Events Calendar

CS Student Colloquium: Benjamin Ford - Adaptive Resource Allocation for Wildlife Protection against Illegal Poachers & Thanh H. Nguyen - Stop the Compartmentalization: Unified Robust Algorithms for Handling Uncertainties in Security Games

Speaker: Benjamin Ford, Thanh H. Nguyen, USC CS

Talk Title: Benjamin Ford - Adaptive Resource Allocation for Wildlife Protection against Illegal Poachers & Thanh H. Nguyen - Stop the Compartmentalization: Unified Robust Algorithms for Handling Uncertainties in Security Games

Series: Student Seminar Series

Abstract: Benjamin Ford - Adaptive Resource Allocation for Wildlife Protection against Illegal Poachers

Abstract: Illegal poaching is an international problem that leads to the extinction of species and the destruction of ecosystems. As evidenced by dangerously dwindling populations of endangered species, existing anti-poaching mechanisms are insufficient. This paper introduces the Protection Assistant for Wildlife Security (PAWS) application - a joint deployment effort done with researchers at Uganda’s Queen Elizabeth National Park (QENP) with the goal of improving wildlife ranger patrols. While previous works have deployed applications with a game-theoretic approach (specifically Stackelberg Games) for counter-terrorism, wildlife crime is an important domain that promotes a wide range of new deployments. Additionally, this domain presents new research challenges and opportunities related to learning behavioral models from collected poaching data. In addressing these challenges, our first contribution is a behavioral model extension that captures the heterogeneity of poachers’ decision making processes. Second, we provide a novel framework, PAWS-Learn, that incrementally improves the behavioral model of the poacher population with more data. Third, we develop a new algorithm, PAWS-Adapt, that adaptively improves the resource allocation strategy against the learned model of poachers. Fourth, we demonstrate PAWS’s potential effectiveness when applied to patrols in QENP, where PAWS will be deployed.

Thanh H. Nguyen - Stop the Compartmentalization: Unified Robust Algorithms for Handling Uncertainties in Security Games

Given the real-world applications of Stackelberg security games (SSGs), addressing uncertainties in these games is a major challenge. Unfortunately, we lack any unified computational framework for handling uncertainties in SSGs. Current state-of-the-art has provided only compartmentalized robust algorithms that handle uncertainty exclusively either in the defender’s strategy or in adversary’s payoff or in the adversary’s rationality, leading to potential failures in real-world scenarios where a defender often faces multiple types of uncertainties. Furthermore, insights for improving performance are not leveraged across the compartments, leading to significant losses in quality or efficiency. In this paper, we provide the following main contributions: 1) we present the first unified framework for handling the uncertainties explored in SSGs; 2) based on this unified framework, we propose the first set of “unified” robust algorithms to address combinations of these uncertainties; 3) we introduce approximate scalable robust algorithms for handling these uncertainties that leverage insights across compartments; 4) we present experiments demonstrating solution quality and runtime advantages of our algorithms.


Host: CS PHD Committee

Location: 101

Audiences: Everyone Is Invited

Contact: Assistant to CS chair