data-aware game theory and mechanism design for security, … · 2017. 8. 23. · societal...
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Data-Aware Game Theory and Mechanism Design
for Security, Sustainability and Mobility
Fei Fang
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
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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
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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
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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
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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
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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
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Behind the Scene
Hybrid spatio-temporal models
Decision Trees
Markov Random Fields
Behavioral game theory
Quantal response-based models
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Societal Challenges: Security and Sustainability
Improve tactics of patrol, inspection, screening etc
8/23/2017Fei Fang
Machine Learning
Fine-Grained
Planning
Game Theoretic
Reasoning
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Fine-Grained Planning
8/23/2017Fang et al. Deploying PAWS: Field Optimization of the Protection Assistant for
Wildlife Security. In IAAI, 2016.
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Fine-Grained Planning
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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.
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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.
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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.
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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
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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
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Societal Challenges: Mobility
New modes of transportation
8/23/2017Fei Fang
Image from: http://lighthouse-sf.org/ Image from: http://transitized.com/
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Societal Challenges: Mobility
Ensure efficiency of on-demand ridesharing through
scheduling and pricing
8/23/2017Fei Fang26
Scheduling
Mechanism Design
Pricing
Mechanism Design
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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
Wean Hall 4126
Consider working with 1~2 Ph.D. students
Backup Slides
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Mechanism Design
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$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.
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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.
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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
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Behind the Scene
Policy gradient
Fictitious play
CNN
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