Data-Aware Game Theory and Mechanism Design

for Security, Sustainability and Mobility

Fei Fang

feifang@cmu.edu

Wean Hall 4126

Consider working with 1~2 Ph.D. students

Societal Challenges: Security and Sustainability

8/23/2017Fei Fang2

Societal Challenges: Security and Sustainability

Today

≈ 3,200

100 years ago

≈ 60,000

2/24/2016Fei Fang3

Societal Challenges: Security and Sustainability

Physical Infrastructure Transportation Networks Cyber Systems

Environmental Resources Endangered Wildlife Fisheries

2/24/2016Fei Fang4

Societal Challenges: Security and Sustainability

Improve tactics of patrol, inspection, screening etc

8/23/2017Fei Fang

Game Theoretic

Reasoning

Attacker

Defender

5

Game Theoretic Reasoning

Limited resource allocation

Adversary surveillance

Target #1 Target #2

Target #1 5, -3 -1, 1

Target #2 -5, 4 2, -1

Adversary

Defender

2/24/2016Fei Fang6/64

Game Theoretic Reasoning

Limited resource allocation

Adversary surveillance

Target #1 Target #2

Target #1 5, -3 -1, 1

Target #2 -5, 4 2, -1

Adversary

Defender

2/24/2016Fei Fang7/64

Game Theoretic Reasoning

Randomization make defender unpredictable

Stackelberg Security game

Defender: Commits to mixed strategy

Adversary: Conduct surveillance and best responds

Target #1 Target #2

Target #1 5, -3 -1, 1

Target #2 -5, 4 2, -1

Adversary

Defender

55.6%

44.4%

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Game Theoretic Reasoning

Compute optimal defender strategy

Polynomial time solvable in games with finite actions and

simple structures [Conitzer06]

NP-Hard in general settings [Korzhyk10]

SSE=NE for zero-sum games, SSE⊂NE for games with

special properties [Yin10]

Research Challenges

Massive scale games with constraints

Plan/reason under uncertainty

Repeated interaction

8/23/2017Fei Fang9/67

Game Theoretic Reasoning

8/23/2017Fang, F., Jiang, A. X., & Tambe, M. (2013). Protecting moving targets with multiple

mobile resources. Journal of Artificial Intelligence Research, 48, 583-634.

10/67

Attempt to address the research challenges

Behind the Scene

Optimal defender strategy can be computed in polynomial time in spatio-temporal security games with infinite action set [Fang13, Behnezhad17] Analyze equivalent class and dominance relationship

Exploit spatio-temporal structure

Bi-level optimization Construct Linear Program or Mixed Integer Linear Program

Column generation, branch and bound etc

Improve robustness against uncertainty Equilibrium refinement

8/23/201711/67 Fang, F., Jiang, A. X., & Tambe, M. (2013). Protecting moving targets with multiple

mobile resources. Journal of Artificial Intelligence Research, 48, 583-634.

Societal Challenges: Security and Sustainability

Improve tactics of patrol, inspection, screening etc

8/23/2017Fei Fang12/67

Machine Learning

Fine-Grained

Planning

Game Theoretic

Reasoning

Machine Learning

Learn from data

Predict threat: Classification / Regression

Build and learn behavioral model

Source of data

Human subject experiments

Real-world data

Research Challenges

Sparsity

Class imbalance

Uncertainty / noise

8/23/2017Fei Fang13

Machine Learning

Attempt to address the research

challenges

Queen Elizabeth National Park

Features

Terrain (e.g., forest, slope)

Distance to {Town, Water, Outpost}

Monthly Ranger Coverage

Labels

Crime Observations

Real-world deployment

1-month trial test

8-month controlled test

8/23/2017Kar & Ford et al., 2017; Gholami & Ford et al., 2017

High Low

Catch Per

Unit Effort0.12 0.01

14

Behind the Scene

Hybrid spatio-temporal models

Decision Trees

Markov Random Fields

Behavioral game theory

Quantal response-based models

8/23/2017Fei Fang15

Societal Challenges: Security and Sustainability

Improve tactics of patrol, inspection, screening etc

8/23/2017Fei Fang

Machine Learning

Fine-Grained

Planning

Game Theoretic

Reasoning

16

Fine-Grained Planning

8/23/2017Fang et al. Deploying PAWS: Field Optimization of the Protection Assistant for

Wildlife Security. In IAAI, 2016.

17

Fine-Grained Planning

8/23/2017Fei Fang18

Behind the Scene

Hierarchical Modeling

Find implementable game-theoretic solutions

Incremental constraint generation

Cutting plane

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PAWS (Protection Assistant for Wildlife Security)

8/23/2017

Protected Area Information

Past Patrolling and Poaching Information

Patrol RoutesPoaching Data Collected

Machine Learning

Game-theoretic Reasoning

Fine-Grained Planning

Fang et al. Deploying PAWS: Field Optimization of the Protection Assistant for

Wildlife Security. In IAAI, 2016.

20

Real-World Deployment

In collaboration with Panthera, Rimba

Regular deployment since July 2015 (Malaysia)

8/23/2017Fang et al. Deploying PAWS: Field Optimization of the Protection Assistant for

Wildlife Security. In IAAI, 2016.

21

Real-World Deployment

Animal Footprint

Tiger Sign

Tree Mark

Lighter

Camping Sign

8/23/2017Fang et al. Deploying PAWS: Field Optimization of the Protection Assistant for

Wildlife Security. In IAAI, 2016.

22

Societal Challenges: Security and Sustainability

Improve tactics of patrol, inspection, screening etc

(Future Directions)

Deep Learning

Learn from camera & drone

Learn from simulated game instances

Reinforcement Learning

Data-aware reasoning / planning

When action provide information for future

Transfer Learning

Learn across sites / domains

Integrate various resources

Collect data and conduct patrol

8/23/2017Fei Fang23

Societal Challenges: Security and Sustainability

Improve tactics of patrol, inspection, screening etc

What else?

Improve operational planning

Decide the rule of the game before playing it

Reason about external options and evolving threats

Model the unknowns in and outside the problem scope

Take into account bystanders

Three (or more)-player games

8/23/2017Fei Fang24

Societal Challenges: Mobility

New modes of transportation

8/23/2017Fei Fang

Image from: http://lighthouse-sf.org/ Image from: http://transitized.com/

25

Societal Challenges: Mobility

Ensure efficiency of on-demand ridesharing through

scheduling and pricing

8/23/2017Fei Fang26

Scheduling

Mechanism Design

Pricing

Mechanism Design

8/23/2017Fei Fang27

Behind the Scene

Spatial-Temporal Pricing

Assignment rule: decompose a min-cost flow

Payment rule: price = welfare gain difference

𝑝𝑎,𝑏,𝑡 = Φ𝑎,𝑡 −Φ𝑏,𝑡+𝑑𝑖𝑠𝑡 𝑎,𝑏

Φ𝑎,𝑡 ≜ 𝑊 𝐷 ∪ 𝑡, 𝑇, 𝑎 , 𝑅 −𝑊(𝐷, 𝑅)

Prove desired properties

Envy-freeness

Incentive compatible subgame perfect equilibrium

8/23/2017Fei Fang28

Societal Challenges: Mobility

Ensure efficiency of on-demand ridesharing through

scheduling and pricing (Future Directions)

8/23/2017Fei Fang29

Machine Learning

Predict demand/supply

Learn behavioral models

Scheduling

Mechanism Design

Pricing

Societal Challenges: Mobility

Ensure efficiency of on-demand ridesharing through

scheduling and pricing

What else?

Fairness and subsidies

Multi-modal ridesharing

Subscription-based service + on-demand service

8/23/2017Fei Fang30

AI and Social Good

AI research that can deliver societal benefits now and

in the near future

8/23/2017Fei Fang31

Data-Aware Game Theory and Mechanism Design

for Security, Sustainability and Mobility

Fei Fang

feifang@cmu.edu

Wean Hall 4126

Consider working with 1~2 Ph.D. students

Backup Slides

8/23/2017Fei Fang33

Mechanism Design

8/23/2017Fei Fang34

$80: Trip Price

$100: Value of riders picked up

$10: Value of riders not picked up

Game Theoretic Reasoning

Goal: minimize trespass distance

No patrols

Higher density

Lower density

No patrols

8/23/2017Johnson, M. P., Fang, F., & Tambe, M. (2012). Patrol Strategies to Maximize

Pristine Forest Area. In AAAI.

35/67

Machine Learning

8/23/2017Kar, D., Fang, F., Delle Fave, F., Sintov, N., & Tambe, M. (2015). A game of thrones:

when human behavior models compete in repeated Stackelberg security games.

36/67

Forest Protection

Goal: protect valuable trees

8/23/2017Kamra, N., Fang, F., Kar, D., Liu, Y. & Tambe, M.. Handling Continuous Space

Security Games with Neural Networks. In IWAISe-17

37/67

Behind the Scene

Policy gradient

Fictitious play

CNN

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