To ensure security, public safety, and environmental sustainability, law enforcement and security agencies often operate with a very limited budget and resources to combat illegal activities such as attacks on critical infrastructure, urban crimes, poaching, and illegal logging. Advanced game-theoretic models and algorithms have been developed for analyzing threats in such scenarios that can be described as having attackers and defenders. These techniques have been effective in helping defenders allocate resources to defend against threats. Now, in a modern information environment dominated by social networks and increased connectivity in all aspects of life, defenders will need to consider the influence of the "information hubs" (IHs) in that environment that collect, process and disseminate information. IHs include all sorts of connected devices in the growing "Internet of Things", as well as the people in the social networks. This project aims to design new game theoretic models that account for the influence of IHs on attacker-defender interactions and develop methods to help the defender optimally select and allocate IHs for best defensive effect. This project will also develop methods to find the optimal allocation of defensive units given the presence of IHs so that the defender can achieve maximal benefit with minimal resources.
Towards gaining a full understanding of the role of IHs in adversarial settings, the researchers will (1) develop new game-theoretic approaches that extend models such as Stackelberg security games to allow the IHs to communicate with the agents; (2) theoretically analyze the computational complexity of optimally selecting and allocating IHs to maximize the defender's expected utility in the game, and provide Mixed Integer Linear Programming (MILP)-based solution approach to compute the optimal selection and allocation, as well as approximation schemes and heuristic algorithms for large-scale problem instances; and (3) develop MILP-based algorithms to compute robust strategies against uncertainties in information collection, processing and dissemination. The researchers will evaluate the new algorithms both in simulation and for real-world conservation scenarios.
This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.
