
informationorcapabilitiesfor
solvingtheproblemand,thus,has
alimitedviewpoint
Thereisnosystemglobalcontrol
Dataisdecentralized
Computationisasynchronous
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Whatcantheydobetter?
TosolveProblemsthataretoolargefor
acentralizedagentwithlimited
distributedcomputing
Toreducetheriskoffailureofacentralized
Disastermitigation/UrbanSearchAndRescue
Tokeeplegacysystemsinter-connectableand
inter-operational
Migrationofoutdatedsoftware
Tosolveproblemsthatcannaturallyberegarded
associetiesofautonomouscomponents
Air-trafficcontrol,Meetingscheduling,etc.
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ObjectOrientedProgrammingvsMulti-AgentSystems
Object-OrientedProgramming
Objectsarepassive,
i.e.anobjecthasno
controlovermethod
invocation
Objectsaredesigned
foracommongoal
Typicallyintegrated
intoasinglethread
ApplicationofMulti-AgentSystems(1/5):
ComputerGames
Multi-AgentSystems
Agentsare
autonomous,i.e.
pro-active
Agentscanhave
diverginggoals,e.g.
comingfrom
different
Agentshave
ownthreadof
Objectsdoitforfree;agentsdoitformoney.(Jenningsetal.1998)
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RealTimeStrategy(e.g.Starcraft,AgeofEmpires):TaskAssignmentandMultiAgentPathPlanning
FirstPersonShooter(e.g.HalfLife2,SplinterCell):CharacterInteractions,Team
Formation,LimitedSensing,PathPlanning,etc...
Simulations(e.g.TheSims):CharacterInteractions,UtilityMaximization
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ApplicationofMulti-AgentSystems(2/5):
LogisticandTransport
ApplicationofMulti-AgentSystems(3/5):
Industry
Supplychain
management
B2B,Logistics
FactoryAnd
Warehouse
Taskassignment,coalitionformation,
Management
pathplanning
Project
coalitionformation
problem,standardized
communications,
auctions
Airtrafficcontrol
distributedsensing,
auctions,...
Teamof100decentralized“elements”
toaccomplishautonomously
transportationtasks
Features:
Automaticloadandunloadat
assemblychains
Automaticbatteryrechargingviathe
ground
Mechanismtocouplewithstationsor
othervehicles
Challenges:
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Navigationandcoordinationof
decentralizedteams
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ApplicationofMulti-AgentSystems(4/5):
SpaceExploration
ApplicationofMulti-AgentSystems(5/5):
Rescue
UrbanSearchAnd
Rescue
SpaceMissionswithmultiple
rovers
Earthorbiters
Decentralizedsensing,taskassignment,
coalitionformation...
distributedsensors
unmannedvehicles
firstresponder
management
Marsnetwork
Decentralizedsensing,task
assignment,coalitionformation,
pathplanning
Spaceshiprepair
...3Dpathplanning,andmanymore
challenges...
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EmergencyServicesAssistance
Multi-AgentSystemsfor
EmergencyServicesAssistance
Adevastatingearthquakeofhighmagnitudeoccurredon
December,26,2004offthewestcoastofSumatra,Indonesia.The
resultingTsunamikilledthousandsofpeopleinsouthernIndia,
Indonesia,Thailand,etc.
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Fukushima
Tsunamisintheworld
11March2011,thenuclearfacilityat
Fukushimaisdestroyedbya
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Thegolden72hours
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MotivationforusingMulti-AgentSystems
Robotscanaccessplaceshumans
cannot
smallopeningsandconfinedspacesunder
thefloor
hazardousplaces
Mapping
Qualityofdisasterresponsestronglydepends
oninformation,suchasmapswithvictim
locations
TomHaus(firemenat9/11):Weneeda
trackingsystemthattellsuswhereweare,
wherewehavebeen,andwherewehavetogo
to
Efficientcoordinationofvictim
Mixedinitiativeteamsofhumansandrobots
search
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SearchandRelief
Searchingforinjuredpeopleanddeliveringfood,
medicineandothersuppliesarehighlyprioritized
activitiesindisasterrelief.
UASTechUAVPlatforms
LinkQuadweight~1kg,diameter~70cm
YamahaRMAXweight95kg,length3.6m
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SelectedAutonomousFunctionalities
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FindingInjuredPeople:Mission
Assumptions
Optimalflightaltitude:
Averageflightvelocity:5m/s
Humanbodysize:20-50pixels
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Context
11livebodies/2
2UAVsforscanning
290x185
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FindingInjuredPeople:Results
DeliverFoodandSupplies:Mission
SaliencyMap
ThermalandColorImages
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Failtoattach
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ACooperativeMissionScenario
Ifthingscangowrongtheywillgowrong.
->needtomonitorexecution
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SomefamousLandmarkProjects
RoboCup:
aLandmarkProject
forMulti-AgentSystems
TheApolloProgram:fromWrightFlyerin1903tothefirstmanonthemoonin1969
ComputerChess:fromEniacin1946toDeepBluein1997
TheRoboCupProject
Thevision:By2050,builda
teamoffullyautonomous
humanoidswhichwinsagainst
thehumanworld-champion
undertheofficialregulationof
Since1997annualcompetitions
FIFA.
andworkshops,since2001
RoboCupRescue
Aplatformforproject-oriented
educationinscienceand
AstandardproblemforAIand
technology
robotics
Technologytransfer
AlandmarkProject:challenging
goalandspill-overoftechnologies
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TheRoboCupProject
ComputerChessvsRoboCup
Feature
Chess
Environment Static
RoboCup
Dynamic
Worldaccessibility Completeinformation Incompleteinformation
Percepts
Symbolic Non-symbolic
Execution Turn-based Real-time
ActioneffectsDeterministic Stochastic
Agents
Central
Distributed
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ThelandmarkofRoboCupRescue
RescueRobotCompetition
Bytheyear2050,enablelarge-scaleMulti-AgentSupport
fordisastermitigation
SensorNetworks
IntegrationofSensors
distributedinthecity
Humanrescuepersonnel
DigitallyEmpoweredbywearable
computers
SharedGISKnowledgebas ee.g.
GoogleMaps fors haringmis s ioncriticaldata
Simulatornetwork
e.g.FireGrid,RRSim
EmergencyResponseCenter:
EfficientMASdecisionmaking
Roughterrainnavigation
RobotTeamsReconnaissanceExplorationofinaccessibleplaces
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RescueRobotCompetition
Signsoflife:form,motion,heat,sound,
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RescueVirtualCompetition
BasedontheUnrealgame
engine(UT2004,EpicGames)
RealisticmodelsofUSAR
environments,robots(Pioneer2
DX,SonyAIBO),andsensors
(LaserRangeFinder,Color
Camera,IMU,WheelOdometry)
Multipleheterogeneousagents
canbeplacedinthesimulation
environment
Highfidelitysimulationofupto
12robots
AgentsconnectviaaTCP/IP
interface
Simulationofwireless
communication
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RescueAgentCompetion
RescueAgentCompetition
Simulatorsfor
earthquake,fire,
civiliansandtraffic
Thetaskistodevelop
softwareagentswith
differentroles:
Thedomainmodelsalargeandcooperativemulti-agent
problem(#Agents>50)
Theenvironmentispartiallyobservable,agentshavetoact
rationallygiventhehistoryoftheirlocalpercepts
Thedomainisstochastic,effectsoffirefightingandrescue
mightvary
Theenvironmentissequential,i.e.continuouslyprogressing
Thedomainisdynamic,e.g.firesandcollapsingbuildings
Theworldisacontinuousandratherbig
Agentsareheterogeneoussincetheyhavedifferent
capabilities
Thedomainisdecentralizedduetoalimitedcommunication
bandwidth
thatmakeroads
passable(police)
extinguishthefires
(firebrigades)
rescueall
civilians
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Summary
TolearnaboutMulti-AgentSystems
frombooksonlyisdifficult
Thereexistsnoultimatestrategyoralgorithm(maybe
inthefuture)
However,challengeswithindifferentdomainsarevery
similar
ForlearningaboutMulti-AgentSystemswehavetoget
intouchwiththem!
RoboCupRescueoffersarichsetof
ArtificialIntelligencerelatedproblems:Lets
solvethem!
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