Key Algorithmic Principles, Deployed Systems, …...Argentina airport USC /56 Global Presence of...

561

Security GamesKey Algorithmic Principles Deployed Systems Research Challenges

Milind TambeUniversity of Southern California

with

Currentformer PhD studentspostdocs

Bo An Matthew Brown Francesco Delle Fave Fei

Fang Benjamin Ford William Haskell Manish Jain

Albert Jiang Debarun Kar Chris Kiekintveld Rajiv

Maheswaran Janusz Marecki Sara McCarthy Thanh

Nguyen Praveen Paruchuri Jonathan PearceJames

Pita Yundi Qian Aaron Schlenker Eric Shieh Jason

Tsai Pradeep Varakantham Haifeng Xu Amulya

Yadav Rong Yang Zhengyu Yin Chao Zhang

Other collaborators Shaddin Dughmi (USC) Richard John (USC) David

Kempe (USC) Nicole Sintov (USC) Nicholas

Weller (USC) hellipamp

Craig Boutilier (Google) Vince Conitzer (Duke)

Sarit Kraus (BIU Israel) E Lam (Panthera) Andrew

Lemieux (NCSR) Arnault Lyet (WWF) Kevin

Leyton-Brown (UBC) Fernando Ordonez (U Chile)

M Pechoucek (CTU Czech R) Rob Pickles

(Panthera) Andy Plumptre (WCS) Ariel Procaccia

(CMU) Tuomas Sandholm (CMU) Peter Stone

(UT Austin) Y Vorobeychik (Vanderbilt) hellipamp

Collaborators from the US Coast Guard

Transportation Security Administration LA

Sheriffrsquos Dept Uganda Wildlife Authority hellipamp

56

Global Challenges for Security

Game Theory for Security Resource Optimization

2

56

Example Model

Stackelberg Security Games

Security allocation

Targets have weights

Adversary surveillance

Target

1

Target

2

Target 1 4 -3 -1 1

Target 2 -5 5 2 -1

Adversary

3

Defender

56

Stackelberg Security GamesSecurity Resource Optimization Not 100 Security

Random strategy

Increase costuncertainty to attackers

Stackelberg game

Defender commits to mixed strategy

Adversary conducts surveillance responds

Stackelberg Equilibrium Optimal random

Target

1

Target

2

Target 1 4 -3 -1 1

Target 2 -5 5 2 -1

Adversary

4

Defender

56

Security Games Research amp Applications

Game theory+Optimization+Uncertainty+Learning+hellip

5

Infrastructure

Security Games

bull Massive-scale games

bull Reason with uncertainty

bull Learn adversary behavior from data

bull Repeated games

bull + Conservation biology criminology

LAX TSA

Coast

Guard

Coast

Guard

LA Sheriff

Green

Security Games

Coast

Guard

Opportunistic Crime

Security Games

PantheraWWF

Cyber Security

Games

IBM

Argentina airport USC

56

Global Presence of Security Games Efforts

56

Startup ARMORWAY

568

Outline Security Games Research (2007-)

2007 2011 2013 2014 2014 2015

Air travel Ports Trains

Evaluation II Real-world deployments (Patience)

Evaluation I AAAI IJCAI AAMAS papershellip

Fisheries Wildlife Urban Crime

Infrastructure

Security Games

Green

Security Games

Opportunistic

Crime

Security Games

2014 2014 20152015

569

ARMOR Airport Security LAX [2007]

Basic ldquoStackelberg Security Gamerdquo Model

GLASGOW 63007

5610

Basic Security Game Operation [2007]Using ARMOR as an Example Pita Paruchuri

Mixed Integer Program

Pr(Canine patrol 8 AM Terminals 357) = 033


helliphellipCanine Team Schedule July 28Term 1 Term 2 Term 3 Term 4 Term 5 Term 6 Term 7 Term 8

8 AM Team1 Team3 Team5



Target 1 Target 2 Target 3

Defender 1 2 -1 -3 4 -3 4

Defender 2 -3 3 3 -2 hellip

Defender 3 hellip hellip hellip

5611

Security Game MIP [2007]Generate Mixed Strategy for Defender in ARMOR

1 i

ixts

jq

ix

ijR

Xi Qj

max

1Qj

jq

MqxCaji

Xi

ij)1()(0

Maximize defender

expected utility

Defender mixed

strategy

Adversary best

response

Pita Paruchuri

Adversary response


Defender 1 2 -1 -3 4 -3 4



5612

Security Game Payoffs [2007]Previous Research Provides Payoffs in Security Game Domains

jq

ix

ijR

Xi Qj

maxMaximize defender

expected utility


Defender 1 2 -1 -3 4 -3 4



5613

ARMOR MIP [2007]Solving for a Single Adversary Type

119909119894

ARMORhellipthrows a digital cloak of invisibilityhellip

56

IRIS Federal Air Marshals Service [2009]Scale Up Number of Defender Strategies

Strategy 1 Strategy 2 Strategy 3

Strateg

y 1

Strateg

y 2

Strateg

y 3

Strateg

y 4

Strateg

y 5

Strateg

y 6

Incremental strategy generation

Column generation Not enumerate all 1041

actions


Strategy 1

Strategy 2

Strategy 3

Strategy 4

Strategy 5

Strategy 6

14

ARMOR runs out of memory

56

Best new pure strategy

IRIS Incremental Strategy Generation

Column Generation

Attack 1 Attack 2 hellip Attack 6

124 5-10 4-8 hellip -209

378 -8 10 -810 hellip -810

hellip

500 rows

NOT 1041


124 5-10 4-8 hellip -209 Slave

Master

GLOBAL

OPTIMAL


124 5-10 4-8 hellip -209

378 -8 10 -810 hellip -810

15

Jain Kiekintveld

56

IRIS Deployed FAMS (2009-)

ldquohellipin 2011 the Military Operations Research Society selected a University

of Southern California project with FAMS on randomizing flight

schedules for the prestigious Rist Awardhelliprdquo

-R S Bray (TSA)

Transportation Security Subcommittee

US House of Representatives 2012

16

Significant change in FAMS operations

5617

Road Social Networks[2013]Scale-up Double Oracle

Jain

Road networkseg checkpoints

20416 Roads15 checkpoints

20 min

56

PROTECT Port Protection Patrols Deployed 2011-

18

Using ldquoMarginalsrdquo for Scale-up

USS Cole after attack French oil tanker hit by small boat

56

PROTECT Ferry Protection Deployed 2013-Using ldquoMarginalsrdquo for Scale-up

19

56

Ferries Scale-up with Mobile Resources amp Moving TargetsTransition Graph Representation

A 5 min A 10 min A 15 min

B 5 min B 10 min B 15 min

C 5 min C 10 min C 15 min

A

B

C

5 min 10 min 15 min

20

56




A

B

C

5 min 10 min 15 min

Ferry


21

56




A

B

C

5 min 10 min 15 min

Ferry

22

Patrols protect nearby ferry location Solve as done in ARMOR

Ferries Patrol Routes as VariablesExponential Numbers of Patrol Routes

56




A

B

C

5 min 10 min 15 min

Ferry



23

Pr([(B5) (C 10) (C15)]) = 047

Pr([(B5) (C10) (B15)]) =023

NT variables

Pr([(A5) (A10) (B15)]) = 017

Pr([(A5) (A10) (A15)]) = 013

Jiang KiekintveldFang

56




A

B

C

5 min 10 min 15 min

Ferry

Variables NOT routes but probability flow over each segment

Ferries Scale-up Marginal Probabilities Over Segments

24

NT variables


56




A

B

C

5 min 10 min 15 min

Ferry

030

017

013

Obtain marginal probabilities over segments

010

007

003

N2T variablesExtract Pr([(B5) (C 10) (C15)]) = 047

Pr([(B5) (C10) (B15)]) =023

Ferries Scale-up with Marginals Over Separable SegmentsSignificant Speedup

25

NT variables

56

Fisheries

26

Outline ldquoSecurity Gamesrdquo Research (2007-)

2007 2009 2013 2014 2014 2015

Air travel Ports Trains Wildlife Urban Crime

56

TRUSTS Frequent adversary interaction games

Patrols Against Fare Evaders




A

B

C

5 min 10 min 15 min

030

017

013

010

010

27

56

TRUSTS Patrols Against Fare Evaders

Uncertainty in Defender Action Execution




A

B

C

5 min 10 min 15 min

030

015

010

010

007

003

005

28

Jiang Delle Fave

56

Markov Decision Problems in Security games




A

B

C

5 min 10 min 15 min

030

015

010

010

007

003

005

29


Uncertainty in Defender Action Execution Jiang Delle Fave

56

Uncertainty Space Algorithms

Bayesian and Robust Approaches

Adversary payoff uncertainty

Adversary observation amp

defender execution

uncertainty

Adversary rationality

uncertainty

RECON

BRASS

Monotonic Maximin

(Monotonic adversary)

30

URAC

Payoff interval

Not point

estimateGMC HUNTER

Bayesian

Robust

Yin Nguyen An

0

05

1

15

5 10

Def

end

ers

EU

Targets

ISG RECON URAC

5631


2007 2011 2013 2014 2014 2015

Air travel Ports Trains Fisheries Wildlife Urban Crime

2014 2014 20152015

56

Protecting Forests Fish Rivers amp Wildlife

Green Security Games

Fishery Protection

McCarthy Ford Brown

Forest Protection

River Pollution Prevention

56

Wildlife Protection

Murchison Falls National Park Uganda

5634

Green Security GamesRepeated Stackelberg Game

Learn from crime data

Improve model

Defender calculates

mixed strategy

Defender executes

randomized patrols

Poachers attack targets

Bounded rationality model of poachers

56

Uncertainty in Adversary Decision Bounded Rationality

Human Subjects as Poachers Kar

56

Quantal Response(QR)[McFadden 73]Stochastic Choice Better Choice More likely

Lesson 1 Quantal Response [2011]

Models of Bounded Rationality

T

j

jxadversary

EU

jxadversary

EU

j

e

eq

1

))((

))((

Adversaryrsquos

probability of

choosing target j

RewardProb)1(Penalty Prob)( CaptureCapturejadversary

EUPerfect

-3

-25

-2

-15

-1

-05

0

Payoff 1 Payoff 2 Payoff 3 Payoff 4

Quantal

Response

Epsilon

robust

Perfect

rational

YangPita

42

56

Lesson 2 Subjective Utility Quantal Response Models of

Bounded Rationality

Penalty3

Reward2

Prob1

)( wwCapturewjadversary

SEU

M

j

jxSEU

jxSEU

jadversary

adversary

e

eq

1

)(

)(

Subjective Utility Quantal Response(SUQR) [Nguyen 13]

Ca

ptu

re

Pro

ba

bil

ity

UWA data from

Uganda

Year 2012

Predicting

poaching

Adversaryrsquos

probability of

choosing target j

Nguyen

43

Reward (animals)

5638

Green Security Games[2015]

Testing SUQR From One-Shot to Repeated Games


Defender calculates strategy

Execute randomized

patrols


Repeated games on AMT

35 weeks 40 human subjects

10000 emails

Kar

-08

-07

-06

-05

-04

-03

-02

-01

0

01

Round 1Round 2Round 3Round 4Round 5

Def

end

er U

tili

ty

Maximin SUQR

Bayesian SUQR

5639

Lesson 3 SHARP and Repeated Stackelberg Games

Incorporate Past SuccessFailure in SUQR Kar

Animal Density

Co

ver

ag

e

Pro

ba

bil

ity

Human

success

Human

Failure

Increasedecrease

Subjective Utility

-08-07-06-05-04-03-02-01

001

Round 1 Round 2 Round 3 Round 4 Round 5

Def

end

er U

tili

ty

Maximin SUQR

Bayesian SUQR SHARP



Execute randomized

patrols


564040

Learned Probability Weighting Function

Adversaryrsquos probability weighting function is S-shaped

Contrary to Prospect Theory (Kahneman lsquo79)

Kar

5641

PAWS Protection Assistant for Wildlife Security

Trials in Uganda and Malaysia [2014]

Andrew Lemieux PantheraUganda Malaysia

Important lesson Geography

Malaysia

56

PAWS for Wildlife Security

Scale Uncertainty in Green Security Games

Scale Hierarchical model

Hierarchical Grid +ldquoStreet maprdquo

Species location uncertainty

In regular use in Malaysia

Fang

56

Opportunistic Crime Security Game[2015]

Integrating Learning in Basic Security Game Model

Crime prediction use past crime amp police allocation data

43

Zhang Sinha

Best Simulation Results

56

Evaluating Deployed Security Systems Not EasyAre Security Games Better at Optimizing Limited Resources

Security games improve over humans (or simple) planners

Eg humans fall into predictable patterns high cognitive load

44

Lab

Evaluation

Simulated

adversary

Human subject

adversaries

Field Evaluation

Patrol quality

Unpredictable Cover

Compare real schedules

Scheduling competition

Expert evaluation

Field Evaluation

Tests against adversaries

ldquoMock attackersrdquo

Capture rates of

real adversaries

56

Day 1 Day 2 Day 3 Day 4 Day 5 Day 6 Day 7

Co

un

t

Field Evaluation of Schedule Quality

Improved Patrol Unpredictability amp Coverage for Less Effort

Patrols Before PROTECT Boston Patrols After PROTECT Boston


Co

un

t

Base Patrol Area

45

PROTECT (Coast Guard) 350 increase defender expected utility

56



46

FAMS IRIS Outperformed expert human over six monthsReportGAO-09-903T

Trains TRUSTS outperformed expert humans

schedule 90 officers on LA trains

3

35

4

45

5

55

Q1

Q2

Q3

Q4

Q5

Q6

Q7

Q8

Q9

Q1

0

Q11

Q12

Secu

rity

Sco

re

Human Game Theory

56

Field Test Against Adversaries Mock Attackers

Example from PROTECT

ldquoMock attackerrdquo team deployed in Boston

Comparing PRE- to POST-PROTECT ldquodeterrencerdquo improved

Additional real-world indicators from Boston

Boston boaters questions

ldquohas the Coast Guard recently acquired more boatsrdquo

47

5648

Field Tests Against Adversaries

Computational Game Theory in the Field

Game theory vs Random

21 days of patrol

Identical conditions

Random + Human

Not controlled

0

20

40

60

80

100 Miscellaneous

Drugs

Firearm Violations

0

5

10

15

20

Captures30 min

Warnings30 min

Violations30 min

Game Theory

Rand+Human

Controlled

56

User Feedback

Example from ARMOR IRIS amp PROTECT

February 2009 Commendations

LAX Police (City of Los Angeles)

July 2011 Operational Excellence

Award (US Coast Guard Boston)

September 2011 Certificate of

Appreciation (Federal Air Marshals)

June 2013 Meritorious Team Commendation

from Commandant (US Coast Guard)

49

56

Global Efforts on Security Games

Yet Just the Beginninghellip

50

Thank you

to sponsors

56

THANK YOU

tambeuscedu

httpteamcoreuscedusecurity

51

56

Global Challenges for Security

Game Theory for Security Resource Optimization

2

56

Example Model


Security allocation



Target

1

Target

2

Target 1 4 -3 -1 1

Target 2 -5 5 2 -1

Adversary

3

Defender

56


Random strategy


Stackelberg game




Target

1

Target

2

Target 1 4 -3 -1 1

Target 2 -5 5 2 -1

Adversary

4

Defender

56



5

Infrastructure

Security Games




bull Repeated games


LAX TSA

Coast

Guard

Coast

Guard

LA Sheriff

Green

Security Games

Coast

Guard

Opportunistic Crime

Security Games

PantheraWWF

Cyber Security

Games

IBM


56


56

Startup ARMORWAY

568


2007 2011 2013 2014 2014 2015





Infrastructure

Security Games

Green

Security Games

Opportunistic

Crime

Security Games

2014 2014 20152015

569



GLASGOW 63007

5610










Defender 1 2 -1 -3 4 -3 4



5611


1 i

ixts

jq

ix

ijR

Xi Qj

max

1Qj

jq

MqxCaji

Xi

ij)1()(0

Maximize defender

expected utility

Defender mixed

strategy

Adversary best

response

Pita Paruchuri

Adversary response


Defender 1 2 -1 -3 4 -3 4



5612


jq

ix

ijR

Xi Qj


expected utility


Defender 1 2 -1 -3 4 -3 4



5613


119909119894


56



Strateg

y 1

Strateg

y 2

Strateg

y 3

Strateg

y 4

Strateg

y 5

Strateg

y 6



actions


Strategy 1

Strategy 2

Strategy 3

Strategy 4

Strategy 5

Strategy 6

14


56



Column Generation


124 5-10 4-8 hellip -209

378 -8 10 -810 hellip -810

hellip

500 rows

NOT 1041


124 5-10 4-8 hellip -209 Slave

Master

GLOBAL

OPTIMAL


124 5-10 4-8 hellip -209

378 -8 10 -810 hellip -810

15

Jain Kiekintveld

56





-R S Bray (TSA)



16


5617


Jain



20 min

56


18



56


19

56





A

B

C

5 min 10 min 15 min

20

56




A

B

C

5 min 10 min 15 min

Ferry


21

56




A

B

C

5 min 10 min 15 min

Ferry

22



56




A

B

C

5 min 10 min 15 min

Ferry



23

Pr([(B5) (C 10) (C15)]) = 047

Pr([(B5) (C10) (B15)]) =023

NT variables

Pr([(A5) (A10) (B15)]) = 017

Pr([(A5) (A10) (A15)]) = 013


56




A

B

C

5 min 10 min 15 min

Ferry



24

NT variables


56




A

B

C

5 min 10 min 15 min

Ferry

030

017

013


010

007

003


Pr([(B5) (C10) (B15)]) =023


25

NT variables

56

Fisheries

26


2007 2009 2013 2014 2014 2015


56






A

B

C

5 min 10 min 15 min

030

017

013

010

010

27

56






A

B

C

5 min 10 min 15 min

030

015

010

010

007

003

005

28

Jiang Delle Fave

56





A

B

C

5 min 10 min 15 min

030

015

010

010

007

003

005

29



56





defender execution

uncertainty


uncertainty

RECON

BRASS

Monotonic Maximin


30

URAC

Payoff interval

Not point

estimateGMC HUNTER

Bayesian

Robust

Yin Nguyen An

0

05

1

15

5 10

Def

end

ers

EU

Targets

ISG RECON URAC

5631


2007 2011 2013 2014 2014 2015


2014 2014 20152015

56



Fishery Protection

McCarthy Ford Brown

Forest Protection


56

Wildlife Protection


5634



Improve model

Defender calculates

mixed strategy

Defender executes

randomized patrols



56



56




T

j

jxadversary

EU

jxadversary

EU

j

e

eq

1

))((

))((

Adversaryrsquos

probability of

choosing target j


EUPerfect

-3

-25

-2

-15

-1

-05

0


Quantal

Response

Epsilon

robust

Perfect

rational

YangPita

42

56


Bounded Rationality

Penalty3

Reward2

Prob1


SEU

M

j

jxSEU

jxSEU

jadversary

adversary

e

eq

1

)(

)(


Ca

ptu

re

Pro

ba

bil

ity

UWA data from

Uganda

Year 2012

Predicting

poaching

Adversaryrsquos

probability of

choosing target j

Nguyen

43

Reward (animals)

5638





Execute randomized

patrols




10000 emails

Kar

-08

-07

-06

-05

-04

-03

-02

-01

0

01


Def

end

er U

tili

ty

Maximin SUQR

Bayesian SUQR

5639



Animal Density

Co

ver

ag

e

Pro

ba

bil

ity

Human

success

Human

Failure

Increasedecrease

Subjective Utility

-08-07-06-05-04-03-02-01

001


Def

end

er U

tili

ty

Maximin SUQR

Bayesian SUQR SHARP



Execute randomized

patrols


564040




Kar

5641





Malaysia

56







Fang

56




43

Zhang Sinha


56




44

Lab

Evaluation

Simulated

adversary

Human subject

adversaries

Field Evaluation

Patrol quality

Unpredictable Cover



Expert evaluation

Field Evaluation



Capture rates of

real adversaries

56


Co

un

t





Co

un

t

Base Patrol Area

45


56



46




3

35

4

45

5

55

Q1

Q2

Q3

Q4

Q5

Q6

Q7

Q8

Q9

Q1

0

Q11

Q12

Secu

rity

Sco

re

Human Game Theory

56








47

5648




21 days of patrol


Random + Human

Not controlled

0

20

40

60

80

100 Miscellaneous

Drugs

Firearm Violations

0

5

10

15

20

Captures30 min

Warnings30 min

Violations30 min

Game Theory

Rand+Human

Controlled

56

User Feedback










49

56



50

Thank you

to sponsors

56

THANK YOU

tambeuscedu


51

56

Example Model


Security allocation



Target

1

Target

2

Target 1 4 -3 -1 1

Target 2 -5 5 2 -1

Adversary

3

Defender

56


Random strategy


Stackelberg game




Target

1

Target

2

Target 1 4 -3 -1 1

Target 2 -5 5 2 -1

Adversary

4

Defender

56



5

Infrastructure

Security Games




bull Repeated games


LAX TSA

Coast

Guard

Coast

Guard

LA Sheriff

Green

Security Games

Coast

Guard

Opportunistic Crime

Security Games

PantheraWWF

Cyber Security

Games

IBM


56


56

Startup ARMORWAY

568


2007 2011 2013 2014 2014 2015





Infrastructure

Security Games

Green

Security Games

Opportunistic

Crime

Security Games

2014 2014 20152015

569



GLASGOW 63007

5610










Defender 1 2 -1 -3 4 -3 4



5611


1 i

ixts

jq

ix

ijR

Xi Qj

max

1Qj

jq

MqxCaji

Xi

ij)1()(0

Maximize defender

expected utility

Defender mixed

strategy

Adversary best

response

Pita Paruchuri

Adversary response


Defender 1 2 -1 -3 4 -3 4



5612


jq

ix

ijR

Xi Qj


expected utility


Defender 1 2 -1 -3 4 -3 4



5613


119909119894


56



Strateg

y 1

Strateg

y 2

Strateg

y 3

Strateg

y 4

Strateg

y 5

Strateg

y 6



actions


Strategy 1

Strategy 2

Strategy 3

Strategy 4

Strategy 5

Strategy 6

14


56



Column Generation


124 5-10 4-8 hellip -209

378 -8 10 -810 hellip -810

hellip

500 rows

NOT 1041


124 5-10 4-8 hellip -209 Slave

Master

GLOBAL

OPTIMAL


124 5-10 4-8 hellip -209

378 -8 10 -810 hellip -810

15

Jain Kiekintveld

56





-R S Bray (TSA)



16


5617


Jain



20 min

56


18



56


19

56





A

B

C

5 min 10 min 15 min

20

56




A

B

C

5 min 10 min 15 min

Ferry


21

56




A

B

C

5 min 10 min 15 min

Ferry

22



56




A

B

C

5 min 10 min 15 min

Ferry



23

Pr([(B5) (C 10) (C15)]) = 047

Pr([(B5) (C10) (B15)]) =023

NT variables

Pr([(A5) (A10) (B15)]) = 017

Pr([(A5) (A10) (A15)]) = 013


56




A

B

C

5 min 10 min 15 min

Ferry



24

NT variables


56




A

B

C

5 min 10 min 15 min

Ferry

030

017

013


010

007

003


Pr([(B5) (C10) (B15)]) =023


25

NT variables

56

Fisheries

26


2007 2009 2013 2014 2014 2015


56






A

B

C

5 min 10 min 15 min

030

017

013

010

010

27

56






A

B

C

5 min 10 min 15 min

030

015

010

010

007

003

005

28

Jiang Delle Fave

56





A

B

C

5 min 10 min 15 min

030

015

010

010

007

003

005

29



56





defender execution

uncertainty


uncertainty

RECON

BRASS

Monotonic Maximin


30

URAC

Payoff interval

Not point

estimateGMC HUNTER

Bayesian

Robust

Yin Nguyen An

0

05

1

15

5 10

Def

end

ers

EU

Targets

ISG RECON URAC

5631


2007 2011 2013 2014 2014 2015


2014 2014 20152015

56



Fishery Protection

McCarthy Ford Brown

Forest Protection


56

Wildlife Protection


5634



Improve model

Defender calculates

mixed strategy

Defender executes

randomized patrols



56



56




T

j

jxadversary

EU

jxadversary

EU

j

e

eq

1

))((

))((

Adversaryrsquos

probability of

choosing target j


EUPerfect

-3

-25

-2

-15

-1

-05

0


Quantal

Response

Epsilon

robust

Perfect

rational

YangPita

42

56


Bounded Rationality

Penalty3

Reward2

Prob1


SEU

M

j

jxSEU

jxSEU

jadversary

adversary

e

eq

1

)(

)(


Ca

ptu

re

Pro

ba

bil

ity

UWA data from

Uganda

Year 2012

Predicting

poaching

Adversaryrsquos

probability of

choosing target j

Nguyen

43

Reward (animals)

5638





Execute randomized

patrols




10000 emails

Kar

-08

-07

-06

-05

-04

-03

-02

-01

0

01


Def

end

er U

tili

ty

Maximin SUQR

Bayesian SUQR

5639



Animal Density

Co

ver

ag

e

Pro

ba

bil

ity

Human

success

Human

Failure

Increasedecrease

Subjective Utility

-08-07-06-05-04-03-02-01

001


Def

end

er U

tili

ty

Maximin SUQR

Bayesian SUQR SHARP



Execute randomized

patrols


564040




Kar

5641





Malaysia

56







Fang

56




43

Zhang Sinha


56




44

Lab

Evaluation

Simulated

adversary

Human subject

adversaries

Field Evaluation

Patrol quality

Unpredictable Cover



Expert evaluation

Field Evaluation



Capture rates of

real adversaries

56


Co

un

t





Co

un

t

Base Patrol Area

45


56



46




3

35

4

45

5

55

Q1

Q2

Q3

Q4

Q5

Q6

Q7

Q8

Q9

Q1

0

Q11

Q12

Secu

rity

Sco

re

Human Game Theory

56








47

5648




21 days of patrol


Random + Human

Not controlled

0

20

40

60

80

100 Miscellaneous

Drugs

Firearm Violations

0

5

10

15

20

Captures30 min

Warnings30 min

Violations30 min

Game Theory

Rand+Human

Controlled

56

User Feedback










49

56



50

Thank you

to sponsors

56

THANK YOU

tambeuscedu


51

56


Random strategy


Stackelberg game




Target

1

Target

2

Target 1 4 -3 -1 1

Target 2 -5 5 2 -1

Adversary

4

Defender

56



5

Infrastructure

Security Games




bull Repeated games


LAX TSA

Coast

Guard

Coast

Guard

LA Sheriff

Green

Security Games

Coast

Guard

Opportunistic Crime

Security Games

PantheraWWF

Cyber Security

Games

IBM


56


56

Startup ARMORWAY

568


2007 2011 2013 2014 2014 2015





Infrastructure

Security Games

Green

Security Games

Opportunistic

Crime

Security Games

2014 2014 20152015

569



GLASGOW 63007

5610










Defender 1 2 -1 -3 4 -3 4



5611


1 i

ixts

jq

ix

ijR

Xi Qj

max

1Qj

jq

MqxCaji

Xi

ij)1()(0

Maximize defender

expected utility

Defender mixed

strategy

Adversary best

response

Pita Paruchuri

Adversary response


Defender 1 2 -1 -3 4 -3 4



5612


jq

ix

ijR

Xi Qj


expected utility


Defender 1 2 -1 -3 4 -3 4



5613


119909119894


56



Strateg

y 1

Strateg

y 2

Strateg

y 3

Strateg

y 4

Strateg

y 5

Strateg

y 6



actions


Strategy 1

Strategy 2

Strategy 3

Strategy 4

Strategy 5

Strategy 6

14


56



Column Generation


124 5-10 4-8 hellip -209

378 -8 10 -810 hellip -810

hellip

500 rows

NOT 1041


124 5-10 4-8 hellip -209 Slave

Master

GLOBAL

OPTIMAL


124 5-10 4-8 hellip -209

378 -8 10 -810 hellip -810

15

Jain Kiekintveld

56





-R S Bray (TSA)



16


5617


Jain



20 min

56


18



56


19

56





A

B

C

5 min 10 min 15 min

20

56




A

B

C

5 min 10 min 15 min

Ferry


21

56




A

B

C

5 min 10 min 15 min

Ferry

22



56




A

B

C

5 min 10 min 15 min

Ferry



23

Pr([(B5) (C 10) (C15)]) = 047

Pr([(B5) (C10) (B15)]) =023

NT variables

Pr([(A5) (A10) (B15)]) = 017

Pr([(A5) (A10) (A15)]) = 013


56




A

B

C

5 min 10 min 15 min

Ferry



24

NT variables


56




A

B

C

5 min 10 min 15 min

Ferry

030

017

013


010

007

003


Pr([(B5) (C10) (B15)]) =023


25

NT variables

56

Fisheries

26


2007 2009 2013 2014 2014 2015


56






A

B

C

5 min 10 min 15 min

030

017

013

010

010

27

56






A

B

C

5 min 10 min 15 min

030

015

010

010

007

003

005

28

Jiang Delle Fave

56





A

B

C

5 min 10 min 15 min

030

015

010

010

007

003

005

29



56





defender execution

uncertainty


uncertainty

RECON

BRASS

Monotonic Maximin


30

URAC

Payoff interval

Not point

estimateGMC HUNTER

Bayesian

Robust

Yin Nguyen An

0

05

1

15

5 10

Def

end

ers

EU

Targets

ISG RECON URAC

5631


2007 2011 2013 2014 2014 2015


2014 2014 20152015

56



Fishery Protection

McCarthy Ford Brown

Forest Protection


56

Wildlife Protection


5634



Improve model

Defender calculates

mixed strategy

Defender executes

randomized patrols



56



56




T

j

jxadversary

EU

jxadversary

EU

j

e

eq

1

))((

))((

Adversaryrsquos

probability of

choosing target j


EUPerfect

-3

-25

-2

-15

-1

-05

0


Quantal

Response

Epsilon

robust

Perfect

rational

YangPita

42

56


Bounded Rationality

Penalty3

Reward2

Prob1


SEU

M

j

jxSEU

jxSEU

jadversary

adversary

e

eq

1

)(

)(


Ca

ptu

re

Pro

ba

bil

ity

UWA data from

Uganda

Year 2012

Predicting

poaching

Adversaryrsquos

probability of

choosing target j

Nguyen

43

Reward (animals)

5638





Execute randomized

patrols




10000 emails

Kar

-08

-07

-06

-05

-04

-03

-02

-01

0

01


Def

end

er U

tili

ty

Maximin SUQR

Bayesian SUQR

5639



Animal Density

Co

ver

ag

e

Pro

ba

bil

ity

Human

success

Human

Failure

Increasedecrease

Subjective Utility

-08-07-06-05-04-03-02-01

001


Def

end

er U

tili

ty

Maximin SUQR

Bayesian SUQR SHARP



Execute randomized

patrols


564040




Kar

5641





Malaysia

56







Fang

56




43

Zhang Sinha


56




44

Lab

Evaluation

Simulated

adversary

Human subject

adversaries

Field Evaluation

Patrol quality

Unpredictable Cover



Expert evaluation

Field Evaluation



Capture rates of

real adversaries

56


Co

un

t





Co

un

t

Base Patrol Area

45


56



46




3

35

4

45

5

55

Q1

Q2

Q3

Q4

Q5

Q6

Q7

Q8

Q9

Q1

0

Q11

Q12

Secu

rity

Sco

re

Human Game Theory

56








47

5648




21 days of patrol


Random + Human

Not controlled

0

20

40

60

80

100 Miscellaneous

Drugs

Firearm Violations

0

5

10

15

20

Captures30 min

Warnings30 min

Violations30 min

Game Theory

Rand+Human

Controlled

56

User Feedback










49

56



50

Thank you

to sponsors

56

THANK YOU

tambeuscedu


51

56



5

Infrastructure

Security Games




bull Repeated games


LAX TSA

Coast

Guard

Coast

Guard

LA Sheriff

Green

Security Games

Coast

Guard

Opportunistic Crime

Security Games

PantheraWWF

Cyber Security

Games

IBM


56


56

Startup ARMORWAY

568


2007 2011 2013 2014 2014 2015





Infrastructure

Security Games

Green

Security Games

Opportunistic

Crime

Security Games

2014 2014 20152015

569



GLASGOW 63007

5610










Defender 1 2 -1 -3 4 -3 4



5611


1 i

ixts

jq

ix

ijR

Xi Qj

max

1Qj

jq

MqxCaji

Xi

ij)1()(0

Maximize defender

expected utility

Defender mixed

strategy

Adversary best

response

Pita Paruchuri

Adversary response


Defender 1 2 -1 -3 4 -3 4



5612


jq

ix

ijR

Xi Qj


expected utility


Defender 1 2 -1 -3 4 -3 4



5613


119909119894


56



Strateg

y 1

Strateg

y 2

Strateg

y 3

Strateg

y 4

Strateg

y 5

Strateg

y 6



actions


Strategy 1

Strategy 2

Strategy 3

Strategy 4

Strategy 5

Strategy 6

14


56



Column Generation


124 5-10 4-8 hellip -209

378 -8 10 -810 hellip -810

hellip

500 rows

NOT 1041


124 5-10 4-8 hellip -209 Slave

Master

GLOBAL

OPTIMAL


124 5-10 4-8 hellip -209

378 -8 10 -810 hellip -810

15

Jain Kiekintveld

56





-R S Bray (TSA)



16


5617


Jain



20 min

56


18



56


19

56





A

B

C

5 min 10 min 15 min

20

56




A

B

C

5 min 10 min 15 min

Ferry


21

56




A

B

C

5 min 10 min 15 min

Ferry

22



56




A

B

C

5 min 10 min 15 min

Ferry



23

Pr([(B5) (C 10) (C15)]) = 047

Pr([(B5) (C10) (B15)]) =023

NT variables

Pr([(A5) (A10) (B15)]) = 017

Pr([(A5) (A10) (A15)]) = 013


56




A

B

C

5 min 10 min 15 min

Ferry



24

NT variables


56




A

B

C

5 min 10 min 15 min

Ferry

030

017

013


010

007

003


Pr([(B5) (C10) (B15)]) =023


25

NT variables

56

Fisheries

26


2007 2009 2013 2014 2014 2015


56






A

B

C

5 min 10 min 15 min

030

017

013

010

010

27

56






A

B

C

5 min 10 min 15 min

030

015

010

010

007

003

005

28

Jiang Delle Fave

56





A

B

C

5 min 10 min 15 min

030

015

010

010

007

003

005

29



56





defender execution

uncertainty


uncertainty

RECON

BRASS

Monotonic Maximin


30

URAC

Payoff interval

Not point

estimateGMC HUNTER

Bayesian

Robust

Yin Nguyen An

0

05

1

15

5 10

Def

end

ers

EU

Targets

ISG RECON URAC

5631


2007 2011 2013 2014 2014 2015


2014 2014 20152015

56



Fishery Protection

McCarthy Ford Brown

Forest Protection


56

Wildlife Protection


5634



Improve model

Defender calculates

mixed strategy

Defender executes

randomized patrols



56



56




T

j

jxadversary

EU

jxadversary

EU

j

e

eq

1

))((

))((

Adversaryrsquos

probability of

choosing target j


EUPerfect

-3

-25

-2

-15

-1

-05

0


Quantal

Response

Epsilon

robust

Perfect

rational

YangPita

42

56


Bounded Rationality

Penalty3

Reward2

Prob1


SEU

M

j

jxSEU

jxSEU

jadversary

adversary

e

eq

1

)(

)(


Ca

ptu

re

Pro

ba

bil

ity

UWA data from

Uganda

Year 2012

Predicting

poaching

Adversaryrsquos

probability of

choosing target j

Nguyen

43

Reward (animals)

5638





Execute randomized

patrols




10000 emails

Kar

-08

-07

-06

-05

-04

-03

-02

-01

0

01


Def

end

er U

tili

ty

Maximin SUQR

Bayesian SUQR

5639



Animal Density

Co

ver

ag

e

Pro

ba

bil

ity

Human

success

Human

Failure

Increasedecrease

Subjective Utility

-08-07-06-05-04-03-02-01

001


Def

end

er U

tili

ty

Maximin SUQR

Bayesian SUQR SHARP



Execute randomized

patrols


564040




Kar

5641





Malaysia

56







Fang

56




43

Zhang Sinha


56




44

Lab

Evaluation

Simulated

adversary

Human subject

adversaries

Field Evaluation

Patrol quality

Unpredictable Cover



Expert evaluation

Field Evaluation



Capture rates of

real adversaries

56


Co

un

t





Co

un

t

Base Patrol Area

45


56



46




3

35

4

45

5

55

Q1

Q2

Q3

Q4

Q5

Q6

Q7

Q8

Q9

Q1

0

Q11

Q12

Secu

rity

Sco

re

Human Game Theory

56








47

5648




21 days of patrol


Random + Human

Not controlled

0

20

40

60

80

100 Miscellaneous

Drugs

Firearm Violations

0

5

10

15

20

Captures30 min

Warnings30 min

Violations30 min

Game Theory

Rand+Human

Controlled

56

User Feedback










49

56



50

Thank you

to sponsors

56

THANK YOU

tambeuscedu


51

56


56

Startup ARMORWAY

568


2007 2011 2013 2014 2014 2015





Infrastructure

Security Games

Green

Security Games

Opportunistic

Crime

Security Games

2014 2014 20152015

569



GLASGOW 63007

5610










Defender 1 2 -1 -3 4 -3 4



5611


1 i

ixts

jq

ix

ijR

Xi Qj

max

1Qj

jq

MqxCaji

Xi

ij)1()(0

Maximize defender

expected utility

Defender mixed

strategy

Adversary best

response

Pita Paruchuri

Adversary response


Defender 1 2 -1 -3 4 -3 4



5612


jq

ix

ijR

Xi Qj


expected utility


Defender 1 2 -1 -3 4 -3 4



5613


119909119894


56



Strateg

y 1

Strateg

y 2

Strateg

y 3

Strateg

y 4

Strateg

y 5

Strateg

y 6



actions


Strategy 1

Strategy 2

Strategy 3

Strategy 4

Strategy 5

Strategy 6

14


56



Column Generation


124 5-10 4-8 hellip -209

378 -8 10 -810 hellip -810

hellip

500 rows

NOT 1041


124 5-10 4-8 hellip -209 Slave

Master

GLOBAL

OPTIMAL


124 5-10 4-8 hellip -209

378 -8 10 -810 hellip -810

15

Jain Kiekintveld

56





-R S Bray (TSA)



16


5617


Jain



20 min

56


18



56


19

56





A

B

C

5 min 10 min 15 min

20

56




A

B

C

5 min 10 min 15 min

Ferry


21

56




A

B

C

5 min 10 min 15 min

Ferry

22



56




A

B

C

5 min 10 min 15 min

Ferry



23

Pr([(B5) (C 10) (C15)]) = 047

Pr([(B5) (C10) (B15)]) =023

NT variables

Pr([(A5) (A10) (B15)]) = 017

Pr([(A5) (A10) (A15)]) = 013


56




A

B

C

5 min 10 min 15 min

Ferry



24

NT variables


56




A

B

C

5 min 10 min 15 min

Ferry

030

017

013


010

007

003


Pr([(B5) (C10) (B15)]) =023


25

NT variables

56

Fisheries

26


2007 2009 2013 2014 2014 2015


56






A

B

C

5 min 10 min 15 min

030

017

013

010

010

27

56






A

B

C

5 min 10 min 15 min

030

015

010

010

007

003

005

28

Jiang Delle Fave

56





A

B

C

5 min 10 min 15 min

030

015

010

010

007

003

005

29



56





defender execution

uncertainty


uncertainty

RECON

BRASS

Monotonic Maximin


30

URAC

Payoff interval

Not point

estimateGMC HUNTER

Bayesian

Robust

Yin Nguyen An

0

05

1

15

5 10

Def

end

ers

EU

Targets

ISG RECON URAC

5631


2007 2011 2013 2014 2014 2015


2014 2014 20152015

56



Fishery Protection

McCarthy Ford Brown

Forest Protection


56

Wildlife Protection


5634



Improve model

Defender calculates

mixed strategy

Defender executes

randomized patrols



56



56




T

j

jxadversary

EU

jxadversary

EU

j

e

eq

1

))((

))((

Adversaryrsquos

probability of

choosing target j


EUPerfect

-3

-25

-2

-15

-1

-05

0


Quantal

Response

Epsilon

robust

Perfect

rational

YangPita

42

56


Bounded Rationality

Penalty3

Reward2

Prob1


SEU

M

j

jxSEU

jxSEU

jadversary

adversary

e

eq

1

)(

)(


Ca

ptu

re

Pro

ba

bil

ity

UWA data from

Uganda

Year 2012

Predicting

poaching

Adversaryrsquos

probability of

choosing target j

Nguyen

43

Reward (animals)

5638





Execute randomized

patrols




10000 emails

Kar

-08

-07

-06

-05

-04

-03

-02

-01

0

01


Def

end

er U

tili

ty

Maximin SUQR

Bayesian SUQR

5639



Animal Density

Co

ver

ag

e

Pro

ba

bil

ity

Human

success

Human

Failure

Increasedecrease

Subjective Utility

-08-07-06-05-04-03-02-01

001


Def

end

er U

tili

ty

Maximin SUQR

Bayesian SUQR SHARP



Execute randomized

patrols


564040




Kar

5641





Malaysia

56







Fang

56




43

Zhang Sinha


56




44

Lab

Evaluation

Simulated

adversary

Human subject

adversaries

Field Evaluation

Patrol quality

Unpredictable Cover



Expert evaluation

Field Evaluation



Capture rates of

real adversaries

56


Co

un

t





Co

un

t

Base Patrol Area

45


56



46




3

35

4

45

5

55

Q1

Q2

Q3

Q4

Q5

Q6

Q7

Q8

Q9

Q1

0

Q11

Q12

Secu

rity

Sco

re

Human Game Theory

56








47

5648




21 days of patrol


Random + Human

Not controlled

0

20

40

60

80

100 Miscellaneous

Drugs

Firearm Violations

0

5

10

15

20

Captures30 min

Warnings30 min

Violations30 min

Game Theory

Rand+Human

Controlled

56

User Feedback










49

56



50

Thank you

to sponsors

56

THANK YOU

tambeuscedu


51

56

Startup ARMORWAY

568


2007 2011 2013 2014 2014 2015





Infrastructure

Security Games

Green

Security Games

Opportunistic

Crime

Security Games

2014 2014 20152015

569



GLASGOW 63007

5610










Defender 1 2 -1 -3 4 -3 4



5611


1 i

ixts

jq

ix

ijR

Xi Qj

max

1Qj

jq

MqxCaji

Xi

ij)1()(0

Maximize defender

expected utility

Defender mixed

strategy

Adversary best

response

Pita Paruchuri

Adversary response


Defender 1 2 -1 -3 4 -3 4



5612


jq

ix

ijR

Xi Qj


expected utility


Defender 1 2 -1 -3 4 -3 4



5613


119909119894


56



Strateg

y 1

Strateg

y 2

Strateg

y 3

Strateg

y 4

Strateg

y 5

Strateg

y 6



actions


Strategy 1

Strategy 2

Strategy 3

Strategy 4

Strategy 5

Strategy 6

14


56



Column Generation


124 5-10 4-8 hellip -209

378 -8 10 -810 hellip -810

hellip

500 rows

NOT 1041


124 5-10 4-8 hellip -209 Slave

Master

GLOBAL

OPTIMAL


124 5-10 4-8 hellip -209

378 -8 10 -810 hellip -810

15

Jain Kiekintveld

56





-R S Bray (TSA)



16


5617


Jain



20 min

56


18



56


19

56





A

B

C

5 min 10 min 15 min

20

56




A

B

C

5 min 10 min 15 min

Ferry


21

56




A

B

C

5 min 10 min 15 min

Ferry

22



56




A

B

C

5 min 10 min 15 min

Ferry



23

Pr([(B5) (C 10) (C15)]) = 047

Pr([(B5) (C10) (B15)]) =023

NT variables

Pr([(A5) (A10) (B15)]) = 017

Pr([(A5) (A10) (A15)]) = 013


56




A

B

C

5 min 10 min 15 min

Ferry



24

NT variables


56




A

B

C

5 min 10 min 15 min

Ferry

030

017

013


010

007

003


Pr([(B5) (C10) (B15)]) =023


25

NT variables

56

Fisheries

26


2007 2009 2013 2014 2014 2015


56






A

B

C

5 min 10 min 15 min

030

017

013

010

010

27

56






A

B

C

5 min 10 min 15 min

030

015

010

010

007

003

005

28

Jiang Delle Fave

56





A

B

C

5 min 10 min 15 min

030

015

010

010

007

003

005

29



56





defender execution

uncertainty


uncertainty

RECON

BRASS

Monotonic Maximin


30

URAC

Payoff interval

Not point

estimateGMC HUNTER

Bayesian

Robust

Yin Nguyen An

0

05

1

15

5 10

Def

end

ers

EU

Targets

ISG RECON URAC

5631


2007 2011 2013 2014 2014 2015


2014 2014 20152015

56



Fishery Protection

McCarthy Ford Brown

Forest Protection


56

Wildlife Protection


5634



Improve model

Defender calculates

mixed strategy

Defender executes

randomized patrols



56



56




T

j

jxadversary

EU

jxadversary

EU

j

e

eq

1

))((

))((

Adversaryrsquos

probability of

choosing target j


EUPerfect

-3

-25

-2

-15

-1

-05

0


Quantal

Response

Epsilon

robust

Perfect

rational

YangPita

42

56


Bounded Rationality

Penalty3

Reward2

Prob1


SEU

M

j

jxSEU

jxSEU

jadversary

adversary

e

eq

1

)(

)(


Ca

ptu

re

Pro

ba

bil

ity

UWA data from

Uganda

Year 2012

Predicting

poaching

Adversaryrsquos

probability of

choosing target j

Nguyen

43

Reward (animals)

5638





Execute randomized

patrols




10000 emails

Kar

-08

-07

-06

-05

-04

-03

-02

-01

0

01


Def

end

er U

tili

ty

Maximin SUQR

Bayesian SUQR

5639



Animal Density

Co

ver

ag

e

Pro

ba

bil

ity

Human

success

Human

Failure

Increasedecrease

Subjective Utility

-08-07-06-05-04-03-02-01

001


Def

end

er U

tili

ty

Maximin SUQR

Bayesian SUQR SHARP



Execute randomized

patrols


564040




Kar

5641





Malaysia

56







Fang

56




43

Zhang Sinha


56




44

Lab

Evaluation

Simulated

adversary

Human subject

adversaries

Field Evaluation

Patrol quality

Unpredictable Cover



Expert evaluation

Field Evaluation



Capture rates of

real adversaries

56


Co

un

t





Co

un

t

Base Patrol Area

45


56



46




3

35

4

45

5

55

Q1

Q2

Q3

Q4

Q5

Q6

Q7

Q8

Q9

Q1

0

Q11

Q12

Secu

rity

Sco

re

Human Game Theory

56








47

5648




21 days of patrol


Random + Human

Not controlled

0

20

40

60

80

100 Miscellaneous

Drugs

Firearm Violations

0

5

10

15

20

Captures30 min

Warnings30 min

Violations30 min

Game Theory

Rand+Human

Controlled

56

User Feedback










49

56



50

Thank you

to sponsors

56

THANK YOU

tambeuscedu


51

568


2007 2011 2013 2014 2014 2015





Infrastructure

Security Games

Green

Security Games

Opportunistic

Crime

Security Games

2014 2014 20152015

569



GLASGOW 63007

5610










Defender 1 2 -1 -3 4 -3 4



5611


1 i

ixts

jq

ix

ijR

Xi Qj

max

1Qj

jq

MqxCaji

Xi

ij)1()(0

Maximize defender

expected utility

Defender mixed

strategy

Adversary best

response

Pita Paruchuri

Adversary response


Defender 1 2 -1 -3 4 -3 4



5612


jq

ix

ijR

Xi Qj


expected utility


Defender 1 2 -1 -3 4 -3 4



5613


119909119894


56



Strateg

y 1

Strateg

y 2

Strateg

y 3

Strateg

y 4

Strateg

y 5

Strateg

y 6



actions


Strategy 1

Strategy 2

Strategy 3

Strategy 4

Strategy 5

Strategy 6

14


56



Column Generation


124 5-10 4-8 hellip -209

378 -8 10 -810 hellip -810

hellip

500 rows

NOT 1041


124 5-10 4-8 hellip -209 Slave

Master

GLOBAL

OPTIMAL


124 5-10 4-8 hellip -209

378 -8 10 -810 hellip -810

15

Jain Kiekintveld

56





-R S Bray (TSA)



16


5617


Jain



20 min

56


18



56


19

56





A

B

C

5 min 10 min 15 min

20

56




A

B

C

5 min 10 min 15 min

Ferry


21

56




A

B

C

5 min 10 min 15 min

Ferry

22



56




A

B

C

5 min 10 min 15 min

Ferry



23

Pr([(B5) (C 10) (C15)]) = 047

Pr([(B5) (C10) (B15)]) =023

NT variables

Pr([(A5) (A10) (B15)]) = 017

Pr([(A5) (A10) (A15)]) = 013


56




A

B

C

5 min 10 min 15 min

Ferry



24

NT variables


56




A

B

C

5 min 10 min 15 min

Ferry

030

017

013


010

007

003


Pr([(B5) (C10) (B15)]) =023


25

NT variables

56

Fisheries

26


2007 2009 2013 2014 2014 2015


56






A

B

C

5 min 10 min 15 min

030

017

013

010

010

27

56






A

B

C

5 min 10 min 15 min

030

015

010

010

007

003

005

28

Jiang Delle Fave

56





A

B

C

5 min 10 min 15 min

030

015

010

010

007

003

005

29



56





defender execution

uncertainty


uncertainty

RECON

BRASS

Monotonic Maximin


30

URAC

Payoff interval

Not point

estimateGMC HUNTER

Bayesian

Robust

Yin Nguyen An

0

05

1

15

5 10

Def

end

ers

EU

Targets

ISG RECON URAC

5631


2007 2011 2013 2014 2014 2015


2014 2014 20152015

56



Fishery Protection

McCarthy Ford Brown

Forest Protection


56

Wildlife Protection


5634



Improve model

Defender calculates

mixed strategy

Defender executes

randomized patrols



56



56




T

j

jxadversary

EU

jxadversary

EU

j

e

eq

1

))((

))((

Adversaryrsquos

probability of

choosing target j


EUPerfect

-3

-25

-2

-15

-1

-05

0


Quantal

Response

Epsilon

robust

Perfect

rational

YangPita

42

56


Bounded Rationality

Penalty3

Reward2

Prob1


SEU

M

j

jxSEU

jxSEU

jadversary

adversary

e

eq

1

)(

)(


Ca

ptu

re

Pro

ba

bil

ity

UWA data from

Uganda

Year 2012

Predicting

poaching

Adversaryrsquos

probability of

choosing target j

Nguyen

43

Reward (animals)

5638





Execute randomized

patrols




10000 emails

Kar

-08

-07

-06

-05

-04

-03

-02

-01

0

01


Def

end

er U

tili

ty

Maximin SUQR

Bayesian SUQR

5639



Animal Density

Co

ver

ag

e

Pro

ba

bil

ity

Human

success

Human

Failure

Increasedecrease

Subjective Utility

-08-07-06-05-04-03-02-01

001


Def

end

er U

tili

ty

Maximin SUQR

Bayesian SUQR SHARP



Execute randomized

patrols


564040




Kar

5641





Malaysia

56







Fang

56




43

Zhang Sinha


56




44

Lab

Evaluation

Simulated

adversary

Human subject

adversaries

Field Evaluation

Patrol quality

Unpredictable Cover



Expert evaluation

Field Evaluation



Capture rates of

real adversaries

56


Co

un

t





Co

un

t

Base Patrol Area

45


56



46




3

35

4

45

5

55

Q1

Q2

Q3

Q4

Q5

Q6

Q7

Q8

Q9

Q1

0

Q11

Q12

Secu

rity

Sco

re

Human Game Theory

56








47

5648




21 days of patrol


Random + Human

Not controlled

0

20

40

60

80

100 Miscellaneous

Drugs

Firearm Violations

0

5

10

15

20

Captures30 min

Warnings30 min

Violations30 min

Game Theory

Rand+Human

Controlled

56

User Feedback










49

56



50

Thank you

to sponsors

56

THANK YOU

tambeuscedu


51

569



GLASGOW 63007

5610










Defender 1 2 -1 -3 4 -3 4



5611


1 i

ixts

jq

ix

ijR

Xi Qj

max

1Qj

jq

MqxCaji

Xi

ij)1()(0

Maximize defender

expected utility

Defender mixed

strategy

Adversary best

response

Pita Paruchuri

Adversary response


Defender 1 2 -1 -3 4 -3 4



5612


jq

ix

ijR

Xi Qj


expected utility


Defender 1 2 -1 -3 4 -3 4



5613


119909119894


56



Strateg

y 1

Strateg

y 2

Strateg

y 3

Strateg

y 4

Strateg

y 5

Strateg

y 6



actions


Strategy 1

Strategy 2

Strategy 3

Strategy 4

Strategy 5

Strategy 6

14


56



Column Generation


124 5-10 4-8 hellip -209

378 -8 10 -810 hellip -810

hellip

500 rows

NOT 1041


124 5-10 4-8 hellip -209 Slave

Master

GLOBAL

OPTIMAL


124 5-10 4-8 hellip -209

378 -8 10 -810 hellip -810

15

Jain Kiekintveld

56





-R S Bray (TSA)



16


5617


Jain



20 min

56


18



56


19

56





A

B

C

5 min 10 min 15 min

20

56




A

B

C

5 min 10 min 15 min

Ferry


21

56




A

B

C

5 min 10 min 15 min

Ferry

22



56




A

B

C

5 min 10 min 15 min

Ferry



23

Pr([(B5) (C 10) (C15)]) = 047

Pr([(B5) (C10) (B15)]) =023

NT variables

Pr([(A5) (A10) (B15)]) = 017

Pr([(A5) (A10) (A15)]) = 013


56




A

B

C

5 min 10 min 15 min

Ferry



24

NT variables


56




A

B

C

5 min 10 min 15 min

Ferry

030

017

013


010

007

003


Pr([(B5) (C10) (B15)]) =023


25

NT variables

56

Fisheries

26


2007 2009 2013 2014 2014 2015


56






A

B

C

5 min 10 min 15 min

030

017

013

010

010

27

56






A

B

C

5 min 10 min 15 min

030

015

010

010

007

003

005

28

Jiang Delle Fave

56





A

B

C

5 min 10 min 15 min

030

015

010

010

007

003

005

29



56





defender execution

uncertainty


uncertainty

RECON

BRASS

Monotonic Maximin


30

URAC

Payoff interval

Not point

estimateGMC HUNTER

Bayesian

Robust

Yin Nguyen An

0

05

1

15

5 10

Def

end

ers

EU

Targets

ISG RECON URAC

5631


2007 2011 2013 2014 2014 2015


2014 2014 20152015

56



Fishery Protection

McCarthy Ford Brown

Forest Protection


56

Wildlife Protection


5634



Improve model

Defender calculates

mixed strategy

Defender executes

randomized patrols



56



56




T

j

jxadversary

EU

jxadversary

EU

j

e

eq

1

))((

))((

Adversaryrsquos

probability of

choosing target j


EUPerfect

-3

-25

-2

-15

-1

-05

0


Quantal

Response

Epsilon

robust

Perfect

rational

YangPita

42

56


Bounded Rationality

Penalty3

Reward2

Prob1


SEU

M

j

jxSEU

jxSEU

jadversary

adversary

e

eq

1

)(

)(


Ca

ptu

re

Pro

ba

bil

ity

UWA data from

Uganda

Year 2012

Predicting

poaching

Adversaryrsquos

probability of

choosing target j

Nguyen

43

Reward (animals)

5638





Execute randomized

patrols




10000 emails

Kar

-08

-07

-06

-05

-04

-03

-02

-01

0

01


Def

end

er U

tili

ty

Maximin SUQR

Bayesian SUQR

5639



Animal Density

Co

ver

ag

e

Pro

ba

bil

ity

Human

success

Human

Failure

Increasedecrease

Subjective Utility

-08-07-06-05-04-03-02-01

001


Def

end

er U

tili

ty

Maximin SUQR

Bayesian SUQR SHARP



Execute randomized

patrols


564040




Kar

5641





Malaysia

56







Fang

56




43

Zhang Sinha


56




44

Lab

Evaluation

Simulated

adversary

Human subject

adversaries

Field Evaluation

Patrol quality

Unpredictable Cover



Expert evaluation

Field Evaluation



Capture rates of

real adversaries

56


Co

un

t





Co

un

t

Base Patrol Area

45


56



46




3

35

4

45

5

55

Q1

Q2

Q3

Q4

Q5

Q6

Q7

Q8

Q9

Q1

0

Q11

Q12

Secu

rity

Sco

re

Human Game Theory

56








47

5648




21 days of patrol


Random + Human

Not controlled

0

20

40

60

80

100 Miscellaneous

Drugs

Firearm Violations

0

5

10

15

20

Captures30 min

Warnings30 min

Violations30 min

Game Theory

Rand+Human

Controlled

56

User Feedback










49

56



50

Thank you

to sponsors

56

THANK YOU

tambeuscedu


51

5610










Defender 1 2 -1 -3 4 -3 4



5611


1 i

ixts

jq

ix

ijR

Xi Qj

max

1Qj

jq

MqxCaji

Xi

ij)1()(0

Maximize defender

expected utility

Defender mixed

strategy

Adversary best

response

Pita Paruchuri

Adversary response


Defender 1 2 -1 -3 4 -3 4



5612


jq

ix

ijR

Xi Qj


expected utility


Defender 1 2 -1 -3 4 -3 4



5613


119909119894


56



Strateg

y 1

Strateg

y 2

Strateg

y 3

Strateg

y 4

Strateg

y 5

Strateg

y 6



actions


Strategy 1

Strategy 2

Strategy 3

Strategy 4

Strategy 5

Strategy 6

14


56



Column Generation


124 5-10 4-8 hellip -209

378 -8 10 -810 hellip -810

hellip

500 rows

NOT 1041


124 5-10 4-8 hellip -209 Slave

Master

GLOBAL

OPTIMAL


124 5-10 4-8 hellip -209

378 -8 10 -810 hellip -810

15

Jain Kiekintveld

56





-R S Bray (TSA)



16


5617


Jain



20 min

56


18



56


19

56





A

B

C

5 min 10 min 15 min

20

56




A

B

C

5 min 10 min 15 min

Ferry


21

56




A

B

C

5 min 10 min 15 min

Ferry

22



56




A

B

C

5 min 10 min 15 min

Ferry



23

Pr([(B5) (C 10) (C15)]) = 047

Pr([(B5) (C10) (B15)]) =023

NT variables

Pr([(A5) (A10) (B15)]) = 017

Pr([(A5) (A10) (A15)]) = 013


56




A

B

C

5 min 10 min 15 min

Ferry



24

NT variables


56




A

B

C

5 min 10 min 15 min

Ferry

030

017

013


010

007

003


Pr([(B5) (C10) (B15)]) =023


25

NT variables

56

Fisheries

26


2007 2009 2013 2014 2014 2015


56






A

B

C

5 min 10 min 15 min

030

017

013

010

010

27

56






A

B

C

5 min 10 min 15 min

030

015

010

010

007

003

005

28

Jiang Delle Fave

56





A

B

C

5 min 10 min 15 min

030

015

010

010

007

003

005

29



56





defender execution

uncertainty


uncertainty

RECON

BRASS

Monotonic Maximin


30

URAC

Payoff interval

Not point

estimateGMC HUNTER

Bayesian

Robust

Yin Nguyen An

0

05

1

15

5 10

Def

end

ers

EU

Targets

ISG RECON URAC

5631


2007 2011 2013 2014 2014 2015


2014 2014 20152015

56



Fishery Protection

McCarthy Ford Brown

Forest Protection


56

Wildlife Protection


5634



Improve model

Defender calculates

mixed strategy

Defender executes

randomized patrols



56



56




T

j

jxadversary

EU

jxadversary

EU

j

e

eq

1

))((

))((

Adversaryrsquos

probability of

choosing target j


EUPerfect

-3

-25

-2

-15

-1

-05

0


Quantal

Response

Epsilon

robust

Perfect

rational

YangPita

42

56


Bounded Rationality

Penalty3

Reward2

Prob1


SEU

M

j

jxSEU

jxSEU

jadversary

adversary

e

eq

1

)(

)(


Ca

ptu

re

Pro

ba

bil

ity

UWA data from

Uganda

Year 2012

Predicting

poaching

Adversaryrsquos

probability of

choosing target j

Nguyen

43

Reward (animals)

5638





Execute randomized

patrols




10000 emails

Kar

-08

-07

-06

-05

-04

-03

-02

-01

0

01


Def

end

er U

tili

ty

Maximin SUQR

Bayesian SUQR

5639



Animal Density

Co

ver

ag

e

Pro

ba

bil

ity

Human

success

Human

Failure

Increasedecrease

Subjective Utility

-08-07-06-05-04-03-02-01

001


Def

end

er U

tili

ty

Maximin SUQR

Bayesian SUQR SHARP



Execute randomized

patrols


564040




Kar

5641





Malaysia

56







Fang

56




43

Zhang Sinha


56




44

Lab

Evaluation

Simulated

adversary

Human subject

adversaries

Field Evaluation

Patrol quality

Unpredictable Cover



Expert evaluation

Field Evaluation



Capture rates of

real adversaries

56


Co

un

t





Co

un

t

Base Patrol Area

45


56



46




3

35

4

45

5

55

Q1

Q2

Q3

Q4

Q5

Q6

Q7

Q8

Q9

Q1

0

Q11

Q12

Secu

rity

Sco

re

Human Game Theory

56








47

5648




21 days of patrol


Random + Human

Not controlled

0

20

40

60

80

100 Miscellaneous

Drugs

Firearm Violations

0

5

10

15

20

Captures30 min

Warnings30 min

Violations30 min

Game Theory

Rand+Human

Controlled

56

User Feedback










49

56



50

Thank you

to sponsors

56

THANK YOU

tambeuscedu


51

5611


1 i

ixts

jq

ix

ijR

Xi Qj

max

1Qj

jq

MqxCaji

Xi

ij)1()(0

Maximize defender

expected utility

Defender mixed

strategy

Adversary best

response

Pita Paruchuri

Adversary response


Defender 1 2 -1 -3 4 -3 4



5612


jq

ix

ijR

Xi Qj


expected utility


Defender 1 2 -1 -3 4 -3 4



5613


119909119894


56



Strateg

y 1

Strateg

y 2

Strateg

y 3

Strateg

y 4

Strateg

y 5

Strateg

y 6



actions


Strategy 1

Strategy 2

Strategy 3

Strategy 4

Strategy 5

Strategy 6

14


56



Column Generation


124 5-10 4-8 hellip -209

378 -8 10 -810 hellip -810

hellip

500 rows

NOT 1041


124 5-10 4-8 hellip -209 Slave

Master

GLOBAL

OPTIMAL


124 5-10 4-8 hellip -209

378 -8 10 -810 hellip -810

15

Jain Kiekintveld

56





-R S Bray (TSA)



16


5617


Jain



20 min

56


18



56


19

56





A

B

C

5 min 10 min 15 min

20

56




A

B

C

5 min 10 min 15 min

Ferry


21

56




A

B

C

5 min 10 min 15 min

Ferry

22



56




A

B

C

5 min 10 min 15 min

Ferry



23

Pr([(B5) (C 10) (C15)]) = 047

Pr([(B5) (C10) (B15)]) =023

NT variables

Pr([(A5) (A10) (B15)]) = 017

Pr([(A5) (A10) (A15)]) = 013


56




A

B

C

5 min 10 min 15 min

Ferry



24

NT variables


56




A

B

C

5 min 10 min 15 min

Ferry

030

017

013


010

007

003


Pr([(B5) (C10) (B15)]) =023


25

NT variables

56

Fisheries

26


2007 2009 2013 2014 2014 2015


56






A

B

C

5 min 10 min 15 min

030

017

013

010

010

27

56






A

B

C

5 min 10 min 15 min

030

015

010

010

007

003

005

28

Jiang Delle Fave

56





A

B

C

5 min 10 min 15 min

030

015

010

010

007

003

005

29



56





defender execution

uncertainty


uncertainty

RECON

BRASS

Monotonic Maximin


30

URAC

Payoff interval

Not point

estimateGMC HUNTER

Bayesian

Robust

Yin Nguyen An

0

05

1

15

5 10

Def

end

ers

EU

Targets

ISG RECON URAC

5631


2007 2011 2013 2014 2014 2015


2014 2014 20152015

56



Fishery Protection

McCarthy Ford Brown

Forest Protection


56

Wildlife Protection


5634



Improve model

Defender calculates

mixed strategy

Defender executes

randomized patrols



56



56




T

j

jxadversary

EU

jxadversary

EU

j

e

eq

1

))((

))((

Adversaryrsquos

probability of

choosing target j


EUPerfect

-3

-25

-2

-15

-1

-05

0


Quantal

Response

Epsilon

robust

Perfect

rational

YangPita

42

56


Bounded Rationality

Penalty3

Reward2

Prob1


SEU

M

j

jxSEU

jxSEU

jadversary

adversary

e

eq

1

)(

)(


Ca

ptu

re

Pro

ba

bil

ity

UWA data from

Uganda

Year 2012

Predicting

poaching

Adversaryrsquos

probability of

choosing target j

Nguyen

43

Reward (animals)

5638





Execute randomized

patrols




10000 emails

Kar

-08

-07

-06

-05

-04

-03

-02

-01

0

01


Def

end

er U

tili

ty

Maximin SUQR

Bayesian SUQR

5639



Animal Density

Co

ver

ag

e

Pro

ba

bil

ity

Human

success

Human

Failure

Increasedecrease

Subjective Utility

-08-07-06-05-04-03-02-01

001


Def

end

er U

tili

ty

Maximin SUQR

Bayesian SUQR SHARP



Execute randomized

patrols


564040




Kar

5641





Malaysia

56







Fang

56




43

Zhang Sinha


56




44

Lab

Evaluation

Simulated

adversary

Human subject

adversaries

Field Evaluation

Patrol quality

Unpredictable Cover



Expert evaluation

Field Evaluation



Capture rates of

real adversaries

56


Co

un

t





Co

un

t

Base Patrol Area

45


56



46




3

35

4

45

5

55

Q1

Q2

Q3

Q4

Q5

Q6

Q7

Q8

Q9

Q1

0

Q11

Q12

Secu

rity

Sco

re

Human Game Theory

56








47

5648




21 days of patrol


Random + Human

Not controlled

0

20

40

60

80

100 Miscellaneous

Drugs

Firearm Violations

0

5

10

15

20

Captures30 min

Warnings30 min

Violations30 min

Game Theory

Rand+Human

Controlled

56

User Feedback










49

56



50

Thank you

to sponsors

56

THANK YOU

tambeuscedu


51

5612


jq

ix

ijR

Xi Qj


expected utility


Defender 1 2 -1 -3 4 -3 4



5613


119909119894


56



Strateg

y 1

Strateg

y 2

Strateg

y 3

Strateg

y 4

Strateg

y 5

Strateg

y 6



actions


Strategy 1

Strategy 2

Strategy 3

Strategy 4

Strategy 5

Strategy 6

14


56



Column Generation


124 5-10 4-8 hellip -209

378 -8 10 -810 hellip -810

hellip

500 rows

NOT 1041


124 5-10 4-8 hellip -209 Slave

Master

GLOBAL

OPTIMAL


124 5-10 4-8 hellip -209

378 -8 10 -810 hellip -810

15

Jain Kiekintveld

56





-R S Bray (TSA)



16


5617


Jain



20 min

56


18



56


19

56





A

B

C

5 min 10 min 15 min

20

56




A

B

C

5 min 10 min 15 min

Ferry


21

56




A

B

C

5 min 10 min 15 min

Ferry

22



56




A

B

C

5 min 10 min 15 min

Ferry



23

Pr([(B5) (C 10) (C15)]) = 047

Pr([(B5) (C10) (B15)]) =023

NT variables

Pr([(A5) (A10) (B15)]) = 017

Pr([(A5) (A10) (A15)]) = 013


56




A

B

C

5 min 10 min 15 min

Ferry



24

NT variables


56




A

B

C

5 min 10 min 15 min

Ferry

030

017

013


010

007

003


Pr([(B5) (C10) (B15)]) =023


25

NT variables

56

Fisheries

26


2007 2009 2013 2014 2014 2015


56






A

B

C

5 min 10 min 15 min

030

017

013

010

010

27

56






A

B

C

5 min 10 min 15 min

030

015

010

010

007

003

005

28

Jiang Delle Fave

56





A

B

C

5 min 10 min 15 min

030

015

010

010

007

003

005

29



56





defender execution

uncertainty


uncertainty

RECON

BRASS

Monotonic Maximin


30

URAC

Payoff interval

Not point

estimateGMC HUNTER

Bayesian

Robust

Yin Nguyen An

0

05

1

15

5 10

Def

end

ers

EU

Targets

ISG RECON URAC

5631


2007 2011 2013 2014 2014 2015


2014 2014 20152015

56



Fishery Protection

McCarthy Ford Brown

Forest Protection


56

Wildlife Protection


5634



Improve model

Defender calculates

mixed strategy

Defender executes

randomized patrols



56



56




T

j

jxadversary

EU

jxadversary

EU

j

e

eq

1

))((

))((

Adversaryrsquos

probability of

choosing target j


EUPerfect

-3

-25

-2

-15

-1

-05

0


Quantal

Response

Epsilon

robust

Perfect

rational

YangPita

42

56


Bounded Rationality

Penalty3

Reward2

Prob1


SEU

M

j

jxSEU

jxSEU

jadversary

adversary

e

eq

1

)(

)(


Ca

ptu

re

Pro

ba

bil

ity

UWA data from

Uganda

Year 2012

Predicting

poaching

Adversaryrsquos

probability of

choosing target j

Nguyen

43

Reward (animals)

5638





Execute randomized

patrols




10000 emails

Kar

-08

-07

-06

-05

-04

-03

-02

-01

0

01


Def

end

er U

tili

ty

Maximin SUQR

Bayesian SUQR

5639



Animal Density

Co

ver

ag

e

Pro

ba

bil

ity

Human

success

Human

Failure

Increasedecrease

Subjective Utility

-08-07-06-05-04-03-02-01

001


Def

end

er U

tili

ty

Maximin SUQR

Bayesian SUQR SHARP



Execute randomized

patrols


564040




Kar

5641





Malaysia

56







Fang

56




43

Zhang Sinha


56




44

Lab

Evaluation

Simulated

adversary

Human subject

adversaries

Field Evaluation

Patrol quality

Unpredictable Cover



Expert evaluation

Field Evaluation



Capture rates of

real adversaries

56


Co

un

t





Co

un

t

Base Patrol Area

45


56



46




3

35

4

45

5

55

Q1

Q2

Q3

Q4

Q5

Q6

Q7

Q8

Q9

Q1

0

Q11

Q12

Secu

rity

Sco

re

Human Game Theory

56








47

5648




21 days of patrol


Random + Human

Not controlled

0

20

40

60

80

100 Miscellaneous

Drugs

Firearm Violations

0

5

10

15

20

Captures30 min

Warnings30 min

Violations30 min

Game Theory

Rand+Human

Controlled

56

User Feedback










49

56



50

Thank you

to sponsors

56

THANK YOU

tambeuscedu


51

5613


119909119894


56



Strateg

y 1

Strateg

y 2

Strateg

y 3

Strateg

y 4

Strateg

y 5

Strateg

y 6



actions


Strategy 1

Strategy 2

Strategy 3

Strategy 4

Strategy 5

Strategy 6

14


56



Column Generation


124 5-10 4-8 hellip -209

378 -8 10 -810 hellip -810

hellip

500 rows

NOT 1041


124 5-10 4-8 hellip -209 Slave

Master

GLOBAL

OPTIMAL


124 5-10 4-8 hellip -209

378 -8 10 -810 hellip -810

15

Jain Kiekintveld

56





-R S Bray (TSA)



16


5617


Jain



20 min

56


18



56


19

56





A

B

C

5 min 10 min 15 min

20

56




A

B

C

5 min 10 min 15 min

Ferry


21

56




A

B

C

5 min 10 min 15 min

Ferry

22



56




A

B

C

5 min 10 min 15 min

Ferry



23

Pr([(B5) (C 10) (C15)]) = 047

Pr([(B5) (C10) (B15)]) =023

NT variables

Pr([(A5) (A10) (B15)]) = 017

Pr([(A5) (A10) (A15)]) = 013


56




A

B

C

5 min 10 min 15 min

Ferry



24

NT variables


56




A

B

C

5 min 10 min 15 min

Ferry

030

017

013


010

007

003


Pr([(B5) (C10) (B15)]) =023


25

NT variables

56

Fisheries

26


2007 2009 2013 2014 2014 2015


56






A

B

C

5 min 10 min 15 min

030

017

013

010

010

27

56






A

B

C

5 min 10 min 15 min

030

015

010

010

007

003

005

28

Jiang Delle Fave

56





A

B

C

5 min 10 min 15 min

030

015

010

010

007

003

005

29



56





defender execution

uncertainty


uncertainty

RECON

BRASS

Monotonic Maximin


30

URAC

Payoff interval

Not point

estimateGMC HUNTER

Bayesian

Robust

Yin Nguyen An

0

05

1

15

5 10

Def

end

ers

EU

Targets

ISG RECON URAC

5631


2007 2011 2013 2014 2014 2015


2014 2014 20152015

56



Fishery Protection

McCarthy Ford Brown

Forest Protection


56

Wildlife Protection


5634



Improve model

Defender calculates

mixed strategy

Defender executes

randomized patrols



56



56




T

j

jxadversary

EU

jxadversary

EU

j

e

eq

1

))((

))((

Adversaryrsquos

probability of

choosing target j


EUPerfect

-3

-25

-2

-15

-1

-05

0


Quantal

Response

Epsilon

robust

Perfect

rational

YangPita

42

56


Bounded Rationality

Penalty3

Reward2

Prob1


SEU

M

j

jxSEU

jxSEU

jadversary

adversary

e

eq

1

)(

)(


Ca

ptu

re

Pro

ba

bil

ity

UWA data from

Uganda

Year 2012

Predicting

poaching

Adversaryrsquos

probability of

choosing target j

Nguyen

43

Reward (animals)

5638





Execute randomized

patrols




10000 emails

Kar

-08

-07

-06

-05

-04

-03

-02

-01

0

01


Def

end

er U

tili

ty

Maximin SUQR

Bayesian SUQR

5639



Animal Density

Co

ver

ag

e

Pro

ba

bil

ity

Human

success

Human

Failure

Increasedecrease

Subjective Utility

-08-07-06-05-04-03-02-01

001


Def

end

er U

tili

ty

Maximin SUQR

Bayesian SUQR SHARP



Execute randomized

patrols


564040




Kar

5641





Malaysia

56







Fang

56




43

Zhang Sinha


56




44

Lab

Evaluation

Simulated

adversary

Human subject

adversaries

Field Evaluation

Patrol quality

Unpredictable Cover



Expert evaluation

Field Evaluation



Capture rates of

real adversaries

56


Co

un

t





Co

un

t

Base Patrol Area

45


56



46




3

35

4

45

5

55

Q1

Q2

Q3

Q4

Q5

Q6

Q7

Q8

Q9

Q1

0

Q11

Q12

Secu

rity

Sco

re

Human Game Theory

56








47

5648




21 days of patrol


Random + Human

Not controlled

0

20

40

60

80

100 Miscellaneous

Drugs

Firearm Violations

0

5

10

15

20

Captures30 min

Warnings30 min

Violations30 min

Game Theory

Rand+Human

Controlled

56

User Feedback










49

56



50

Thank you

to sponsors

56

THANK YOU

tambeuscedu


51

56



Strateg

y 1

Strateg

y 2

Strateg

y 3

Strateg

y 4

Strateg

y 5

Strateg

y 6



actions


Strategy 1

Strategy 2

Strategy 3

Strategy 4

Strategy 5

Strategy 6

14


56



Column Generation


124 5-10 4-8 hellip -209

378 -8 10 -810 hellip -810

hellip

500 rows

NOT 1041


124 5-10 4-8 hellip -209 Slave

Master

GLOBAL

OPTIMAL


124 5-10 4-8 hellip -209

378 -8 10 -810 hellip -810

15

Jain Kiekintveld

56





-R S Bray (TSA)



16


5617


Jain



20 min

56


18



56


19

56





A

B

C

5 min 10 min 15 min

20

56




A

B

C

5 min 10 min 15 min

Ferry


21

56




A

B

C

5 min 10 min 15 min

Ferry

22



56




A

B

C

5 min 10 min 15 min

Ferry



23

Pr([(B5) (C 10) (C15)]) = 047

Pr([(B5) (C10) (B15)]) =023

NT variables

Pr([(A5) (A10) (B15)]) = 017

Pr([(A5) (A10) (A15)]) = 013


56




A

B

C

5 min 10 min 15 min

Ferry



24

NT variables


56




A

B

C

5 min 10 min 15 min

Ferry

030

017

013


010

007

003


Pr([(B5) (C10) (B15)]) =023


25

NT variables

56

Fisheries

26


2007 2009 2013 2014 2014 2015


56






A

B

C

5 min 10 min 15 min

030

017

013

010

010

27

56






A

B

C

5 min 10 min 15 min

030

015

010

010

007

003

005

28

Jiang Delle Fave

56





A

B

C

5 min 10 min 15 min

030

015

010

010

007

003

005

29



56





defender execution

uncertainty


uncertainty

RECON

BRASS

Monotonic Maximin


30

URAC

Payoff interval

Not point

estimateGMC HUNTER

Bayesian

Robust

Yin Nguyen An

0

05

1

15

5 10

Def

end

ers

EU

Targets

ISG RECON URAC

5631


2007 2011 2013 2014 2014 2015


2014 2014 20152015

56



Fishery Protection

McCarthy Ford Brown

Forest Protection


56

Wildlife Protection


5634



Improve model

Defender calculates

mixed strategy

Defender executes

randomized patrols



56



56




T

j

jxadversary

EU

jxadversary

EU

j

e

eq

1

))((

))((

Adversaryrsquos

probability of

choosing target j


EUPerfect

-3

-25

-2

-15

-1

-05

0


Quantal

Response

Epsilon

robust

Perfect

rational

YangPita

42

56


Bounded Rationality

Penalty3

Reward2

Prob1


SEU

M

j

jxSEU

jxSEU

jadversary

adversary

e

eq

1

)(

)(


Ca

ptu

re

Pro

ba

bil

ity

UWA data from

Uganda

Year 2012

Predicting

poaching

Adversaryrsquos

probability of

choosing target j

Nguyen

43

Reward (animals)

5638





Execute randomized

patrols




10000 emails

Kar

-08

-07

-06

-05

-04

-03

-02

-01

0

01


Def

end

er U

tili

ty

Maximin SUQR

Bayesian SUQR

5639



Animal Density

Co

ver

ag

e

Pro

ba

bil

ity

Human

success

Human

Failure

Increasedecrease

Subjective Utility

-08-07-06-05-04-03-02-01

001


Def

end

er U

tili

ty

Maximin SUQR

Bayesian SUQR SHARP



Execute randomized

patrols


564040




Kar

5641





Malaysia

56







Fang

56




43

Zhang Sinha


56




44

Lab

Evaluation

Simulated

adversary

Human subject

adversaries

Field Evaluation

Patrol quality

Unpredictable Cover



Expert evaluation

Field Evaluation



Capture rates of

real adversaries

56


Co

un

t





Co

un

t

Base Patrol Area

45


56



46




3

35

4

45

5

55

Q1

Q2

Q3

Q4

Q5

Q6

Q7

Q8

Q9

Q1

0

Q11

Q12

Secu

rity

Sco

re

Human Game Theory

56








47

5648




21 days of patrol


Random + Human

Not controlled

0

20

40

60

80

100 Miscellaneous

Drugs

Firearm Violations

0

5

10

15

20

Captures30 min

Warnings30 min

Violations30 min

Game Theory

Rand+Human

Controlled

56

User Feedback










49

56



50

Thank you

to sponsors

56

THANK YOU

tambeuscedu


51

56



Column Generation


124 5-10 4-8 hellip -209

378 -8 10 -810 hellip -810

hellip

500 rows

NOT 1041


124 5-10 4-8 hellip -209 Slave

Master

GLOBAL

OPTIMAL


124 5-10 4-8 hellip -209

378 -8 10 -810 hellip -810

15

Jain Kiekintveld

56





-R S Bray (TSA)



16


5617


Jain



20 min

56


18



56


19

56





A

B

C

5 min 10 min 15 min

20

56




A

B

C

5 min 10 min 15 min

Ferry


21

56




A

B

C

5 min 10 min 15 min

Ferry

22



56




A

B

C

5 min 10 min 15 min

Ferry



23

Pr([(B5) (C 10) (C15)]) = 047

Pr([(B5) (C10) (B15)]) =023

NT variables

Pr([(A5) (A10) (B15)]) = 017

Pr([(A5) (A10) (A15)]) = 013


56




A

B

C

5 min 10 min 15 min

Ferry



24

NT variables


56




A

B

C

5 min 10 min 15 min

Ferry

030

017

013


010

007

003


Pr([(B5) (C10) (B15)]) =023


25

NT variables

56

Fisheries

26


2007 2009 2013 2014 2014 2015


56






A

B

C

5 min 10 min 15 min

030

017

013

010

010

27

56






A

B

C

5 min 10 min 15 min

030

015

010

010

007

003

005

28

Jiang Delle Fave

56





A

B

C

5 min 10 min 15 min

030

015

010

010

007

003

005

29



56





defender execution

uncertainty


uncertainty

RECON

BRASS

Monotonic Maximin


30

URAC

Payoff interval

Not point

estimateGMC HUNTER

Bayesian

Robust

Yin Nguyen An

0

05

1

15

5 10

Def

end

ers

EU

Targets

ISG RECON URAC

5631


2007 2011 2013 2014 2014 2015


2014 2014 20152015

56



Fishery Protection

McCarthy Ford Brown

Forest Protection


56

Wildlife Protection


5634



Improve model

Defender calculates

mixed strategy

Defender executes

randomized patrols



56



56




T

j

jxadversary

EU

jxadversary

EU

j

e

eq

1

))((

))((

Adversaryrsquos

probability of

choosing target j


EUPerfect

-3

-25

-2

-15

-1

-05

0


Quantal

Response

Epsilon

robust

Perfect

rational

YangPita

42

56


Bounded Rationality

Penalty3

Reward2

Prob1


SEU

M

j

jxSEU

jxSEU

jadversary

adversary

e

eq

1

)(

)(


Ca

ptu

re

Pro

ba

bil

ity

UWA data from

Uganda

Year 2012

Predicting

poaching

Adversaryrsquos

probability of

choosing target j

Nguyen

43

Reward (animals)

5638





Execute randomized

patrols




10000 emails

Kar

-08

-07

-06

-05

-04

-03

-02

-01

0

01


Def

end

er U

tili

ty

Maximin SUQR

Bayesian SUQR

5639



Animal Density

Co

ver

ag

e

Pro

ba

bil

ity

Human

success

Human

Failure

Increasedecrease

Subjective Utility

-08-07-06-05-04-03-02-01

001


Def

end

er U

tili

ty

Maximin SUQR

Bayesian SUQR SHARP



Execute randomized

patrols


564040




Kar

5641





Malaysia

56







Fang

56




43

Zhang Sinha


56




44

Lab

Evaluation

Simulated

adversary

Human subject

adversaries

Field Evaluation

Patrol quality

Unpredictable Cover



Expert evaluation

Field Evaluation



Capture rates of

real adversaries

56


Co

un

t





Co

un

t

Base Patrol Area

45


56



46




3

35

4

45

5

55

Q1

Q2

Q3

Q4

Q5

Q6

Q7

Q8

Q9

Q1

0

Q11

Q12

Secu

rity

Sco

re

Human Game Theory

56








47

5648




21 days of patrol


Random + Human

Not controlled

0

20

40

60

80

100 Miscellaneous

Drugs

Firearm Violations

0

5

10

15

20

Captures30 min

Warnings30 min

Violations30 min

Game Theory

Rand+Human

Controlled

56

User Feedback










49

56



50

Thank you

to sponsors

56

THANK YOU

tambeuscedu


51

56





-R S Bray (TSA)



16


5617


Jain



20 min

56


18



56


19

56





A

B

C

5 min 10 min 15 min

20

56




A

B

C

5 min 10 min 15 min

Ferry


21

56




A

B

C

5 min 10 min 15 min

Ferry

22



56




A

B

C

5 min 10 min 15 min

Ferry



23

Pr([(B5) (C 10) (C15)]) = 047

Pr([(B5) (C10) (B15)]) =023

NT variables

Pr([(A5) (A10) (B15)]) = 017

Pr([(A5) (A10) (A15)]) = 013


56




A

B

C

5 min 10 min 15 min

Ferry



24

NT variables


56




A

B

C

5 min 10 min 15 min

Ferry

030

017

013


010

007

003


Pr([(B5) (C10) (B15)]) =023


25

NT variables

56

Fisheries

26


2007 2009 2013 2014 2014 2015


56






A

B

C

5 min 10 min 15 min

030

017

013

010

010

27

56






A

B

C

5 min 10 min 15 min

030

015

010

010

007

003

005

28

Jiang Delle Fave

56





A

B

C

5 min 10 min 15 min

030

015

010

010

007

003

005

29



56





defender execution

uncertainty


uncertainty

RECON

BRASS

Monotonic Maximin


30

URAC

Payoff interval

Not point

estimateGMC HUNTER

Bayesian

Robust

Yin Nguyen An

0

05

1

15

5 10

Def

end

ers

EU

Targets

ISG RECON URAC

5631


2007 2011 2013 2014 2014 2015


2014 2014 20152015

56



Fishery Protection

McCarthy Ford Brown

Forest Protection


56

Wildlife Protection


5634



Improve model

Defender calculates

mixed strategy

Defender executes

randomized patrols



56



56




T

j

jxadversary

EU

jxadversary

EU

j

e

eq

1

))((

))((

Adversaryrsquos

probability of

choosing target j


EUPerfect

-3

-25

-2

-15

-1

-05

0


Quantal

Response

Epsilon

robust

Perfect

rational

YangPita

42

56


Bounded Rationality

Penalty3

Reward2

Prob1


SEU

M

j

jxSEU

jxSEU

jadversary

adversary

e

eq

1

)(

)(


Ca

ptu

re

Pro

ba

bil

ity

UWA data from

Uganda

Year 2012

Predicting

poaching

Adversaryrsquos

probability of

choosing target j

Nguyen

43

Reward (animals)

5638





Execute randomized

patrols




10000 emails

Kar

-08

-07

-06

-05

-04

-03

-02

-01

0

01


Def

end

er U

tili

ty

Maximin SUQR

Bayesian SUQR

5639



Animal Density

Co

ver

ag

e

Pro

ba

bil

ity

Human

success

Human

Failure

Increasedecrease

Subjective Utility

-08-07-06-05-04-03-02-01

001


Def

end

er U

tili

ty

Maximin SUQR

Bayesian SUQR SHARP



Execute randomized

patrols


564040




Kar

5641





Malaysia

56







Fang

56




43

Zhang Sinha


56




44

Lab

Evaluation

Simulated

adversary

Human subject

adversaries

Field Evaluation

Patrol quality

Unpredictable Cover



Expert evaluation

Field Evaluation



Capture rates of

real adversaries

56


Co

un

t





Co

un

t

Base Patrol Area

45


56



46




3

35

4

45

5

55

Q1

Q2

Q3

Q4

Q5

Q6

Q7

Q8

Q9

Q1

0

Q11

Q12

Secu

rity

Sco

re

Human Game Theory

56








47

5648




21 days of patrol


Random + Human

Not controlled

0

20

40

60

80

100 Miscellaneous

Drugs

Firearm Violations

0

5

10

15

20

Captures30 min

Warnings30 min

Violations30 min

Game Theory

Rand+Human

Controlled

56

User Feedback










49

56



50

Thank you

to sponsors

56

THANK YOU

tambeuscedu


51

5617


Jain



20 min

56


18



56


19

56





A

B

C

5 min 10 min 15 min

20

56




A

B

C

5 min 10 min 15 min

Ferry


21

56




A

B

C

5 min 10 min 15 min

Ferry

22



56




A

B

C

5 min 10 min 15 min

Ferry



23

Pr([(B5) (C 10) (C15)]) = 047

Pr([(B5) (C10) (B15)]) =023

NT variables

Pr([(A5) (A10) (B15)]) = 017

Pr([(A5) (A10) (A15)]) = 013


56




A

B

C

5 min 10 min 15 min

Ferry



24

NT variables


56




A

B

C

5 min 10 min 15 min

Ferry

030

017

013


010

007

003


Pr([(B5) (C10) (B15)]) =023


25

NT variables

56

Fisheries

26


2007 2009 2013 2014 2014 2015


56






A

B

C

5 min 10 min 15 min

030

017

013

010

010

27

56






A

B

C

5 min 10 min 15 min

030

015

010

010

007

003

005

28

Jiang Delle Fave

56





A

B

C

5 min 10 min 15 min

030

015

010

010

007

003

005

29



56





defender execution

uncertainty


uncertainty

RECON

BRASS

Monotonic Maximin


30

URAC

Payoff interval

Not point

estimateGMC HUNTER

Bayesian

Robust

Yin Nguyen An

0

05

1

15

5 10

Def

end

ers

EU

Targets

ISG RECON URAC

5631


2007 2011 2013 2014 2014 2015


2014 2014 20152015

56



Fishery Protection

McCarthy Ford Brown

Forest Protection


56

Wildlife Protection


5634



Improve model

Defender calculates

mixed strategy

Defender executes

randomized patrols



56



56




T

j

jxadversary

EU

jxadversary

EU

j

e

eq

1

))((

))((

Adversaryrsquos

probability of

choosing target j


EUPerfect

-3

-25

-2

-15

-1

-05

0


Quantal

Response

Epsilon

robust

Perfect

rational

YangPita

42

56


Bounded Rationality

Penalty3

Reward2

Prob1


SEU

M

j

jxSEU

jxSEU

jadversary

adversary

e

eq

1

)(

)(


Ca

ptu

re

Pro

ba

bil

ity

UWA data from

Uganda

Year 2012

Predicting

poaching

Adversaryrsquos

probability of

choosing target j

Nguyen

43

Reward (animals)

5638





Execute randomized

patrols




10000 emails

Kar

-08

-07

-06

-05

-04

-03

-02

-01

0

01


Def

end

er U

tili

ty

Maximin SUQR

Bayesian SUQR

5639



Animal Density

Co

ver

ag

e

Pro

ba

bil

ity

Human

success

Human

Failure

Increasedecrease

Subjective Utility

-08-07-06-05-04-03-02-01

001


Def

end

er U

tili

ty

Maximin SUQR

Bayesian SUQR SHARP



Execute randomized

patrols


564040




Kar

5641





Malaysia

56







Fang

56




43

Zhang Sinha


56




44

Lab

Evaluation

Simulated

adversary

Human subject

adversaries

Field Evaluation

Patrol quality

Unpredictable Cover



Expert evaluation

Field Evaluation



Capture rates of

real adversaries

56


Co

un

t





Co

un

t

Base Patrol Area

45


56



46




3

35

4

45

5

55

Q1

Q2

Q3

Q4

Q5

Q6

Q7

Q8

Q9

Q1

0

Q11

Q12

Secu

rity

Sco

re

Human Game Theory

56








47

5648




21 days of patrol


Random + Human

Not controlled

0

20

40

60

80

100 Miscellaneous

Drugs

Firearm Violations

0

5

10

15

20

Captures30 min

Warnings30 min

Violations30 min

Game Theory

Rand+Human

Controlled

56

User Feedback










49

56



50

Thank you

to sponsors

56

THANK YOU

tambeuscedu


51

56


18



56


19

56





A

B

C

5 min 10 min 15 min

20

56




A

B

C

5 min 10 min 15 min

Ferry


21

56




A

B

C

5 min 10 min 15 min

Ferry

22



56




A

B

C

5 min 10 min 15 min

Ferry



23

Pr([(B5) (C 10) (C15)]) = 047

Pr([(B5) (C10) (B15)]) =023

NT variables

Pr([(A5) (A10) (B15)]) = 017

Pr([(A5) (A10) (A15)]) = 013


56




A

B

C

5 min 10 min 15 min

Ferry



24

NT variables


56




A

B

C

5 min 10 min 15 min

Ferry

030

017

013


010

007

003


Pr([(B5) (C10) (B15)]) =023


25

NT variables

56

Fisheries

26


2007 2009 2013 2014 2014 2015


56






A

B

C

5 min 10 min 15 min

030

017

013

010

010

27

56






A

B

C

5 min 10 min 15 min

030

015

010

010

007

003

005

28

Jiang Delle Fave

56





A

B

C

5 min 10 min 15 min

030

015

010

010

007

003

005

29



56





defender execution

uncertainty


uncertainty

RECON

BRASS

Monotonic Maximin


30

URAC

Payoff interval

Not point

estimateGMC HUNTER

Bayesian

Robust

Yin Nguyen An

0

05

1

15

5 10

Def

end

ers

EU

Targets

ISG RECON URAC

5631


2007 2011 2013 2014 2014 2015


2014 2014 20152015

56



Fishery Protection

McCarthy Ford Brown

Forest Protection


56

Wildlife Protection


5634



Improve model

Defender calculates

mixed strategy

Defender executes

randomized patrols



56



56




T

j

jxadversary

EU

jxadversary

EU

j

e

eq

1

))((

))((

Adversaryrsquos

probability of

choosing target j


EUPerfect

-3

-25

-2

-15

-1

-05

0


Quantal

Response

Epsilon

robust

Perfect

rational

YangPita

42

56


Bounded Rationality

Penalty3

Reward2

Prob1


SEU

M

j

jxSEU

jxSEU

jadversary

adversary

e

eq

1

)(

)(


Ca

ptu

re

Pro

ba

bil

ity

UWA data from

Uganda

Year 2012

Predicting

poaching

Adversaryrsquos

probability of

choosing target j

Nguyen

43

Reward (animals)

5638





Execute randomized

patrols




10000 emails

Kar

-08

-07

-06

-05

-04

-03

-02

-01

0

01


Def

end

er U

tili

ty

Maximin SUQR

Bayesian SUQR

5639



Animal Density

Co

ver

ag

e

Pro

ba

bil

ity

Human

success

Human

Failure

Increasedecrease

Subjective Utility

-08-07-06-05-04-03-02-01

001


Def

end

er U

tili

ty

Maximin SUQR

Bayesian SUQR SHARP



Execute randomized

patrols


564040




Kar

5641





Malaysia

56







Fang

56




43

Zhang Sinha


56




44

Lab

Evaluation

Simulated

adversary

Human subject

adversaries

Field Evaluation

Patrol quality

Unpredictable Cover



Expert evaluation

Field Evaluation



Capture rates of

real adversaries

56


Co

un

t





Co

un

t

Base Patrol Area

45


56



46




3

35

4

45

5

55

Q1

Q2

Q3

Q4

Q5

Q6

Q7

Q8

Q9

Q1

0

Q11

Q12

Secu

rity

Sco

re

Human Game Theory

56








47

5648




21 days of patrol


Random + Human

Not controlled

0

20

40

60

80

100 Miscellaneous

Drugs

Firearm Violations

0

5

10

15

20

Captures30 min

Warnings30 min

Violations30 min

Game Theory

Rand+Human

Controlled

56

User Feedback










49

56



50

Thank you

to sponsors

56

THANK YOU

tambeuscedu


51

56


19

56





A

B

C

5 min 10 min 15 min

20

56




A

B

C

5 min 10 min 15 min

Ferry


21

56




A

B

C

5 min 10 min 15 min

Ferry

22



56




A

B

C

5 min 10 min 15 min

Ferry



23

Pr([(B5) (C 10) (C15)]) = 047

Pr([(B5) (C10) (B15)]) =023

NT variables

Pr([(A5) (A10) (B15)]) = 017

Pr([(A5) (A10) (A15)]) = 013


56




A

B

C

5 min 10 min 15 min

Ferry



24

NT variables


56




A

B

C

5 min 10 min 15 min

Ferry

030

017

013


010

007

003


Pr([(B5) (C10) (B15)]) =023


25

NT variables

56

Fisheries

26


2007 2009 2013 2014 2014 2015


56






A

B

C

5 min 10 min 15 min

030

017

013

010

010

27

56






A

B

C

5 min 10 min 15 min

030

015

010

010

007

003

005

28

Jiang Delle Fave

56





A

B

C

5 min 10 min 15 min

030

015

010

010

007

003

005

29



56





defender execution

uncertainty


uncertainty

RECON

BRASS

Monotonic Maximin


30

URAC

Payoff interval

Not point

estimateGMC HUNTER

Bayesian

Robust

Yin Nguyen An

0

05

1

15

5 10

Def

end

ers

EU

Targets

ISG RECON URAC

5631


2007 2011 2013 2014 2014 2015


2014 2014 20152015

56



Fishery Protection

McCarthy Ford Brown

Forest Protection


56

Wildlife Protection


5634



Improve model

Defender calculates

mixed strategy

Defender executes

randomized patrols



56



56




T

j

jxadversary

EU

jxadversary

EU

j

e

eq

1

))((

))((

Adversaryrsquos

probability of

choosing target j


EUPerfect

-3

-25

-2

-15

-1

-05

0


Quantal

Response

Epsilon

robust

Perfect

rational

YangPita

42

56


Bounded Rationality

Penalty3

Reward2

Prob1


SEU

M

j

jxSEU

jxSEU

jadversary

adversary

e

eq

1

)(

)(


Ca

ptu

re

Pro

ba

bil

ity

UWA data from

Uganda

Year 2012

Predicting

poaching

Adversaryrsquos

probability of

choosing target j

Nguyen

43

Reward (animals)

5638





Execute randomized

patrols




10000 emails

Kar

-08

-07

-06

-05

-04

-03

-02

-01

0

01


Def

end

er U

tili

ty

Maximin SUQR

Bayesian SUQR

5639



Animal Density

Co

ver

ag

e

Pro

ba

bil

ity

Human

success

Human

Failure

Increasedecrease

Subjective Utility

-08-07-06-05-04-03-02-01

001


Def

end

er U

tili

ty

Maximin SUQR

Bayesian SUQR SHARP



Execute randomized

patrols


564040




Kar

5641





Malaysia

56







Fang

56




43

Zhang Sinha


56




44

Lab

Evaluation

Simulated

adversary

Human subject

adversaries

Field Evaluation

Patrol quality

Unpredictable Cover



Expert evaluation

Field Evaluation



Capture rates of

real adversaries

56


Co

un

t





Co

un

t

Base Patrol Area

45


56



46




3

35

4

45

5

55

Q1

Q2

Q3

Q4

Q5

Q6

Q7

Q8

Q9

Q1

0

Q11

Q12

Secu

rity

Sco

re

Human Game Theory

56








47

5648




21 days of patrol


Random + Human

Not controlled

0

20

40

60

80

100 Miscellaneous

Drugs

Firearm Violations

0

5

10

15

20

Captures30 min

Warnings30 min

Violations30 min

Game Theory

Rand+Human

Controlled

56

User Feedback










49

56



50

Thank you

to sponsors

56

THANK YOU

tambeuscedu


51

56





A

B

C

5 min 10 min 15 min

20

56




A

B

C

5 min 10 min 15 min

Ferry


21

56




A

B

C

5 min 10 min 15 min

Ferry

22



56




A

B

C

5 min 10 min 15 min

Ferry



23

Pr([(B5) (C 10) (C15)]) = 047

Pr([(B5) (C10) (B15)]) =023

NT variables

Pr([(A5) (A10) (B15)]) = 017

Pr([(A5) (A10) (A15)]) = 013


56




A

B

C

5 min 10 min 15 min

Ferry



24

NT variables


56




A

B

C

5 min 10 min 15 min

Ferry

030

017

013


010

007

003


Pr([(B5) (C10) (B15)]) =023


25

NT variables

56

Fisheries

26


2007 2009 2013 2014 2014 2015


56






A

B

C

5 min 10 min 15 min

030

017

013

010

010

27

56






A

B

C

5 min 10 min 15 min

030

015

010

010

007

003

005

28

Jiang Delle Fave

56





A

B

C

5 min 10 min 15 min

030

015

010

010

007

003

005

29



56





defender execution

uncertainty


uncertainty

RECON

BRASS

Monotonic Maximin


30

URAC

Payoff interval

Not point

estimateGMC HUNTER

Bayesian

Robust

Yin Nguyen An

0

05

1

15

5 10

Def

end

ers

EU

Targets

ISG RECON URAC

5631


2007 2011 2013 2014 2014 2015


2014 2014 20152015

56



Fishery Protection

McCarthy Ford Brown

Forest Protection


56

Wildlife Protection


5634



Improve model

Defender calculates

mixed strategy

Defender executes

randomized patrols



56



56




T

j

jxadversary

EU

jxadversary

EU

j

e

eq

1

))((

))((

Adversaryrsquos

probability of

choosing target j


EUPerfect

-3

-25

-2

-15

-1

-05

0


Quantal

Response

Epsilon

robust

Perfect

rational

YangPita

42

56


Bounded Rationality

Penalty3

Reward2

Prob1


SEU

M

j

jxSEU

jxSEU

jadversary

adversary

e

eq

1

)(

)(


Ca

ptu

re

Pro

ba

bil

ity

UWA data from

Uganda

Year 2012

Predicting

poaching

Adversaryrsquos

probability of

choosing target j

Nguyen

43

Reward (animals)

5638





Execute randomized

patrols




10000 emails

Kar

-08

-07

-06

-05

-04

-03

-02

-01

0

01


Def

end

er U

tili

ty

Maximin SUQR

Bayesian SUQR

5639



Animal Density

Co

ver

ag

e

Pro

ba

bil

ity

Human

success

Human

Failure

Increasedecrease

Subjective Utility

-08-07-06-05-04-03-02-01

001


Def

end

er U

tili

ty

Maximin SUQR

Bayesian SUQR SHARP



Execute randomized

patrols


564040




Kar

5641





Malaysia

56







Fang

56




43

Zhang Sinha


56




44

Lab

Evaluation

Simulated

adversary

Human subject

adversaries

Field Evaluation

Patrol quality

Unpredictable Cover



Expert evaluation

Field Evaluation



Capture rates of

real adversaries

56


Co

un

t





Co

un

t

Base Patrol Area

45


56



46




3

35

4

45

5

55

Q1

Q2

Q3

Q4

Q5

Q6

Q7

Q8

Q9

Q1

0

Q11

Q12

Secu

rity

Sco

re

Human Game Theory

56








47

5648




21 days of patrol


Random + Human

Not controlled

0

20

40

60

80

100 Miscellaneous

Drugs

Firearm Violations

0

5

10

15

20

Captures30 min

Warnings30 min

Violations30 min

Game Theory

Rand+Human

Controlled

56

User Feedback










49

56



50

Thank you

to sponsors

56

THANK YOU

tambeuscedu


51

56




A

B

C

5 min 10 min 15 min

Ferry


21

56




A

B

C

5 min 10 min 15 min

Ferry

22



56




A

B

C

5 min 10 min 15 min

Ferry



23

Pr([(B5) (C 10) (C15)]) = 047

Pr([(B5) (C10) (B15)]) =023

NT variables

Pr([(A5) (A10) (B15)]) = 017

Pr([(A5) (A10) (A15)]) = 013


56




A

B

C

5 min 10 min 15 min

Ferry



24

NT variables


56




A

B

C

5 min 10 min 15 min

Ferry

030

017

013


010

007

003


Pr([(B5) (C10) (B15)]) =023


25

NT variables

56

Fisheries

26


2007 2009 2013 2014 2014 2015


56






A

B

C

5 min 10 min 15 min

030

017

013

010

010

27

56






A

B

C

5 min 10 min 15 min

030

015

010

010

007

003

005

28

Jiang Delle Fave

56





A

B

C

5 min 10 min 15 min

030

015

010

010

007

003

005

29



56





defender execution

uncertainty


uncertainty

RECON

BRASS

Monotonic Maximin


30

URAC

Payoff interval

Not point

estimateGMC HUNTER

Bayesian

Robust

Yin Nguyen An

0

05

1

15

5 10

Def

end

ers

EU

Targets

ISG RECON URAC

5631


2007 2011 2013 2014 2014 2015


2014 2014 20152015

56



Fishery Protection

McCarthy Ford Brown

Forest Protection


56

Wildlife Protection


5634



Improve model

Defender calculates

mixed strategy

Defender executes

randomized patrols



56



56




T

j

jxadversary

EU

jxadversary

EU

j

e

eq

1

))((

))((

Adversaryrsquos

probability of

choosing target j


EUPerfect

-3

-25

-2

-15

-1

-05

0


Quantal

Response

Epsilon

robust

Perfect

rational

YangPita

42

56


Bounded Rationality

Penalty3

Reward2

Prob1


SEU

M

j

jxSEU

jxSEU

jadversary

adversary

e

eq

1

)(

)(


Ca

ptu

re

Pro

ba

bil

ity

UWA data from

Uganda

Year 2012

Predicting

poaching

Adversaryrsquos

probability of

choosing target j

Nguyen

43

Reward (animals)

5638





Execute randomized

patrols




10000 emails

Kar

-08

-07

-06

-05

-04

-03

-02

-01

0

01


Def

end

er U

tili

ty

Maximin SUQR

Bayesian SUQR

5639



Animal Density

Co

ver

ag

e

Pro

ba

bil

ity

Human

success

Human

Failure

Increasedecrease

Subjective Utility

-08-07-06-05-04-03-02-01

001


Def

end

er U

tili

ty

Maximin SUQR

Bayesian SUQR SHARP



Execute randomized

patrols


564040




Kar

5641





Malaysia

56







Fang

56




43

Zhang Sinha


56




44

Lab

Evaluation

Simulated

adversary

Human subject

adversaries

Field Evaluation

Patrol quality

Unpredictable Cover



Expert evaluation

Field Evaluation



Capture rates of

real adversaries

56


Co

un

t





Co

un

t

Base Patrol Area

45


56



46




3

35

4

45

5

55

Q1

Q2

Q3

Q4

Q5

Q6

Q7

Q8

Q9

Q1

0

Q11

Q12

Secu

rity

Sco

re

Human Game Theory

56








47

5648




21 days of patrol


Random + Human

Not controlled

0

20

40

60

80

100 Miscellaneous

Drugs

Firearm Violations

0

5

10

15

20

Captures30 min

Warnings30 min

Violations30 min

Game Theory

Rand+Human

Controlled

56

User Feedback










49

56



50

Thank you

to sponsors

56

THANK YOU

tambeuscedu


51

56




A

B

C

5 min 10 min 15 min

Ferry

22



56




A

B

C

5 min 10 min 15 min

Ferry



23

Pr([(B5) (C 10) (C15)]) = 047

Pr([(B5) (C10) (B15)]) =023

NT variables

Pr([(A5) (A10) (B15)]) = 017

Pr([(A5) (A10) (A15)]) = 013


56




A

B

C

5 min 10 min 15 min

Ferry



24

NT variables


56




A

B

C

5 min 10 min 15 min

Ferry

030

017

013


010

007

003


Pr([(B5) (C10) (B15)]) =023


25

NT variables

56

Fisheries

26


2007 2009 2013 2014 2014 2015


56






A

B

C

5 min 10 min 15 min

030

017

013

010

010

27

56






A

B

C

5 min 10 min 15 min

030

015

010

010

007

003

005

28

Jiang Delle Fave

56





A

B

C

5 min 10 min 15 min

030

015

010

010

007

003

005

29



56





defender execution

uncertainty


uncertainty

RECON

BRASS

Monotonic Maximin


30

URAC

Payoff interval

Not point

estimateGMC HUNTER

Bayesian

Robust

Yin Nguyen An

0

05

1

15

5 10

Def

end

ers

EU

Targets

ISG RECON URAC

5631


2007 2011 2013 2014 2014 2015


2014 2014 20152015

56



Fishery Protection

McCarthy Ford Brown

Forest Protection


56

Wildlife Protection


5634



Improve model

Defender calculates

mixed strategy

Defender executes

randomized patrols



56



56




T

j

jxadversary

EU

jxadversary

EU

j

e

eq

1

))((

))((

Adversaryrsquos

probability of

choosing target j


EUPerfect

-3

-25

-2

-15

-1

-05

0


Quantal

Response

Epsilon

robust

Perfect

rational

YangPita

42

56


Bounded Rationality

Penalty3

Reward2

Prob1


SEU

M

j

jxSEU

jxSEU

jadversary

adversary

e

eq

1

)(

)(


Ca

ptu

re

Pro

ba

bil

ity

UWA data from

Uganda

Year 2012

Predicting

poaching

Adversaryrsquos

probability of

choosing target j

Nguyen

43

Reward (animals)

5638





Execute randomized

patrols




10000 emails

Kar

-08

-07

-06

-05

-04

-03

-02

-01

0

01


Def

end

er U

tili

ty

Maximin SUQR

Bayesian SUQR

5639



Animal Density

Co

ver

ag

e

Pro

ba

bil

ity

Human

success

Human

Failure

Increasedecrease

Subjective Utility

-08-07-06-05-04-03-02-01

001


Def

end

er U

tili

ty

Maximin SUQR

Bayesian SUQR SHARP



Execute randomized

patrols


564040




Kar

5641





Malaysia

56







Fang

56




43

Zhang Sinha


56




44

Lab

Evaluation

Simulated

adversary

Human subject

adversaries

Field Evaluation

Patrol quality

Unpredictable Cover



Expert evaluation

Field Evaluation



Capture rates of

real adversaries

56


Co

un

t





Co

un

t

Base Patrol Area

45


56



46




3

35

4

45

5

55

Q1

Q2

Q3

Q4

Q5

Q6

Q7

Q8

Q9

Q1

0

Q11

Q12

Secu

rity

Sco

re

Human Game Theory

56








47

5648




21 days of patrol


Random + Human

Not controlled

0

20

40

60

80

100 Miscellaneous

Drugs

Firearm Violations

0

5

10

15

20

Captures30 min

Warnings30 min

Violations30 min

Game Theory

Rand+Human

Controlled

56

User Feedback










49

56



50

Thank you

to sponsors

56

THANK YOU

tambeuscedu


51

56




A

B

C

5 min 10 min 15 min

Ferry



23

Pr([(B5) (C 10) (C15)]) = 047

Pr([(B5) (C10) (B15)]) =023

NT variables

Pr([(A5) (A10) (B15)]) = 017

Pr([(A5) (A10) (A15)]) = 013


56




A

B

C

5 min 10 min 15 min

Ferry



24

NT variables


56




A

B

C

5 min 10 min 15 min

Ferry

030

017

013


010

007

003


Pr([(B5) (C10) (B15)]) =023


25

NT variables

56

Fisheries

26


2007 2009 2013 2014 2014 2015


56






A

B

C

5 min 10 min 15 min

030

017

013

010

010

27

56






A

B

C

5 min 10 min 15 min

030

015

010

010

007

003

005

28

Jiang Delle Fave

56





A

B

C

5 min 10 min 15 min

030

015

010

010

007

003

005

29



56





defender execution

uncertainty


uncertainty

RECON

BRASS

Monotonic Maximin


30

URAC

Payoff interval

Not point

estimateGMC HUNTER

Bayesian

Robust

Yin Nguyen An

0

05

1

15

5 10

Def

end

ers

EU

Targets

ISG RECON URAC

5631


2007 2011 2013 2014 2014 2015


2014 2014 20152015

56



Fishery Protection

McCarthy Ford Brown

Forest Protection


56

Wildlife Protection


5634



Improve model

Defender calculates

mixed strategy

Defender executes

randomized patrols



56



56




T

j

jxadversary

EU

jxadversary

EU

j

e

eq

1

))((

))((

Adversaryrsquos

probability of

choosing target j


EUPerfect

-3

-25

-2

-15

-1

-05

0


Quantal

Response

Epsilon

robust

Perfect

rational

YangPita

42

56


Bounded Rationality

Penalty3

Reward2

Prob1


SEU

M

j

jxSEU

jxSEU

jadversary

adversary

e

eq

1

)(

)(


Ca

ptu

re

Pro

ba

bil

ity

UWA data from

Uganda

Year 2012

Predicting

poaching

Adversaryrsquos

probability of

choosing target j

Nguyen

43

Reward (animals)

5638





Execute randomized

patrols




10000 emails

Kar

-08

-07

-06

-05

-04

-03

-02

-01

0

01


Def

end

er U

tili

ty

Maximin SUQR

Bayesian SUQR

5639



Animal Density

Co

ver

ag

e

Pro

ba

bil

ity

Human

success

Human

Failure

Increasedecrease

Subjective Utility

-08-07-06-05-04-03-02-01

001


Def

end

er U

tili

ty

Maximin SUQR

Bayesian SUQR SHARP



Execute randomized

patrols


564040




Kar

5641





Malaysia

56







Fang

56




43

Zhang Sinha


56




44

Lab

Evaluation

Simulated

adversary

Human subject

adversaries

Field Evaluation

Patrol quality

Unpredictable Cover



Expert evaluation

Field Evaluation



Capture rates of

real adversaries

56


Co

un

t





Co

un

t

Base Patrol Area

45


56



46




3

35

4

45

5

55

Q1

Q2

Q3

Q4

Q5

Q6

Q7

Q8

Q9

Q1

0

Q11

Q12

Secu

rity

Sco

re

Human Game Theory

56








47

5648




21 days of patrol


Random + Human

Not controlled

0

20

40

60

80

100 Miscellaneous

Drugs

Firearm Violations

0

5

10

15

20

Captures30 min

Warnings30 min

Violations30 min

Game Theory

Rand+Human

Controlled

56

User Feedback










49

56



50

Thank you

to sponsors

56

THANK YOU

tambeuscedu


51

56




A

B

C

5 min 10 min 15 min

Ferry



24

NT variables


56




A

B

C

5 min 10 min 15 min

Ferry

030

017

013


010

007

003


Pr([(B5) (C10) (B15)]) =023


25

NT variables

56

Fisheries

26


2007 2009 2013 2014 2014 2015


56






A

B

C

5 min 10 min 15 min

030

017

013

010

010

27

56






A

B

C

5 min 10 min 15 min

030

015

010

010

007

003

005

28

Jiang Delle Fave

56





A

B

C

5 min 10 min 15 min

030

015

010

010

007

003

005

29



56





defender execution

uncertainty


uncertainty

RECON

BRASS

Monotonic Maximin


30

URAC

Payoff interval

Not point

estimateGMC HUNTER

Bayesian

Robust

Yin Nguyen An

0

05

1

15

5 10

Def

end

ers

EU

Targets

ISG RECON URAC

5631


2007 2011 2013 2014 2014 2015


2014 2014 20152015

56



Fishery Protection

McCarthy Ford Brown

Forest Protection


56

Wildlife Protection


5634



Improve model

Defender calculates

mixed strategy

Defender executes

randomized patrols



56



56




T

j

jxadversary

EU

jxadversary

EU

j

e

eq

1

))((

))((

Adversaryrsquos

probability of

choosing target j


EUPerfect

-3

-25

-2

-15

-1

-05

0


Quantal

Response

Epsilon

robust

Perfect

rational

YangPita

42

56


Bounded Rationality

Penalty3

Reward2

Prob1


SEU

M

j

jxSEU

jxSEU

jadversary

adversary

e

eq

1

)(

)(


Ca

ptu

re

Pro

ba

bil

ity

UWA data from

Uganda

Year 2012

Predicting

poaching

Adversaryrsquos

probability of

choosing target j

Nguyen

43

Reward (animals)

5638





Execute randomized

patrols




10000 emails

Kar

-08

-07

-06

-05

-04

-03

-02

-01

0

01


Def

end

er U

tili

ty

Maximin SUQR

Bayesian SUQR

5639



Animal Density

Co

ver

ag

e

Pro

ba

bil

ity

Human

success

Human

Failure

Increasedecrease

Subjective Utility

-08-07-06-05-04-03-02-01

001


Def

end

er U

tili

ty

Maximin SUQR

Bayesian SUQR SHARP



Execute randomized

patrols


564040




Kar

5641





Malaysia

56







Fang

56




43

Zhang Sinha


56




44

Lab

Evaluation

Simulated

adversary

Human subject

adversaries

Field Evaluation

Patrol quality

Unpredictable Cover



Expert evaluation

Field Evaluation



Capture rates of

real adversaries

56


Co

un

t





Co

un

t

Base Patrol Area

45


56



46




3

35

4

45

5

55

Q1

Q2

Q3

Q4

Q5

Q6

Q7

Q8

Q9

Q1

0

Q11

Q12

Secu

rity

Sco

re

Human Game Theory

56








47

5648




21 days of patrol


Random + Human

Not controlled

0

20

40

60

80

100 Miscellaneous

Drugs

Firearm Violations

0

5

10

15

20

Captures30 min

Warnings30 min

Violations30 min

Game Theory

Rand+Human

Controlled

56

User Feedback










49

56



50

Thank you

to sponsors

56

THANK YOU

tambeuscedu


51

56




A

B

C

5 min 10 min 15 min

Ferry

030

017

013


010

007

003


Pr([(B5) (C10) (B15)]) =023


25

NT variables

56

Fisheries

26


2007 2009 2013 2014 2014 2015


56






A

B

C

5 min 10 min 15 min

030

017

013

010

010

27

56






A

B

C

5 min 10 min 15 min

030

015

010

010

007

003

005

28

Jiang Delle Fave

56





A

B

C

5 min 10 min 15 min

030

015

010

010

007

003

005

29



56





defender execution

uncertainty


uncertainty

RECON

BRASS

Monotonic Maximin


30

URAC

Payoff interval

Not point

estimateGMC HUNTER

Bayesian

Robust

Yin Nguyen An

0

05

1

15

5 10

Def

end

ers

EU

Targets

ISG RECON URAC

5631


2007 2011 2013 2014 2014 2015


2014 2014 20152015

56



Fishery Protection

McCarthy Ford Brown

Forest Protection


56

Wildlife Protection


5634



Improve model

Defender calculates

mixed strategy

Defender executes

randomized patrols



56



56




T

j

jxadversary

EU

jxadversary

EU

j

e

eq

1

))((

))((

Adversaryrsquos

probability of

choosing target j


EUPerfect

-3

-25

-2

-15

-1

-05

0


Quantal

Response

Epsilon

robust

Perfect

rational

YangPita

42

56


Bounded Rationality

Penalty3

Reward2

Prob1


SEU

M

j

jxSEU

jxSEU

jadversary

adversary

e

eq

1

)(

)(


Ca

ptu

re

Pro

ba

bil

ity

UWA data from

Uganda

Year 2012

Predicting

poaching

Adversaryrsquos

probability of

choosing target j

Nguyen

43

Reward (animals)

5638





Execute randomized

patrols




10000 emails

Kar

-08

-07

-06

-05

-04

-03

-02

-01

0

01


Def

end

er U

tili

ty

Maximin SUQR

Bayesian SUQR

5639



Animal Density

Co

ver

ag

e

Pro

ba

bil

ity

Human

success

Human

Failure

Increasedecrease

Subjective Utility

-08-07-06-05-04-03-02-01

001


Def

end

er U

tili

ty

Maximin SUQR

Bayesian SUQR SHARP



Execute randomized

patrols


564040




Kar

5641





Malaysia

56







Fang

56




43

Zhang Sinha


56




44

Lab

Evaluation

Simulated

adversary

Human subject

adversaries

Field Evaluation

Patrol quality

Unpredictable Cover



Expert evaluation

Field Evaluation



Capture rates of

real adversaries

56


Co

un

t





Co

un

t

Base Patrol Area

45


56



46




3

35

4

45

5

55

Q1

Q2

Q3

Q4

Q5

Q6

Q7

Q8

Q9

Q1

0

Q11

Q12

Secu

rity

Sco

re

Human Game Theory

56








47

5648




21 days of patrol


Random + Human

Not controlled

0

20

40

60

80

100 Miscellaneous

Drugs

Firearm Violations

0

5

10

15

20

Captures30 min

Warnings30 min

Violations30 min

Game Theory

Rand+Human

Controlled

56

User Feedback










49

56



50

Thank you

to sponsors

56

THANK YOU

tambeuscedu


51

56

Fisheries

26


2007 2009 2013 2014 2014 2015


56






A

B

C

5 min 10 min 15 min

030

017

013

010

010

27

56






A

B

C

5 min 10 min 15 min

030

015

010

010

007

003

005

28

Jiang Delle Fave

56





A

B

C

5 min 10 min 15 min

030

015

010

010

007

003

005

29



56





defender execution

uncertainty


uncertainty

RECON

BRASS

Monotonic Maximin


30

URAC

Payoff interval

Not point

estimateGMC HUNTER

Bayesian

Robust

Yin Nguyen An

0

05

1

15

5 10

Def

end

ers

EU

Targets

ISG RECON URAC

5631


2007 2011 2013 2014 2014 2015


2014 2014 20152015

56



Fishery Protection

McCarthy Ford Brown

Forest Protection


56

Wildlife Protection


5634



Improve model

Defender calculates

mixed strategy

Defender executes

randomized patrols



56



56




T

j

jxadversary

EU

jxadversary

EU

j

e

eq

1

))((

))((

Adversaryrsquos

probability of

choosing target j


EUPerfect

-3

-25

-2

-15

-1

-05

0


Quantal

Response

Epsilon

robust

Perfect

rational

YangPita

42

56


Bounded Rationality

Penalty3

Reward2

Prob1


SEU

M

j

jxSEU

jxSEU

jadversary

adversary

e

eq

1

)(

)(


Ca

ptu

re

Pro

ba

bil

ity

UWA data from

Uganda

Year 2012

Predicting

poaching

Adversaryrsquos

probability of

choosing target j

Nguyen

43

Reward (animals)

5638





Execute randomized

patrols




10000 emails

Kar

-08

-07

-06

-05

-04

-03

-02

-01

0

01


Def

end

er U

tili

ty

Maximin SUQR

Bayesian SUQR

5639



Animal Density

Co

ver

ag

e

Pro

ba

bil

ity

Human

success

Human

Failure

Increasedecrease

Subjective Utility

-08-07-06-05-04-03-02-01

001


Def

end

er U

tili

ty

Maximin SUQR

Bayesian SUQR SHARP



Execute randomized

patrols


564040




Kar

5641





Malaysia

56







Fang

56




43

Zhang Sinha


56




44

Lab

Evaluation

Simulated

adversary

Human subject

adversaries

Field Evaluation

Patrol quality

Unpredictable Cover



Expert evaluation

Field Evaluation



Capture rates of

real adversaries

56


Co

un

t





Co

un

t

Base Patrol Area

45


56



46




3

35

4

45

5

55

Q1

Q2

Q3

Q4

Q5

Q6

Q7

Q8

Q9

Q1

0

Q11

Q12

Secu

rity

Sco

re

Human Game Theory

56








47

5648




21 days of patrol


Random + Human

Not controlled

0

20

40

60

80

100 Miscellaneous

Drugs

Firearm Violations

0

5

10

15

20

Captures30 min

Warnings30 min

Violations30 min

Game Theory

Rand+Human

Controlled

56

User Feedback










49

56



50

Thank you

to sponsors

56

THANK YOU

tambeuscedu


51

56






A

B

C

5 min 10 min 15 min

030

017

013

010

010

27

56






A

B

C

5 min 10 min 15 min

030

015

010

010

007

003

005

28

Jiang Delle Fave

56





A

B

C

5 min 10 min 15 min

030

015

010

010

007

003

005

29



56





defender execution

uncertainty


uncertainty

RECON

BRASS

Monotonic Maximin


30

URAC

Payoff interval

Not point

estimateGMC HUNTER

Bayesian

Robust

Yin Nguyen An

0

05

1

15

5 10

Def

end

ers

EU

Targets

ISG RECON URAC

5631


2007 2011 2013 2014 2014 2015


2014 2014 20152015

56



Fishery Protection

McCarthy Ford Brown

Forest Protection


56

Wildlife Protection


5634



Improve model

Defender calculates

mixed strategy

Defender executes

randomized patrols



56



56




T

j

jxadversary

EU

jxadversary

EU

j

e

eq

1

))((

))((

Adversaryrsquos

probability of

choosing target j


EUPerfect

-3

-25

-2

-15

-1

-05

0


Quantal

Response

Epsilon

robust

Perfect

rational

YangPita

42

56


Bounded Rationality

Penalty3

Reward2

Prob1


SEU

M

j

jxSEU

jxSEU

jadversary

adversary

e

eq

1

)(

)(


Ca

ptu

re

Pro

ba

bil

ity

UWA data from

Uganda

Year 2012

Predicting

poaching

Adversaryrsquos

probability of

choosing target j

Nguyen

43

Reward (animals)

5638





Execute randomized

patrols




10000 emails

Kar

-08

-07

-06

-05

-04

-03

-02

-01

0

01


Def

end

er U

tili

ty

Maximin SUQR

Bayesian SUQR

5639



Animal Density

Co

ver

ag

e

Pro

ba

bil

ity

Human

success

Human

Failure

Increasedecrease

Subjective Utility

-08-07-06-05-04-03-02-01

001


Def

end

er U

tili

ty

Maximin SUQR

Bayesian SUQR SHARP



Execute randomized

patrols


564040




Kar

5641





Malaysia

56







Fang

56




43

Zhang Sinha


56




44

Lab

Evaluation

Simulated

adversary

Human subject

adversaries

Field Evaluation

Patrol quality

Unpredictable Cover



Expert evaluation

Field Evaluation



Capture rates of

real adversaries

56


Co

un

t





Co

un

t

Base Patrol Area

45


56



46




3

35

4

45

5

55

Q1

Q2

Q3

Q4

Q5

Q6

Q7

Q8

Q9

Q1

0

Q11

Q12

Secu

rity

Sco

re

Human Game Theory

56








47

5648




21 days of patrol


Random + Human

Not controlled

0

20

40

60

80

100 Miscellaneous

Drugs

Firearm Violations

0

5

10

15

20

Captures30 min

Warnings30 min

Violations30 min

Game Theory

Rand+Human

Controlled

56

User Feedback










49

56



50

Thank you

to sponsors

56

THANK YOU

tambeuscedu


51

56






A

B

C

5 min 10 min 15 min

030

015

010

010

007

003

005

28

Jiang Delle Fave

56





A

B

C

5 min 10 min 15 min

030

015

010

010

007

003

005

29



56





defender execution

uncertainty


uncertainty

RECON

BRASS

Monotonic Maximin


30

URAC

Payoff interval

Not point

estimateGMC HUNTER

Bayesian

Robust

Yin Nguyen An

0

05

1

15

5 10

Def

end

ers

EU

Targets

ISG RECON URAC

5631


2007 2011 2013 2014 2014 2015


2014 2014 20152015

56



Fishery Protection

McCarthy Ford Brown

Forest Protection


56

Wildlife Protection


5634



Improve model

Defender calculates

mixed strategy

Defender executes

randomized patrols



56



56




T

j

jxadversary

EU

jxadversary

EU

j

e

eq

1

))((

))((

Adversaryrsquos

probability of

choosing target j


EUPerfect

-3

-25

-2

-15

-1

-05

0


Quantal

Response

Epsilon

robust

Perfect

rational

YangPita

42

56


Bounded Rationality

Penalty3

Reward2

Prob1


SEU

M

j

jxSEU

jxSEU

jadversary

adversary

e

eq

1

)(

)(


Ca

ptu

re

Pro

ba

bil

ity

UWA data from

Uganda

Year 2012

Predicting

poaching

Adversaryrsquos

probability of

choosing target j

Nguyen

43

Reward (animals)

5638





Execute randomized

patrols




10000 emails

Kar

-08

-07

-06

-05

-04

-03

-02

-01

0

01


Def

end

er U

tili

ty

Maximin SUQR

Bayesian SUQR

5639



Animal Density

Co

ver

ag

e

Pro

ba

bil

ity

Human

success

Human

Failure

Increasedecrease

Subjective Utility

-08-07-06-05-04-03-02-01

001


Def

end

er U

tili

ty

Maximin SUQR

Bayesian SUQR SHARP



Execute randomized

patrols


564040




Kar

5641





Malaysia

56







Fang

56




43

Zhang Sinha


56




44

Lab

Evaluation

Simulated

adversary

Human subject

adversaries

Field Evaluation

Patrol quality

Unpredictable Cover



Expert evaluation

Field Evaluation



Capture rates of

real adversaries

56


Co

un

t





Co

un

t

Base Patrol Area

45


56



46




3

35

4

45

5

55

Q1

Q2

Q3

Q4

Q5

Q6

Q7

Q8

Q9

Q1

0

Q11

Q12

Secu

rity

Sco

re

Human Game Theory

56








47

5648




21 days of patrol


Random + Human

Not controlled

0

20

40

60

80

100 Miscellaneous

Drugs

Firearm Violations

0

5

10

15

20

Captures30 min

Warnings30 min

Violations30 min

Game Theory

Rand+Human

Controlled

56

User Feedback










49

56



50

Thank you

to sponsors

56

THANK YOU

tambeuscedu


51

56





A

B

C

5 min 10 min 15 min

030

015

010

010

007

003

005

29



56





defender execution

uncertainty


uncertainty

RECON

BRASS

Monotonic Maximin


30

URAC

Payoff interval

Not point

estimateGMC HUNTER

Bayesian

Robust

Yin Nguyen An

0

05

1

15

5 10

Def

end

ers

EU

Targets

ISG RECON URAC

5631


2007 2011 2013 2014 2014 2015


2014 2014 20152015

56



Fishery Protection

McCarthy Ford Brown

Forest Protection


56

Wildlife Protection


5634



Improve model

Defender calculates

mixed strategy

Defender executes

randomized patrols



56



56




T

j

jxadversary

EU

jxadversary

EU

j

e

eq

1

))((

))((

Adversaryrsquos

probability of

choosing target j


EUPerfect

-3

-25

-2

-15

-1

-05

0


Quantal

Response

Epsilon

robust

Perfect

rational

YangPita

42

56


Bounded Rationality

Penalty3

Reward2

Prob1


SEU

M

j

jxSEU

jxSEU

jadversary

adversary

e

eq

1

)(

)(


Ca

ptu

re

Pro

ba

bil

ity

UWA data from

Uganda

Year 2012

Predicting

poaching

Adversaryrsquos

probability of

choosing target j

Nguyen

43

Reward (animals)

5638





Execute randomized

patrols




10000 emails

Kar

-08

-07

-06

-05

-04

-03

-02

-01

0

01


Def

end

er U

tili

ty

Maximin SUQR

Bayesian SUQR

5639



Animal Density

Co

ver

ag

e

Pro

ba

bil

ity

Human

success

Human

Failure

Increasedecrease

Subjective Utility

-08-07-06-05-04-03-02-01

001


Def

end

er U

tili

ty

Maximin SUQR

Bayesian SUQR SHARP



Execute randomized

patrols


564040




Kar

5641





Malaysia

56







Fang

56




43

Zhang Sinha


56




44

Lab

Evaluation

Simulated

adversary

Human subject

adversaries

Field Evaluation

Patrol quality

Unpredictable Cover



Expert evaluation

Field Evaluation



Capture rates of

real adversaries

56


Co

un

t





Co

un

t

Base Patrol Area

45


56



46




3

35

4

45

5

55

Q1

Q2

Q3

Q4

Q5

Q6

Q7

Q8

Q9

Q1

0

Q11

Q12

Secu

rity

Sco

re

Human Game Theory

56








47

5648




21 days of patrol


Random + Human

Not controlled

0

20

40

60

80

100 Miscellaneous

Drugs

Firearm Violations

0

5

10

15

20

Captures30 min

Warnings30 min

Violations30 min

Game Theory

Rand+Human

Controlled

56

User Feedback










49

56



50

Thank you

to sponsors

56

THANK YOU

tambeuscedu


51

56





defender execution

uncertainty


uncertainty

RECON

BRASS

Monotonic Maximin


30

URAC

Payoff interval

Not point

estimateGMC HUNTER

Bayesian

Robust

Yin Nguyen An

0

05

1

15

5 10

Def

end

ers

EU

Targets

ISG RECON URAC

5631


2007 2011 2013 2014 2014 2015


2014 2014 20152015

56



Fishery Protection

McCarthy Ford Brown

Forest Protection


56

Wildlife Protection


5634



Improve model

Defender calculates

mixed strategy

Defender executes

randomized patrols



56



56




T

j

jxadversary

EU

jxadversary

EU

j

e

eq

1

))((

))((

Adversaryrsquos

probability of

choosing target j


EUPerfect

-3

-25

-2

-15

-1

-05

0


Quantal

Response

Epsilon

robust

Perfect

rational

YangPita

42

56


Bounded Rationality

Penalty3

Reward2

Prob1


SEU

M

j

jxSEU

jxSEU

jadversary

adversary

e

eq

1

)(

)(


Ca

ptu

re

Pro

ba

bil

ity

UWA data from

Uganda

Year 2012

Predicting

poaching

Adversaryrsquos

probability of

choosing target j

Nguyen

43

Reward (animals)

5638





Execute randomized

patrols




10000 emails

Kar

-08

-07

-06

-05

-04

-03

-02

-01

0

01


Def

end

er U

tili

ty

Maximin SUQR

Bayesian SUQR

5639



Animal Density

Co

ver

ag

e

Pro

ba

bil

ity

Human

success

Human

Failure

Increasedecrease

Subjective Utility

-08-07-06-05-04-03-02-01

001


Def

end

er U

tili

ty

Maximin SUQR

Bayesian SUQR SHARP



Execute randomized

patrols


564040




Kar

5641





Malaysia

56







Fang

56




43

Zhang Sinha


56




44

Lab

Evaluation

Simulated

adversary

Human subject

adversaries

Field Evaluation

Patrol quality

Unpredictable Cover



Expert evaluation

Field Evaluation



Capture rates of

real adversaries

56


Co

un

t





Co

un

t

Base Patrol Area

45


56



46




3

35

4

45

5

55

Q1

Q2

Q3

Q4

Q5

Q6

Q7

Q8

Q9

Q1

0

Q11

Q12

Secu

rity

Sco

re

Human Game Theory

56








47

5648




21 days of patrol


Random + Human

Not controlled

0

20

40

60

80

100 Miscellaneous

Drugs

Firearm Violations

0

5

10

15

20

Captures30 min

Warnings30 min

Violations30 min

Game Theory

Rand+Human

Controlled

56

User Feedback










49

56



50

Thank you

to sponsors

56

THANK YOU

tambeuscedu


51

5631


2007 2011 2013 2014 2014 2015


2014 2014 20152015

56



Fishery Protection

McCarthy Ford Brown

Forest Protection


56

Wildlife Protection


5634



Improve model

Defender calculates

mixed strategy

Defender executes

randomized patrols



56



56




T

j

jxadversary

EU

jxadversary

EU

j

e

eq

1

))((

))((

Adversaryrsquos

probability of

choosing target j


EUPerfect

-3

-25

-2

-15

-1

-05

0


Quantal

Response

Epsilon

robust

Perfect

rational

YangPita

42

56


Bounded Rationality

Penalty3

Reward2

Prob1


SEU

M

j

jxSEU

jxSEU

jadversary

adversary

e

eq

1

)(

)(


Ca

ptu

re

Pro

ba

bil

ity

UWA data from

Uganda

Year 2012

Predicting

poaching

Adversaryrsquos

probability of

choosing target j

Nguyen

43

Reward (animals)

5638





Execute randomized

patrols




10000 emails

Kar

-08

-07

-06

-05

-04

-03

-02

-01

0

01


Def

end

er U

tili

ty

Maximin SUQR

Bayesian SUQR

5639



Animal Density

Co

ver

ag

e

Pro

ba

bil

ity

Human

success

Human

Failure

Increasedecrease

Subjective Utility

-08-07-06-05-04-03-02-01

001


Def

end

er U

tili

ty

Maximin SUQR

Bayesian SUQR SHARP



Execute randomized

patrols


564040




Kar

5641





Malaysia

56







Fang

56




43

Zhang Sinha


56




44

Lab

Evaluation

Simulated

adversary

Human subject

adversaries

Field Evaluation

Patrol quality

Unpredictable Cover



Expert evaluation

Field Evaluation



Capture rates of

real adversaries

56


Co

un

t





Co

un

t

Base Patrol Area

45


56



46




3

35

4

45

5

55

Q1

Q2

Q3

Q4

Q5

Q6

Q7

Q8

Q9

Q1

0

Q11

Q12

Secu

rity

Sco

re

Human Game Theory

56








47

5648




21 days of patrol


Random + Human

Not controlled

0

20

40

60

80

100 Miscellaneous

Drugs

Firearm Violations

0

5

10

15

20

Captures30 min

Warnings30 min

Violations30 min

Game Theory

Rand+Human

Controlled

56

User Feedback










49

56



50

Thank you

to sponsors

56

THANK YOU

tambeuscedu


51

56



Fishery Protection

McCarthy Ford Brown

Forest Protection


56

Wildlife Protection


5634



Improve model

Defender calculates

mixed strategy

Defender executes

randomized patrols



56



56




T

j

jxadversary

EU

jxadversary

EU

j

e

eq

1

))((

))((

Adversaryrsquos

probability of

choosing target j


EUPerfect

-3

-25

-2

-15

-1

-05

0


Quantal

Response

Epsilon

robust

Perfect

rational

YangPita

42

56


Bounded Rationality

Penalty3

Reward2

Prob1


SEU

M

j

jxSEU

jxSEU

jadversary

adversary

e

eq

1

)(

)(


Ca

ptu

re

Pro

ba

bil

ity

UWA data from

Uganda

Year 2012

Predicting

poaching

Adversaryrsquos

probability of

choosing target j

Nguyen

43

Reward (animals)

5638





Execute randomized

patrols




10000 emails

Kar

-08

-07

-06

-05

-04

-03

-02

-01

0

01


Def

end

er U

tili

ty

Maximin SUQR

Bayesian SUQR

5639



Animal Density

Co

ver

ag

e

Pro

ba

bil

ity

Human

success

Human

Failure

Increasedecrease

Subjective Utility

-08-07-06-05-04-03-02-01

001


Def

end

er U

tili

ty

Maximin SUQR

Bayesian SUQR SHARP



Execute randomized

patrols


564040




Kar

5641





Malaysia

56







Fang

56




43

Zhang Sinha


56




44

Lab

Evaluation

Simulated

adversary

Human subject

adversaries

Field Evaluation

Patrol quality

Unpredictable Cover



Expert evaluation

Field Evaluation



Capture rates of

real adversaries

56


Co

un

t





Co

un

t

Base Patrol Area

45


56



46




3

35

4

45

5

55

Q1

Q2

Q3

Q4

Q5

Q6

Q7

Q8

Q9

Q1

0

Q11

Q12

Secu

rity

Sco

re

Human Game Theory

56








47

5648




21 days of patrol


Random + Human

Not controlled

0

20

40

60

80

100 Miscellaneous

Drugs

Firearm Violations

0

5

10

15

20

Captures30 min

Warnings30 min

Violations30 min

Game Theory

Rand+Human

Controlled

56

User Feedback










49

56



50

Thank you

to sponsors

56

THANK YOU

tambeuscedu


51

56

Wildlife Protection


5634



Improve model

Defender calculates

mixed strategy

Defender executes

randomized patrols



56



56




T

j

jxadversary

EU

jxadversary

EU

j

e

eq

1

))((

))((

Adversaryrsquos

probability of

choosing target j


EUPerfect

-3

-25

-2

-15

-1

-05

0


Quantal

Response

Epsilon

robust

Perfect

rational

YangPita

42

56


Bounded Rationality

Penalty3

Reward2

Prob1


SEU

M

j

jxSEU

jxSEU

jadversary

adversary

e

eq

1

)(

)(


Ca

ptu

re

Pro

ba

bil

ity

UWA data from

Uganda

Year 2012

Predicting

poaching

Adversaryrsquos

probability of

choosing target j

Nguyen

43

Reward (animals)

5638





Execute randomized

patrols




10000 emails

Kar

-08

-07

-06

-05

-04

-03

-02

-01

0

01


Def

end

er U

tili

ty

Maximin SUQR

Bayesian SUQR

5639



Animal Density

Co

ver

ag

e

Pro

ba

bil

ity

Human

success

Human

Failure

Increasedecrease

Subjective Utility

-08-07-06-05-04-03-02-01

001


Def

end

er U

tili

ty

Maximin SUQR

Bayesian SUQR SHARP



Execute randomized

patrols


564040




Kar

5641





Malaysia

56







Fang

56




43

Zhang Sinha


56




44

Lab

Evaluation

Simulated

adversary

Human subject

adversaries

Field Evaluation

Patrol quality

Unpredictable Cover



Expert evaluation

Field Evaluation



Capture rates of

real adversaries

56


Co

un

t





Co

un

t

Base Patrol Area

45


56



46




3

35

4

45

5

55

Q1

Q2

Q3

Q4

Q5

Q6

Q7

Q8

Q9

Q1

0

Q11

Q12

Secu

rity

Sco

re

Human Game Theory

56








47

5648




21 days of patrol


Random + Human

Not controlled

0

20

40

60

80

100 Miscellaneous

Drugs

Firearm Violations

0

5

10

15

20

Captures30 min

Warnings30 min

Violations30 min

Game Theory

Rand+Human

Controlled

56

User Feedback










49

56



50

Thank you

to sponsors

56

THANK YOU

tambeuscedu


51

5634



Improve model

Defender calculates

mixed strategy

Defender executes

randomized patrols



56



56




T

j

jxadversary

EU

jxadversary

EU

j

e

eq

1

))((

))((

Adversaryrsquos

probability of

choosing target j


EUPerfect

-3

-25

-2

-15

-1

-05

0


Quantal

Response

Epsilon

robust

Perfect

rational

YangPita

42

56


Bounded Rationality

Penalty3

Reward2

Prob1


SEU

M

j

jxSEU

jxSEU

jadversary

adversary

e

eq

1

)(

)(


Ca

ptu

re

Pro

ba

bil

ity

UWA data from

Uganda

Year 2012

Predicting

poaching

Adversaryrsquos

probability of

choosing target j

Nguyen

43

Reward (animals)

5638





Execute randomized

patrols




10000 emails

Kar

-08

-07

-06

-05

-04

-03

-02

-01

0

01


Def

end

er U

tili

ty

Maximin SUQR

Bayesian SUQR

5639



Animal Density

Co

ver

ag

e

Pro

ba

bil

ity

Human

success

Human

Failure

Increasedecrease

Subjective Utility

-08-07-06-05-04-03-02-01

001


Def

end

er U

tili

ty

Maximin SUQR

Bayesian SUQR SHARP



Execute randomized

patrols


564040




Kar

5641





Malaysia

56







Fang

56




43

Zhang Sinha


56




44

Lab

Evaluation

Simulated

adversary

Human subject

adversaries

Field Evaluation

Patrol quality

Unpredictable Cover



Expert evaluation

Field Evaluation



Capture rates of

real adversaries

56


Co

un

t





Co

un

t

Base Patrol Area

45


56



46




3

35

4

45

5

55

Q1

Q2

Q3

Q4

Q5

Q6

Q7

Q8

Q9

Q1

0

Q11

Q12

Secu

rity

Sco

re

Human Game Theory

56








47

5648




21 days of patrol


Random + Human

Not controlled

0

20

40

60

80

100 Miscellaneous

Drugs

Firearm Violations

0

5

10

15

20

Captures30 min

Warnings30 min

Violations30 min

Game Theory

Rand+Human

Controlled

56

User Feedback










49

56



50

Thank you

to sponsors

56

THANK YOU

tambeuscedu


51

56



56




T

j

jxadversary

EU

jxadversary

EU

j

e

eq

1

))((

))((

Adversaryrsquos

probability of

choosing target j


EUPerfect

-3

-25

-2

-15

-1

-05

0


Quantal

Response

Epsilon

robust

Perfect

rational

YangPita

42

56


Bounded Rationality

Penalty3

Reward2

Prob1


SEU

M

j

jxSEU

jxSEU

jadversary

adversary

e

eq

1

)(

)(


Ca

ptu

re

Pro

ba

bil

ity

UWA data from

Uganda

Year 2012

Predicting

poaching

Adversaryrsquos

probability of

choosing target j

Nguyen

43

Reward (animals)

5638





Execute randomized

patrols




10000 emails

Kar

-08

-07

-06

-05

-04

-03

-02

-01

0

01


Def

end

er U

tili

ty

Maximin SUQR

Bayesian SUQR

5639



Animal Density

Co

ver

ag

e

Pro

ba

bil

ity

Human

success

Human

Failure

Increasedecrease

Subjective Utility

-08-07-06-05-04-03-02-01

001


Def

end

er U

tili

ty

Maximin SUQR

Bayesian SUQR SHARP



Execute randomized

patrols


564040




Kar

5641





Malaysia

56







Fang

56




43

Zhang Sinha


56




44

Lab

Evaluation

Simulated

adversary

Human subject

adversaries

Field Evaluation

Patrol quality

Unpredictable Cover



Expert evaluation

Field Evaluation



Capture rates of

real adversaries

56


Co

un

t





Co

un

t

Base Patrol Area

45


56



46




3

35

4

45

5

55

Q1

Q2

Q3

Q4

Q5

Q6

Q7

Q8

Q9

Q1

0

Q11

Q12

Secu

rity

Sco

re

Human Game Theory

56








47

5648




21 days of patrol


Random + Human

Not controlled

0

20

40

60

80

100 Miscellaneous

Drugs

Firearm Violations

0

5

10

15

20

Captures30 min

Warnings30 min

Violations30 min

Game Theory

Rand+Human

Controlled

56

User Feedback










49

56



50

Thank you

to sponsors

56

THANK YOU

tambeuscedu


51

56




T

j

jxadversary

EU

jxadversary

EU

j

e

eq

1

))((

))((

Adversaryrsquos

probability of

choosing target j


EUPerfect

-3

-25

-2

-15

-1

-05

0


Quantal

Response

Epsilon

robust

Perfect

rational

YangPita

42

56


Bounded Rationality

Penalty3

Reward2

Prob1


SEU

M

j

jxSEU

jxSEU

jadversary

adversary

e

eq

1

)(

)(


Ca

ptu

re

Pro

ba

bil

ity

UWA data from

Uganda

Year 2012

Predicting

poaching

Adversaryrsquos

probability of

choosing target j

Nguyen

43

Reward (animals)

5638





Execute randomized

patrols




10000 emails

Kar

-08

-07

-06

-05

-04

-03

-02

-01

0

01


Def

end

er U

tili

ty

Maximin SUQR

Bayesian SUQR

5639



Animal Density

Co

ver

ag

e

Pro

ba

bil

ity

Human

success

Human

Failure

Increasedecrease

Subjective Utility

-08-07-06-05-04-03-02-01

001


Def

end

er U

tili

ty

Maximin SUQR

Bayesian SUQR SHARP



Execute randomized

patrols


564040




Kar

5641





Malaysia

56







Fang

56




43

Zhang Sinha


56




44

Lab

Evaluation

Simulated

adversary

Human subject

adversaries

Field Evaluation

Patrol quality

Unpredictable Cover



Expert evaluation

Field Evaluation



Capture rates of

real adversaries

56


Co

un

t





Co

un

t

Base Patrol Area

45


56



46




3

35

4

45

5

55

Q1

Q2

Q3

Q4

Q5

Q6

Q7

Q8

Q9

Q1

0

Q11

Q12

Secu

rity

Sco

re

Human Game Theory

56








47

5648




21 days of patrol


Random + Human

Not controlled

0

20

40

60

80

100 Miscellaneous

Drugs

Firearm Violations

0

5

10

15

20

Captures30 min

Warnings30 min

Violations30 min

Game Theory

Rand+Human

Controlled

56

User Feedback










49

56



50

Thank you

to sponsors

56

THANK YOU

tambeuscedu


51

56


Bounded Rationality

Penalty3

Reward2

Prob1


SEU

M

j

jxSEU

jxSEU

jadversary

adversary

e

eq

1

)(

)(


Ca

ptu

re

Pro

ba

bil

ity

UWA data from

Uganda

Year 2012

Predicting

poaching

Adversaryrsquos

probability of

choosing target j

Nguyen

43

Reward (animals)

5638





Execute randomized

patrols




10000 emails

Kar

-08

-07

-06

-05

-04

-03

-02

-01

0

01


Def

end

er U

tili

ty

Maximin SUQR

Bayesian SUQR

5639



Animal Density

Co

ver

ag

e

Pro

ba

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ity

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Human

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Increasedecrease

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-08-07-06-05-04-03-02-01

001


Def

end

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tili

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Maximin SUQR

Bayesian SUQR SHARP



Execute randomized

patrols


564040




Kar

5641





Malaysia

56







Fang

56




43

Zhang Sinha


56




44

Lab

Evaluation

Simulated

adversary

Human subject

adversaries

Field Evaluation

Patrol quality

Unpredictable Cover



Expert evaluation

Field Evaluation



Capture rates of

real adversaries

56


Co

un

t





Co

un

t

Base Patrol Area

45


56



46




3

35

4

45

5

55

Q1

Q2

Q3

Q4

Q5

Q6

Q7

Q8

Q9

Q1

0

Q11

Q12

Secu

rity

Sco

re

Human Game Theory

56








47

5648




21 days of patrol


Random + Human

Not controlled

0

20

40

60

80

100 Miscellaneous

Drugs

Firearm Violations

0

5

10

15

20

Captures30 min

Warnings30 min

Violations30 min

Game Theory

Rand+Human

Controlled

56

User Feedback










49

56



50

Thank you

to sponsors

56

THANK YOU

tambeuscedu


51

5638





Execute randomized

patrols




10000 emails

Kar

-08

-07

-06

-05

-04

-03

-02

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0

01


Def

end

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tili

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5639



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ver

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-08-07-06-05-04-03-02-01

001


Def

end

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Maximin SUQR

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patrols


564040




Kar

5641





Malaysia

56







Fang

56




43

Zhang Sinha


56




44

Lab

Evaluation

Simulated

adversary

Human subject

adversaries

Field Evaluation

Patrol quality

Unpredictable Cover



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Field Evaluation



Capture rates of

real adversaries

56


Co

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45


56



46




3

35

4

45

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55

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Q5

Q6

Q7

Q8

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Q12

Secu

rity

Sco

re

Human Game Theory

56








47

5648




21 days of patrol


Random + Human

Not controlled

0

20

40

60

80

100 Miscellaneous

Drugs

Firearm Violations

0

5

10

15

20

Captures30 min

Warnings30 min

Violations30 min

Game Theory

Rand+Human

Controlled

56

User Feedback










49

56



50

Thank you

to sponsors

56

THANK YOU

tambeuscedu


51

5639



Animal Density

Co

ver

ag

e

Pro

ba

bil

ity

Human

success

Human

Failure

Increasedecrease

Subjective Utility

-08-07-06-05-04-03-02-01

001


Def

end

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tili

ty

Maximin SUQR

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Execute randomized

patrols


564040




Kar

5641





Malaysia

56







Fang

56




43

Zhang Sinha


56




44

Lab

Evaluation

Simulated

adversary

Human subject

adversaries

Field Evaluation

Patrol quality

Unpredictable Cover



Expert evaluation

Field Evaluation



Capture rates of

real adversaries

56


Co

un

t





Co

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t

Base Patrol Area

45


56



46




3

35

4

45

5

55

Q1

Q2

Q3

Q4

Q5

Q6

Q7

Q8

Q9

Q1

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Q12

Secu

rity

Sco

re

Human Game Theory

56








47

5648




21 days of patrol


Random + Human

Not controlled

0

20

40

60

80

100 Miscellaneous

Drugs

Firearm Violations

0

5

10

15

20

Captures30 min

Warnings30 min

Violations30 min

Game Theory

Rand+Human

Controlled

56

User Feedback










49

56



50

Thank you

to sponsors

56

THANK YOU

tambeuscedu


51

564040




Kar

5641





Malaysia

56







Fang

56




43

Zhang Sinha


56




44

Lab

Evaluation

Simulated

adversary

Human subject

adversaries

Field Evaluation

Patrol quality

Unpredictable Cover



Expert evaluation

Field Evaluation



Capture rates of

real adversaries

56


Co

un

t





Co

un

t

Base Patrol Area

45


56



46




3

35

4

45

5

55

Q1

Q2

Q3

Q4

Q5

Q6

Q7

Q8

Q9

Q1

0

Q11

Q12

Secu

rity

Sco

re

Human Game Theory

56








47

5648




21 days of patrol


Random + Human

Not controlled

0

20

40

60

80

100 Miscellaneous

Drugs

Firearm Violations

0

5

10

15

20

Captures30 min

Warnings30 min

Violations30 min

Game Theory

Rand+Human

Controlled

56

User Feedback










49

56



50

Thank you

to sponsors

56

THANK YOU

tambeuscedu


51

5641





Malaysia

56







Fang

56




43

Zhang Sinha


56




44

Lab

Evaluation

Simulated

adversary

Human subject

adversaries

Field Evaluation

Patrol quality

Unpredictable Cover



Expert evaluation

Field Evaluation



Capture rates of

real adversaries

56


Co

un

t





Co

un

t

Base Patrol Area

45


56



46




3

35

4

45

5

55

Q1

Q2

Q3

Q4

Q5

Q6

Q7

Q8

Q9

Q1

0

Q11

Q12

Secu

rity

Sco

re

Human Game Theory

56








47

5648




21 days of patrol


Random + Human

Not controlled

0

20

40

60

80

100 Miscellaneous

Drugs

Firearm Violations

0

5

10

15

20

Captures30 min

Warnings30 min

Violations30 min

Game Theory

Rand+Human

Controlled

56

User Feedback










49

56



50

Thank you

to sponsors

56

THANK YOU

tambeuscedu


51

56







Fang

56




43

Zhang Sinha


56




44

Lab

Evaluation

Simulated

adversary

Human subject

adversaries

Field Evaluation

Patrol quality

Unpredictable Cover



Expert evaluation

Field Evaluation



Capture rates of

real adversaries

56


Co

un

t





Co

un

t

Base Patrol Area

45


56



46




3

35

4

45

5

55

Q1

Q2

Q3

Q4

Q5

Q6

Q7

Q8

Q9

Q1

0

Q11

Q12

Secu

rity

Sco

re

Human Game Theory

56








47

5648




21 days of patrol


Random + Human

Not controlled

0

20

40

60

80

100 Miscellaneous

Drugs

Firearm Violations

0

5

10

15

20

Captures30 min

Warnings30 min

Violations30 min

Game Theory

Rand+Human

Controlled

56

User Feedback










49

56



50

Thank you

to sponsors

56

THANK YOU

tambeuscedu


51

56




43

Zhang Sinha


56




44

Lab

Evaluation

Simulated

adversary

Human subject

adversaries

Field Evaluation

Patrol quality

Unpredictable Cover



Expert evaluation

Field Evaluation



Capture rates of

real adversaries

56


Co

un

t





Co

un

t

Base Patrol Area

45


56



46




3

35

4

45

5

55

Q1

Q2

Q3

Q4

Q5

Q6

Q7

Q8

Q9

Q1

0

Q11

Q12

Secu

rity

Sco

re

Human Game Theory

56








47

5648




21 days of patrol


Random + Human

Not controlled

0

20

40

60

80

100 Miscellaneous

Drugs

Firearm Violations

0

5

10

15

20

Captures30 min

Warnings30 min

Violations30 min

Game Theory

Rand+Human

Controlled

56

User Feedback










49

56



50

Thank you

to sponsors

56

THANK YOU

tambeuscedu


51

56




44

Lab

Evaluation

Simulated

adversary

Human subject

adversaries

Field Evaluation

Patrol quality

Unpredictable Cover



Expert evaluation

Field Evaluation



Capture rates of

real adversaries

56


Co

un

t





Co

un

t

Base Patrol Area

45


56



46




3

35

4

45

5

55

Q1

Q2

Q3

Q4

Q5

Q6

Q7

Q8

Q9

Q1

0

Q11

Q12

Secu

rity

Sco

re

Human Game Theory

56








47

5648




21 days of patrol


Random + Human

Not controlled

0

20

40

60

80

100 Miscellaneous

Drugs

Firearm Violations

0

5

10

15

20

Captures30 min

Warnings30 min

Violations30 min

Game Theory

Rand+Human

Controlled

56

User Feedback










49

56



50

Thank you

to sponsors

56

THANK YOU

tambeuscedu


51

56


Co

un

t





Co

un

t

Base Patrol Area

45


56



46




3

35

4

45

5

55

Q1

Q2

Q3

Q4

Q5

Q6

Q7

Q8

Q9

Q1

0

Q11

Q12

Secu

rity

Sco

re

Human Game Theory

56








47

5648




21 days of patrol


Random + Human

Not controlled

0

20

40

60

80

100 Miscellaneous

Drugs

Firearm Violations

0

5

10

15

20

Captures30 min

Warnings30 min

Violations30 min

Game Theory

Rand+Human

Controlled

56

User Feedback










49

56



50

Thank you

to sponsors

56

THANK YOU

tambeuscedu


51

56



46




3

35

4

45

5

55

Q1

Q2

Q3

Q4

Q5

Q6

Q7

Q8

Q9

Q1

0

Q11

Q12

Secu

rity

Sco

re

Human Game Theory

56








47

5648




21 days of patrol


Random + Human

Not controlled

0

20

40

60

80

100 Miscellaneous

Drugs

Firearm Violations

0

5

10

15

20

Captures30 min

Warnings30 min

Violations30 min

Game Theory

Rand+Human

Controlled

56

User Feedback










49

56



50

Thank you

to sponsors

56

THANK YOU

tambeuscedu


51

56








47

5648




21 days of patrol


Random + Human

Not controlled

0

20

40

60

80

100 Miscellaneous

Drugs

Firearm Violations

0

5

10

15

20

Captures30 min

Warnings30 min

Violations30 min

Game Theory

Rand+Human

Controlled

56

User Feedback










49

56



50

Thank you

to sponsors

56

THANK YOU

tambeuscedu


51

5648




21 days of patrol


Random + Human

Not controlled

0

20

40

60

80

100 Miscellaneous

Drugs

Firearm Violations

0

5

10

15

20

Captures30 min

Warnings30 min

Violations30 min

Game Theory

Rand+Human

Controlled

56

User Feedback










49

56



50

Thank you

to sponsors

56

THANK YOU

tambeuscedu


51

56

User Feedback










49

56



50

Thank you

to sponsors

56

THANK YOU

tambeuscedu


51

56



50

Thank you

to sponsors

56

THANK YOU

tambeuscedu


51

56

THANK YOU

tambeuscedu


51

Key Algorithmic Principles, Deployed Systems, …...Argentina airport USC /56 Global Presence of...

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Transcript of Key Algorithmic Principles, Deployed Systems, …...Argentina airport USC /56 Global Presence of...