Events Calendar

PhD Defense - Thanh Nguyen

Title: Combating Adversaries under Uncertainties in Real-world Security Problems: Advanced Game-theoretic Behavioral Models and Robust Algorithms

Location: EEB 248

Date: April 8th

Time: 10am-12pm

Phd Candidate: Thanh Nguyen

Committee members:

Prof. Milind Tambe (Chair)
Prof. David Kempe
Prof. Jonathan Gratch
Prof. William Halfond
Prof. Richard John
Prof. Ariel Procaccia

Abstract:


Security is a global concern. Real-world security problems range from domains such as the protection of ports and airports from terrorists to protecting forests and wildlife from smugglers and poachers. A key challenge in solving these security problems is that security resources are limited; not all targets can be protected all the time. Therefore, security resources must be deployed intelligently, taking into account responses of attackers and potential uncertainties over their types, preference, and knowledge. Stackelberg Security Games (SSG) have drawn a significant amount of interest from security agencies. SSG-based decision aids are in widespread use for the protection of assets such as major ports in the US and airport terminals.

My research focuses on addressing uncertainties in SSGs --- one recognized area of weakness in SSGs. For example, adversary payoff values can be extremely difficult to assess and are generally characterized by significant uncertainty. My thesis provides innovative techniques and significant advances in addressing these uncertainties in SSGs. First, in many security problems, human adversaries are known to be boundedly rational, and often choose targets with non-highest expected value to attack. I introduce novel behavioral models of adversaries which significantly advance the state-of-the-art models in capturing the adversaries' decision making. More specifically, my new model for predicting poachers'behavior in wildlife protection is the first game-theoretic model which takes into account key domain challenges including the imperfect poaching data and complex temporal dependencies in the poachers' behavior. The superiority of my new models over the existing ones is demonstrated via extensive experiments based on the biggest real-world poaching dataset collected in a national park in Uganda over 12 years. Second, my research also focuses on developing new robust algorithms which address uncertainties in real-world security problems. I present the first unified maximin-based robust algorithm - a single algorithm -to handle all different types of uncertainties explored in SSGs. Furthermore, I propose a less conservative decision criterion; minimax regret for generating new, candidate defensive strategies that handle uncertainties in SSGs. In fact, this is the first time minimax regret has ever been used for addressing uncertainties in SSGs. I then present novel robust algorithms to compute minimax regret for addressing payoff uncertainty.

A contribution of particular significance is that my work is deployed in the real-world; I have deployed my robust algorithms and behavioral models for the PAWS system, which is currently being used by NGOs (Panthera and Rimba) in a conservation area in Malaysia.

Location: 248

Audiences: Everyone Is Invited

Contact: Lizsl De Leon