Visionary Security: Using Uncertain Real-Time Information in Signaling Games

Abstract:

In important domains from natural resource conservation to public safety, real-time information is becoming increasingly important. Strategic deployment of security cameras and mobile sensors such as drones can provide real-time updates on illegal activities. To help plan for such strategic deployments of sensors and human patrollers, as well as warning signals to ward off adversaries, the defender-attacker security games framework can be used. [Zhang et al., 2019] has shown that real-time data (e.g., human view from a helicopter) may be used in conjunction with security game models to interdict criminals. Other recent work relies on real-time information from sensors that can notify the patroller when an opponent is detected [Basilico et al., 2017; Xu et al., 2018]. Despite considering real-time information in all cases, these works do not consider the combined situation of uncertainty in real-time information in addition to strategically signaling to adversaries. In this thesis, we will not only address this gap, but also improve the overall security result by considering security game models and computer vision algorithms together. A major aspect of this work is in applying it to real-world challenges, such as conservation. Although it applies to many environmental challenges, such as protecting forests and avoiding illegal mining, we will focus particularly on reducing poaching of endangered wildlife as an example. To reduce poaching, human patrollers typically search for snares and poaching activity as they patrol, as well as intervene if poaching activity is found. Drones are useful patrolling aids due to their ability to cover additional ground, but they must interpret their environments, notify nearby human patrollers for intervention, and send potentially deceptive signals to the adversary to deter poaching. Rather than treating these as separate tasks, models must coordinate to handle challenges found in real-world conservation scenarios (Fig. 1). We will determine the success of this work both in simulated experiments and through work with conservation agencies such as Air Shepherd to implement the system in the real world.