Security is a global concern. Real-world security problems range from domains such as the protection of ports, airports, and transportation from terrorists to protecting forests, wildlife, and
fisheries from smugglers, poachers, and illegal fishermen. 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 the responses
of adversaries and potential uncertainties over their types, priorities, and knowledge. Stackelberg
Security Games (SSG) have drawn a significant amount of interest from security agencies by
capturing the strategic interaction between security agencies and human adversaries. SSG-based
decision aids are in widespread use (both nationally and internationally) for the protection of
assets such as major ports in the US, airport terminals, and wildlife and fisheries.
My research focuses on addressing uncertainties in SSGs — one recognized area of weakness.
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 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 imperfect poaching data and complex temporal dependencies in 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 maximinbased 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, minimax regret and maximin can be used in different security situations which may demand different
robust criteria. I then present novel robust algorithms to compute minimax regret for addressing
A contribution of particular significance is that my work is deployed in the real world; I have
deployed my robust algorithms and behavioral models in the PAWS system, which is currently
being used by NGOs (Panthera and Rimba) in a conservation area in Malaysia.