When AI helps Wildlife Conservation: Learning Adversary Behaviors

190 Pages·2017·10.95 MB·English

WhenAIhelpsWildlifeConservation: LearningAdversaryBehaviorsin
GreenSecurityGames
by
DebarunKar
ADissertationPresentedtothe
FACULTYOFTHEGRADUATESCHOOL
UNIVERSITYOFSOUTHERNCALIFORNIA
InPartialFulfillmentofthe
RequirementsfortheDegree
DOCTOROFPHILOSOPHY
(ComputerScience)
June2017
Copyright 2017 DebarunKar
Acknowledgements
First, I would like to take this opportunity to thank myadvisor, Professor Milind Tambe. I want
toexpressmyheartfeltgratitudetohimforpatientlyguidingmeduringthecourseofmydoctoral
pursuitinthepastfouryears. Thisdissertationwouldnothavebeenpossiblewithouthisinvalu-
ableguidance,prolificencouragement,constructivecriticismandpersistenthelp. Irealizedvery
earlyonhowluckyIwastohavechosenMilindasmyadvisor,andmyrespectforhimhascon-
stantlygrownovertheyearsandstillcontinuestodoso. Havingattendedsomanyconferences,
I have seen first hand the respect and admiration he commands and the resultant respect we as
his students get wherever we go. Milind, your hard work and dedication never ceases to amaze
meandIhopetoemulatesuchanattitudeandaptitudeinbothmyfutureprofessionalcareerand
personal life. Furthermore, it gives me immense satisfaction and a sense of relief to know that I
haveamentorforlife. Iamdeeplyhumbledtobeyour25th graduatingPhDstudent.
Next, I would like to thank Nicole Sintov for being a wonderful mentor during the early
years of my PhD, guiding me along with Milind through my first set of research papers. Your
invaluableinsightsonhumanbehavior,empiricalresearchmethodsandexperimentaldesignhave
immenselybenefitedmyresearchovertheyears. Withoutourfrequentinteractions,myresearch
wouldhavesuffereddearlyandsoI’llalwaysbegratefulforyourguidance. Iwouldalsoliketo
ii
thankallofmydissertationcommitteemembersfortheirtimetowardsmyresearchanddevelop-
ment. My sincere gratitude to all of you: Jelena Mirkovic, Morteza Dehghani, Phebe Vayanos,
RichardJohnandYilingChen. Havingsuchaninterdisciplinaryensembleofcommitteemembers
helpedshapemythesisinwaysthatIcouldnothaveimagined.
AllofmyprojectsatUSCwerepossibleduetocollaborationswithmanyamazingresearchers
andrangersattheUgandaWildlifeAuthority,WildlifeConservationSociety,andWorldWildlife
Fund. Theirrelentlesseffortstomaketheworldabetterandsaferplaceenabledmetoworkwith
theminnotonlydevelopingnovelartificialintelligencemodelsbutmoreimportantlytestingthem
out in the field with exciting results. Specifically, I want to thank Andrew Plumptre from WCS
and Arnaud Lyet from WWF for working with us and providing us valuable data and insights
throughout the course of this work. This made my PhD all the more fun and valuable and so I
thankthemforgivingmethisopportunity.
During my time at USC I have also had the honor to work with many great researchers, ei-
therwhilecollaboratingonpapersorwhileorganizingworkshopsinconferences. Inparticular,I
wouldliketothank: FeiFangandFrancescoDelleFaveforbeingamazingcollaborators,without
your help many of my papers would never have happened; Ben Ford and Shahrzad Gholami for
collaboratingwithmeonanexcitingwildlifeconservationpaper;SubhasreeSenguptaforhelping
mecompletethefinalpaperinmyPhD;AruneshSinhaforbeinganamazingfriend,yourtremen-
dousmathematicalexpertiseandforallowingmetoworkwithyouontheonlytheorypaperthat
I will probably ever write; Jun-young Kwak for allowing me to work with you on the very first
paperthatIwasapartofduringmyPhD;WilliamHaskellforbeingthecoolestmathematicianI
haveeverseen,thankyouforourjointworkonfisheriesprotection;EceKamarandEricHorvitz
iii
forbeinginterestedinmyworkandforallthevaluablesuggestionsforourpaperonbeliefmod-
eling;LizBondiwithwhomIhopetosignificantlyimpactthefieldofwildlifeconservationinthe
coming years through the use of drone technology; and, Eugene Vorobeychik, Haifeng Xu and
LongTran-Thanhforyourwillingnesstoworkwithmeinconductingtwosuccessfuladversarial
reasoning workshops at AAMAS. I would also like to thank Thanh Nguyen, Matthew Brown,
Yasaman Dehghani Abbasi, Pradeep Varakantham, Aram Galstyan and Bo An for working with
me on some exciting problems. A big thank you to all the B.S. and M.S. students who have
worked with me over the years and for allowing me to guide them: Brian Schwedock, Nikhil
Cherukuri, Amit Plaha, Vivek Tiwari, Jie Zheng, Chiao-meng Huang, Soudhamini Radhakrish-
nanandDonnabellDmello.
ThisbringsmetotherestoftheTEAMCOREfamily,whichhasbeenbuiltthroughyearsof
hardwork,dedicationandmutualrespectbyMilindandhisstudents,manyofwhomIhavehad
the privilege of spending my PhD career with, while others whom I have met elsewhere. All of
you have been a constant source of friendship and support and so I thank you all: Chao Zhang,
Yundi Qian, Leandro Marcolino, Aaron Schlenker, Sara Mc Carthy, Bryan Wilder, Elizabeth
Bondi,AidaRahmattalabi,EricShieh,RongYang,AlbertJiang,ManishJain,JamesPita,Chris
Kiekintveld,MatthewTaylor,JanuszMarecki,PraveenParuchuri,PaulScerri,andMilind’svery
firstPhDstudentGalKaminka. ThereisonememberoftheTEAMCOREfamilywhoIwantto
thank immensely. Amulya Yadav, I am very grateful to have found a friend like you 9000 miles
awayfromhomeandtohavesharedmyPhDlifewithyou. Youarenolessthanabrothertome.
Thank you for the heartfelt discussions during all the good and bad times in my life, and thank
youfortheconstantlaughs,yourinfectiousfree-spiritedattitudeandforbeingaconstantsource
ofencouragement. Winmorebestpaperawardsbuddy!
iv
Iwouldalsoliketothankafewotherpeoplewhoplayedanimportantroleduringmystayat
USC.ThankyouPradiptaGhoshandNasirMohammedforrushingtothehospitalsolateinthe
nightofmyaccidentandformakingsurethatIreachedhomesafelyaftermytreatments. Thank
you Sahil Garg for being a wonderful roommate and for all your help throughout my good and
badtimes,especiallythetoughonemonthwhenIwasbed-riddenaftermyaccident. Thanksalso
toallmyfriendsattheUSCBengaliorganizationformakingmefeelathome.
Since the time I came to the United States, I have shared a major part of my life with one
person inparticular. Thank youPoushali Banerjeefor all yourlove andsupport and forlighting
upmylifeeverydayforthesepastfouryears. Iwanttoexpressmysincerestappreciationtoyou
for being there with me in this long and rocky journey, even during the lengthy periods of time
whenIretreatedtoworkingonmypapers. Sharingmylifewithyouhasbeenaconstantsource
ofhappinessandalearningexperienceatthesametime,andforthatIthankyou.
Finally, I want to thank my parents, the most important people in my life, for their constant
motivationandsupportinallofmyendeavors. BabaandMa,withoutyourdedicationtowardsmy
educationandwell-beingthroughouttheyearsthisresearchwouldnothavebeenpossible. Thank
youforlettingmebeawayfromhomeforsomanyyearsnowsothatIcanfulfillmydreams,and
thankyouforalwaysbeingthereinmygoodandbadtimes,nomatterhowinfrequentmyphone
callscangetfromtimetotime. Thankyouimmenselyforyourpatience,kindnessandsupport.
v
Contents
Acknowledgements ii
ListOfFigures ix
ListOfTables xii
Abstract xiv
1 Introduction 1
1.1 Fine-grainedAdversaryModelingwithPlentifulAttackData . . . . . . . . . . . 4
1.2 Coarse-grainedAdversaryModelingwithSparseAttackData . . . . . . . . . . . 7
1.3 AdversaryModelingwithBeliefData . . . . . . . . . . . . . . . . . . . . . . . 9
1.4 ThesisOverview . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 12
2 Background 13
2.1 StackelbergSecurityGames . . . . . . . . . . . . . . . . . . . . . . . . . . . . 13
2.2 HumanBehaviorModels . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 16
2.2.1 SubjectiveUtilityQuantalResponse(SUQR) . . . . . . . . . . . . . . . 16
2.2.2 BayesianSUQR . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 17
2.2.3 RobustSUQR. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 18
3 RelatedWork 19
3.1 Researchinrepeatedgames . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 19
3.1.1 LearninginrepeatedStackelberggames . . . . . . . . . . . . . . . . . . 19
3.1.2 Robuststrategiesinrepeatedgames . . . . . . . . . . . . . . . . . . . . 20
3.1.3 Learningfromreinforcementsinrepeatedgames . . . . . . . . . . . . . 21
3.2 RepeatedMeasuresStudies . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 23
3.3 ProbabilityWeightingFunctions . . . . . . . . . . . . . . . . . . . . . . . . . . 24
3.4 Relatedresearchinbeliefmodeling . . . . . . . . . . . . . . . . . . . . . . . . 26
3.4.1 Settingwithouttrainingdata . . . . . . . . . . . . . . . . . . . . . . . . 26
3.4.2 Settingwithtrainingdata . . . . . . . . . . . . . . . . . . . . . . . . . . 29
4 WildlifePoachingGame 31
4.1 GameOverview . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 31
4.1.1 ComputationofPoacherReward . . . . . . . . . . . . . . . . . . . . . . 33
4.1.2 Non-zerosumgame . . . . . . . . . . . . . . . . . . . . . . . . . . . . 34
4.2 PayoffStructures . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 35
vi
4.3 OnlineRepeatedMeasuresExperiments . . . . . . . . . . . . . . . . . . . . . . 36
4.3.1 ValidationandTrialGames. . . . . . . . . . . . . . . . . . . . . . . . . 37
4.3.2 ParticipantRetentionRate . . . . . . . . . . . . . . . . . . . . . . . . . 37
5 SHARP:ProbabilityWeighting 39
5.1 ProbabilityWeightingMechanism . . . . . . . . . . . . . . . . . . . . . . . . . 39
5.2 Discussions: . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 47
6 SHARP:AdaptiveUtilityModel 51
6.1 ObservationsandEvidence . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 52
6.2 SHARP’sUtilityComputation . . . . . . . . . . . . . . . . . . . . . . . . . . . 57
6.3 GeneratingDefenderStrategiesAgainstSHARP . . . . . . . . . . . . . . . . . 61
6.4 SHARPinaction: Anexample . . . . . . . . . . . . . . . . . . . . . . . . . . . 62
6.5 RL-SSG:ADescriptiveReinforcementLearningAlgorithmforSSGs . . . . . . 62
7 ResultswithHumanSubjectsonAMT 67
7.1 DefenderUtilities . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 68
7.2 LearnedProbabilityCurves . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 73
7.2.1 ComparisonwithPrelec’sprobabilityweightingfunction . . . . . . . . . 75
7.2.2 ComparisonwithPWV-SUQR . . . . . . . . . . . . . . . . . . . . . . 76
7.3 Attacksurfaceexposure . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 78
7.4 AdaptivenessofSHARP . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 80
7.5 ValidationandTestingRobustnessofAMTfindings . . . . . . . . . . . . . . . . 80
7.5.1 ResultswithSecurityExpertsinIndonesia . . . . . . . . . . . . . . . . 82
7.5.2 Resultswithfractionofcompleteattackdata . . . . . . . . . . . . . . . 84
8 INTERCEPT 88
8.1 WildlifeCrimeDataset . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 88
8.1.1 DatasetChallenges . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 89
8.1.2 DatasetComposition . . . . . . . . . . . . . . . . . . . . . . . . . . . . 89
8.2 CAPTUREandProposedVariants . . . . . . . . . . . . . . . . . . . . . . . . . 90
8.3 INTERCEPT . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 94
8.3.1 BoostIT . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 95
8.3.2 INTERCEPT:EnsembleofExperts . . . . . . . . . . . . . . . . . . . . 97
9 INTERCEPTResults 99
9.1 EvaluationMetrics . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 99
9.2 EvaluationonHistoricalReal-worldPatrolData . . . . . . . . . . . . . . . . . . 101
9.2.1 AttackabilityPredictionResults . . . . . . . . . . . . . . . . . . . . . . 101
9.2.2 ObservationPredictionResults . . . . . . . . . . . . . . . . . . . . . . . 104
9.2.3 ImpactofEnsembleandVotingRules . . . . . . . . . . . . . . . . . . . 105
9.3 EvaluationonReal-WorldDeployment . . . . . . . . . . . . . . . . . . . . . . . 107
9.4 LessonsLearned . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 110
vii
10 BeliefModeling 112
10.1 BeliefModelingGame . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 112
10.1.1 GameOverview . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 113
10.1.2 ExperimentalProcedure . . . . . . . . . . . . . . . . . . . . . . . . . . 115
10.2 ProposedModels: Settingwithouttrainingdata . . . . . . . . . . . . . . . . . . 118
10.2.1 PerfectlyRationalAdversary . . . . . . . . . . . . . . . . . . . . . . . . 119
10.2.1.1 UninformedAdversary . . . . . . . . . . . . . . . . . . . . . 119
10.2.1.2 InformedAdversary . . . . . . . . . . . . . . . . . . . . . . . 121
10.2.2 BoundedlyRationalAdversary . . . . . . . . . . . . . . . . . . . . . . . 121
10.3 ProposedModels: Settingwithtrainingdata . . . . . . . . . . . . . . . . . . . . 124
10.3.1 PerfectlyRationalAdversary . . . . . . . . . . . . . . . . . . . . . . . . 125
10.3.2 BoundedlyRationalAdversary . . . . . . . . . . . . . . . . . . . . . . . 125
10.4 ProposedModels: Settingwithtrainingandtestingdata . . . . . . . . . . . . . . 129
10.4.1 InstancebasedLearningModels . . . . . . . . . . . . . . . . . . . . . . 129
10.4.2 ClusteringbasedModels . . . . . . . . . . . . . . . . . . . . . . . . . . 130
11 BeliefModelingExperimentResults 132
11.1 Settingwithouttrainingdata . . . . . . . . . . . . . . . . . . . . . . . . . . . . 133
11.2 Settingwithtrainingdata . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 136
11.3 Settingwithtrainingandtestingdata . . . . . . . . . . . . . . . . . . . . . . . . 138
12 ConclusionsandFutureDirections 141
13 Appendix 145
13.1 AlgorithmtolearnPSUmodelparameters . . . . . . . . . . . . . . . . . . . . . 145
13.2 ProofofTheorem1 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 147
13.3 SampleEmail . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 150
13.4 ChallengesandRemediesofOnlineRepeatedMeasuresExperiments . . . . . . 151
13.4.1 Step1: PaymentScheme . . . . . . . . . . . . . . . . . . . . . . . . . . 154
13.4.2 InitialStudyEnrollment . . . . . . . . . . . . . . . . . . . . . . . . . . 157
13.4.3 ReminderEmails . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 158
13.5 ParticipantFeedback . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 159
13.5.1 Feedbackfor”Pleasetellusaboutyourexperienceplayingthegame”: . . 160
13.5.2 Feedback for ”Did you use a particular strategy in playing the game? If
yes,pleasespecify.”: . . . . . . . . . . . . . . . . . . . . . . . . . . . . 160
13.6 AdditionalExperimentalResultsonADS andADS . . . . . . . . . . . . . . 161
3 4
13.6.1 DefenderUtilities . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 161
13.6.2 LearnedProbabilityCurves . . . . . . . . . . . . . . . . . . . . . . . . 162
13.6.3 EvidenceofAttackSurfaceExposure . . . . . . . . . . . . . . . . . . . 163
13.6.4 AdaptivenessofSHARP . . . . . . . . . . . . . . . . . . . . . . . . . . 164
13.7 RobustnessofSHARP’sresultsacrossdomains . . . . . . . . . . . . . . . . . . 164
Bibliography 167
viii
List Of Figures
1.1 DifferentGreenSecurityGame(GSG)domains. . . . . . . . . . . . . . . . . . . 3
3.1 ProbabilityWeightingFunctions . . . . . . . . . . . . . . . . . . . . . . . . . . 24
4.1 GameInterfaceforoursimulatedonlinerepeatedSSG(Reward,penaltyandcov-
erageprobabilityforaselectedcellareshown) . . . . . . . . . . . . . . . . . . 32
4.2 Animaldensitystructures(ADS) . . . . . . . . . . . . . . . . . . . . . . . . . . 35
6.1 Evidenceforadaptivityofattackers . . . . . . . . . . . . . . . . . . . . . . . . 55
6.2 For various values of k (the number of nearest neighbors), percentage of people
whoattackedsimilartargetsinround2aftersucceedingorfailingintheprevious
round . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 56
6.3 (a,b,c): Round 2 strategies generated by SHARP (with discounting without At-
tractiveness), SHARP (no discounting but with Attractiveness) and SHARP re-
spectively;(d)ADS . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 63
1
7.1 DefenderutilitiesforvariousmodelsonADS andADS respectively. . . . . . 73
1 2
7.2 CumulativedefenderutilitiesforvariousmodelsonADS andADS respectively. 74
1 2
7.3 (a) Comparison of defender utilities between P-SUQR, SHARP and
SHARP(d=1)onADS andADS respectively . . . . . . . . . . . . . . . . . . 74
1 2
7.4 ComparisonofvariousmodelswithSUQR(PureStrategy)andtheRLmodelon
ADS respectively. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 74
1
7.5 RLmodelbaseddefenderstrategyforround2onADS . . . . . . . . . . . . . . 75
1
7.6 LearnedprobabilitycurvesforP-SUQRonADS andADS respectively. . . . 75
1 2
ix
7.7 Learned probability curves with Prelec’s probability weighting function for P-
SUQRonADS andADS respectively. . . . . . . . . . . . . . . . . . . . . . 76
1 2
7.8 (a) Comparison of the sum of squared errors for P-SUQR with Gonzalez and
Wu, and P-SUQR with Prelec’s probability weighting function respectively; (b)
ComparisonofthesumofsquarederrorsforP-SUQRandPWV-SUQRrespectively 77
7.9 (a) - (d) Learned probability curves for PWV-SUQR on ADS and ADS re-
1 2
spectively. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 78
7.10 Totalnumberofuniqueexposedtargetprofilestilltheendofeachroundforeach
coverageprobabilityintervalforADS andADS . . . . . . . . . . . . . . . . . 79
1 2
7.11 Adaptivity of SHARP and Convergence of P-SUQR on payoff structures ADS
1
andADS respectively. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 81
2
7.12 SHARPbasedstrategyforthedefenderonpayoffstructureADS . . . . . . . . . 81
2
7.13 DefenderutilityforSHARPagainstsecurityexpertsinIndonesia . . . . . . . . . 83
7.14 Evidemceforadaptivityofattackers(securityexpertsinIndonesia) . . . . . . . . 83
7.15 Totalnumberofuniqueexposedtargetprofilestilltheendofeachroundforeach
coverageprobabilityintervalforIndonesiaexpertsdata. . . . . . . . . . . . . . . 84
7.16 LearnedprobabilitycurvesforSHARPonADS onthesecurityexpertsdataset. 85
3
7.17 (a) and (b): Average 1-norm distances between defender strategies generated by
SHARP when the model is learned based on randomly sampled 50% data (0%,
5%, 10% and 15% deviation from actual data) and when the model is learned
fromthecompletedataset. ResultsareshownforADS andADS respectively. 86
1 2
7.18 (a) and (b): Average 1-norm distances between defender strategies generated by
SHARP when the model is learned based on randomly sampled 50% data (0%,
5%, 10% and 15% deviation from actual data) and when the model is learned
fromthecompletedataset. ResultsareshownforADS andADS respectively. 87
3 4
8.1 QENP . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 89
9.1 Illegal activities detected by rangers directed by INTERCEPT. Photo credit:
UgandaWildlifeAuthorityranger . . . . . . . . . . . . . . . . . . . . . . . . . 109
10.1 GameInterfaceforsimulatedonlinebeliefmodelinggame . . . . . . . . . . . . . . . 113
10.2 (a,b): AnimalDensities;(c)Maximin;(d)SUQR . . . . . . . . . . . . . . . . . 116
x