Lectures
TDDD10AIProgramming
PuttingItAllTogether
CyrilleBerger
1AIProgramming:Introduction
2Introductionto
3AgentsandAgents
4Multi-Agentand
5Multi-AgentDecision
6CooperationAndCoordination
7CooperationAndCoordination
8MachineLearning
9AutomatedPlanning
10PuttingItAllTogether
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Lecturegoals
LecturesSummary
Learnhowallthealgorithmsand
conceptsfittogether
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Lecturecontent
AgentArchitecture
CommunicationRelay
Trafficsurveillance
Sensing
AgentArchitecture
Stream-BasedReasoning
DetectingandTrackingcars
Multi-agentsensing
Rescueoperation
HumanDetectionand
Planning
Delegation
BuildingScanning
Masterthesis
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ArchitectureTheory
UAVArchitecture
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CommunicationRelay
Communicationisakeycomponent
ofmulti-agentsystems
Communicationcanbe
CommunicationRelay
Communicationnetworkscanbe
disabledordestroyed
Forinstance,during9/11,cellularphoneswerenotworking
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CommunicationRelay:visibility
CommunicationRelay:multiplepath
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Networkbuildingwithdelegation
CommunicationRelay
ThegoalistofindachainofUAVthatminimizethenumberof
Thisisacomplicatedoptimization
Getworseifyouhavemultiple
UseVoronoigraphandTreebased
Network
buildingmission
Sequence
Deploy
basestation
Sequence
Flyto
Thisworkin2D,butwhatabout
Grab
Deploy
relaystations
Concurrent
Flyto
Grab
Deploy
relaystation1
Sequence
...
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Howtogetfullcoverage?
Findinganoptimalcoveragein3D
isunreasonable
Instead,whenanagentisinan
areawithlowcommunication,it
shouldsendarequestforgetting
arelaystationinstalled
Trafficsurveillance
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TrafficsurveillanceScenario(1/2)
TrafficsurveillanceScenario(2/2)
Continuouslygatherinformationfrom
manydifferentsources.
Selecttherelevantinformationforthe
currenttask.
Derivehigher-levelknowledgeaboutthe
environmentandtheUAV.
Suchasdetectingmisbehavingdrivers
Correctlyinterpretwhatisgoingon.
CoordinationofUAVswithother
CoordinationofUAVswithother
humanpoliceforces(calledmix
initiative)
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Sensing
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Stream-BasedReasoning
TheSense-ReasoningGap
Stream-BasedReasoning
Autonomoussystemsproduceand
processsequencesofvalues
incrementallycreatedatrun-time.
Thesesequencesarenaturalto
modelasstreams.
Stream-basedreasoningis
incrementalreasoningover
streams.
Stream-basedreasoningcaptures
thecontinuousreasoningwith
minimallatencynecessaryto
reacttorapidchangesinthe
environment.
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IncrementalEvaluationofTemporalLogicalFormulas
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StateStreams
Thesemanticsoftheseformulasisdefined
overinfinitestatesequences.Progression
isonetechniquetocheckwhetherthe
currentprefixissufficienttodeterminethe
truthvalueofaformula.
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TrafficMonitoring
DetectingandTrackingcars
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Anchoring(1/3)
Anchoring(2/3)
Theobjectiveoftheanchoringprocessisto
connectsymbolstosensordataoriginatingin
thephysicalworldsothatthesymbolsrepresent
theobjectsintheworld
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Anchoring(3/3)
ChronicleRecognition
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MultiUAVTrafficMonitoring
Multi-agentsensing
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DyKnowFederation
Cooperation
whathappenwhenanhelicopterneedto
leaveitspatrolareatogoinpursuitof
anoffender?
Either,needtofindareplacementforpatrollinghisarea
Oranotherhelicoptertopursuetheoffender
Surveillance
Sequence
Surveillance
Sequence
Pursuit
Sequence
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RescueoperationScenario(1/2)
Rescueoperation
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RescueoperationScenario(2/2)
Explorationtofindallvictims
Divisionofareaintoscanningarea
Taskallocation
Detectionofvictims
HumanDetectionandApplication
Rescueof
Taskallocation
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VictimsDetection
Leashing
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HowtoconstructaTask-SpecificationTree?
Planning
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Naturaldisasterexample
PlantoTask-SpecificationTree
Anautomatedplannertakesa
problemdescription
Example:Supposetherehasbeen
anaturaldisaster
Objectives
Availableresources,actions,…
Objective:100peopleshouldhavefood,
medicineandwater
WehaveasmallfleetofUAVsavailable
Howtodescribethistoaplanner?
Howtogeneratea
…andgeneratesaplanthatachieves
theobjectives
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Naturaldisasterexample:GeneralWorldDescription
High-leveldescriptionoftheworld
weoperatein
Naturaldisasterexample:ProblemDescription
Thecurrentstateoftheworld
Locationsofinjuredpeople,
availabilityofcrates,…
Entities:UAVs,crates,people,…
Properties:Entitieshavelocations,UAVshave
capabilities,…
Agoaltobeachieved
(and(forall?person(has-crate?personfood))
(has-crateperson2medicine)(has-crate
person7medicine)…)
Actionsthatcanbeperformed
(:operator(deliver-crate?uav?crate?from?to)
(:precond(and(has-capability?uavcarry-crates)
(at?uav?from)(at?crate?from)…)
(:phase:duration(flight-time?from?to)
:effects(:assign(location?crate)?to)
(:assign(location?uav)?to)…)…)
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PlanningtoTask-SpecificationTree
Resultingplan
Theresultingplansshouldbeabletoexpress:
Concurrency:sequentialplannersarenotapplicable
Precedence:uav7picksupcarrier2afteruav2loadscrates
Lackofprecedence:Onlywaitforotheragentswhenyouhave
Approximatetiming:(exactdurationsareunknown)
to!
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Delegation(1/2)
Missionconsistingofaflight
action+agoaltoachieve
Delegation
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Delegation(2/2)
DistributedPlanning
Verifyexecutabilitythroughon-boardfunctionalitiesduring
planning
Motionplanner
Scheduling,resourcereasoning,constraintreasoning
Infeasibleactionimmediatebacktracking!
Useofaconstraintsolver
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DelegationExample
BuildingScanning
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BuildingScanningpart1
BuildingScanningScenario
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BuildingScanningpart2
LecturesSummary
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Masterthesis
InvariousdomainofArtificial
Intelligence
Masterthesis
Planning
KnowledgeRepresentation
MachineLearning
Robotic
Sensing
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Objectdetectionandrecognition
GuidedExploration
Lookingforvictims,usingpriorknowledgeaswellasnew
observation
Objectrecongitionusing
deeplearning
Prioritizedscanarea
Implementationwitha
groundrobot
Evaluationonaerial
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HumanRobotInterractions
Voicecommands
Poserecognition
Andmanymore...
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