Wildlife GUARDSS: Using Uncertain Real-Time Information in Signaling Games for Sustainability

Mobile sensors, e.g., unmanned aerial vehicles (UAVs), are becoming increasingly important in security domains and can be used for tasks such as searching for poachers in conservation areas. Such mobile sensors augment human patrollers by assisting in surveillance and in signaling potentially deceptive information to adversaries, and their coordinated deployment could be modeled via the well-known security games framework. Unfortunately, real-world uncertainty in the sensor’s detection of adversaries presents major challenges in the sensors’ use. This leads to significant detriments in security performance. We first discuss the current shortcomings in more detail, and then propose a novel game model that incorporates uncertainty with sensors. We then briefly introduce GUARDSS, the algorithm to solve these games, and show results from a simulation based on a real-world deployment of a conservation system in South Africa.

[1]  Lasse Becker-Czarnetzki.  Report on DeepStack Expert-Level Artificial Intelligence in Heads-Up No-Limit Poker , 2019 .

[2]  Sarit Kraus,et al.  Optimal Cruiser-Drone Traffic Enforcement Under Energy Limitation , 2018, IJCAI.

[3]  Jakub Cerný,et al.  Incremental Strategy Generation for Stackelberg Equilibria in Extensive-Form Games , 2018, EC.

[4]  Bo An,et al.  Camera Placement Based on Vehicle Traffic for Better City Security Surveillance , 2018, IEEE Intelligent Systems.

[5]  Ramakant Nevatia,et al.  SPOT Poachers in Action: Augmenting Conservation Drones With Automatic Detection in Near Real Time , 2018, AAAI.

[6]  Noam Brown,et al.  Superhuman AI for heads-up no-limit poker: Libratus beats top professionals , 2018, Science.

[7]  Haifeng Xu,et al.  Strategic Coordination of Human Patrollers and Mobile Sensors With Signaling for Security Games , 2018, AAAI.

[8]  Fernando Ordóñez,et al.  Building Real Stackelberg Security Games for Border Patrols , 2017, GameSec.

[9]  Sarit Kraus,et al.  When Security Games Hit Traffic: Optimal Traffic Enforcement Under One Sided Uncertainty , 2017, IJCAI.

[10]  Kevin Waugh,et al.  Theoretical and Practical Advances on Smoothing for Extensive-Form Games , 2017, EC.

[11]  Kevin Waugh,et al.  DeepStack: Expert-level artificial intelligence in heads-up no-limit poker , 2017, Science.

[12]  Nicola Basilico,et al.  Adversarial patrolling with spatially uncertain alarm signals , 2015, Artif. Intell..

[13]  Nicola Basilico,et al.  A Security Game Combining Patrolling and Alarm-Triggered Responses Under Spatial and Detection Uncertainties , 2016, AAAI.

[14]  Branislav Bosanský,et al.  Using Correlated Strategies for Computing Stackelberg Equilibria in Extensive-Form Games , 2016, AAAI.

[15]  Juliane Hahn,et al.  Security And Game Theory Algorithms Deployed Systems Lessons Learned , 2016 .

[16]  Miguel A. Olivares-Méndez,et al.  Towards an Autonomous Vision-Based Unmanned Aerial System against Wildlife Poachers , 2015, Sensors.

[17]  Pascal Mettes,et al.  Nature Conservation Drones for Automatic Localization and Counting of Animals , 2014, ECCV Workshops.

[18]  Bo An,et al.  Regret-Based Optimization and Preference Elicitation for Stackelberg Security Games with Uncertainty , 2014, AAAI.

[19]  M. Mulero-Pázmány,et al.  Remotely Piloted Aircraft Systems as a Rhinoceros Anti-Poaching Tool in Africa , 2014, PloS one.

[20]  Manish Jain,et al.  Risk-Averse Strategies for Security Games with Execution and Observational Uncertainty , 2011, AAAI.

[21]  Rong Yang,et al.  Improving Resource Allocation Strategy against Human Adversaries in Security Games , 2011, IJCAI.

[22]  Gerald G. Brown,et al.  Solving Defender-Attacker-Defender Models for Infrastructure Defense , 2011, ICS 2011.

[23]  Vincent Conitzer,et al.  Computing the optimal strategy to commit to , 2006, EC '06.