Green Security Game refers to the general framework to model the repeated and strategic interaction in green security domains such as wildlife protection and fishery protection. In Green Security Game framework, the problem in these domains is cast as a repeated game.
PAWS Video (Winner of Best Application of Artificial Intelligence, AAAI Video Competition 2016)
Poaching of endangered species is reaching critical levels as the populations of these species plummet to unsustainable numbers. The global tiger population, for example, has dropped over 95% from the start of the 1900s and has resulted in three out of nine species extinctions. Depending on the area and animals poached, motivations for poaching range from profit to sustenance, with the former being more common when profitable species such as tigers, elephants, and rhinos are the targets.
To counter poaching efforts and to rebuild the species' populations, countries have set up protected wildlife reserves and conservation agencies tasked with defending these large reserves. Because of the size of the reserves and the common lack of law enforcement resources, conservation agencies are at a significant disadvantage when it comes to deterring and capturing poachers. Agencies use patrolling as a primary method of securing the park. Due to their limited resources, however, patrol managers must carefully create patrols that account for many different variables (e.g., limited patrol units to send out, multiple locations that poachers can attack at varying distances to the outpost).
Note: Please click on the picture for more copyright information, from Wikimedia Commons.
Protection Assistant for Wildlife Security (PAWS) aims to assist conservation agencies in their critical role of patrol creation by predicting where poachers will attack and optimizing patrol routes to cover those areas.
PAWS has been tested in the Queen Elizabeth National Park in Uganda.
! PAWS first computes a defender strategy, i.e., a randomized patrolling strategy. When the defender strategy is executed, adversaries may respond, and wildlife crime data are collected by the patrollers. PAWS learns the behavior models of the poachers from the crime data collected, which lead it to change its defender strategy. As the new strategy is executed, poachers may respond again and more crime data will be collected.
PAWS takes basic information about the protected area and information about previous patrolling and poaching activities as input, and generates patrol routes as output. As the patrollers execute the patrol routes, more poaching data will be collected, and feed back to PAWS. The core algorithm of PAWS integrates learning poachers' behavior model, game-theoretic reasoning and route planning.
More specifically, PAWS learns the behavior models of the poachers from the crime data collected. Based on the poachers' behavior model, PAWS calculates a randomized patrolling strategy, in the form of a set of patrol routes and the probabilities of taking each route. PAWS then suggests patrol routes sampled from this strategy to the patrollers.
Preliminary Field Test
A preliminary field test of PAWS was conducted in Uganda's Queen Elizabeth National Park (QENP) in April 2014. PAWS patrols were outputted onto a GPS unit as a series of waypoints. Using the set of waypoints on the GPS as a directional guide, wildlife rangers executed their patrol and searched for signs of illegal activity. The photos below were taking during the preliminary field tests.
Andrew Lemieux (right) demonstrating features of the GPS to a QENP ranger A QENP ranger using the GPS
Wonderful animals at Murchison Fall National Park
How they are being poached: Yes all these snares in one year - Snares that trap these wonderful animals
USC team recently attended the Second Global Tiger Stocktaking Conference. Some pictures.
Second Global Tiger Stocktaking Conference
Patrol in the Sunderbans
USC Team in the Sunderbans
USC team and collaborators patrol in a tropical forest in southeast asia.
Walking along tree trunk
Steep elevation ascent
Deployed PAWS patrols and signs found during patrols.
Tiger sign found
Walking across stream
Human sign found (lighter)
Human sign found (camping site)
Thanh H. Nguyen, Arunesh Sinha, Shahrzad Gholami, Andrew J. Plumptre, Lucas Joppa, Milind Tambe, Margaret Driciru, Fred Wanyama, Aggrey Rwetsiba, Rob Critchlow, Colin Beale. CAPTURE: A New Predictive Anti-Poaching Tool for Wildlife Protection. In Proceedings of the 15th International Conference on Autonomous Agents and Multiagent Systems (AAMAS).
Fei Fang, Thanh H. Nguyen, Benjamin Ford, Nicole Sintov, Milind Tambe. Introduction to Green Security Games (extended abstract). In Workshop on Cognitive Knowledge Acquisition and Applications held at International Joint Conferences on Artificial Intelligence (IJCAI), July 2015.
Fei Fang, Thanh H. Nguyen, Bo An, Milind Tambe, Rob Pickles, Wai Y. Lam, Gopalasamy R. Clements. Towards Addressing Challenges in Green Security Games in the Wild (extended abstract). In Workshop of Behavioral, Economic and Computational Intelligence for Security (BECIS) held at International Joint Conferences on Artificial Intelligence (IJCAI), July 2015.
Debarun Kar, Fei Fang, Francesco Delle Fave, Nicole Sintov, Arunesh Sinha, Aram Galstyan, Bo An, Milind Tambe. Learning Bounded Rationality Models of the Adversary in Repeated Stackelberg Security Games. In ALA Adaptive and Learning Agents Workshop held at International Conference on Autonomous Agents and Multiagent Systems (AAMAS), May 2015.
Debarun Kar, Fei Fang, Francesco Delle Fave, Nicole Sintov, Milind Tambe. Conducting Longitudinal Experiments with Behavioral Models in Repeated Stackelberg Security Games on Amazon Mechanical Turk. In Fourth International Workshop on Human-Agent Interaction Design and Models (HAIDM) held at International Conference on Autonomous Agents and Multiagent Systems (AAMAS), May 2015.
Fei Fang, Thanh Hong Nguyen, Rob Pickles, Lam Wai Yee, Milind Tambe. Challenges of Green Security Games in the Wild. In International Workshop on Issues with Deployment of Emerging Agent-based Systems (IDEAS) held at International Conference on Autonomous Agents and Multiagent Systems (AAMAS), May 2015.
Fei Fang, Peter Stone, Milind Tambe. Designing Defender Strategies Against Frequent Adversary Interaction. In International Workshop on Optimisation in Multi-Agent Systems (OPTMAS) held at International Conference on Autonomous Agents and Multiagent Systems (AAMAS), May 2015.
Debarun Kar, Fei Fang, Francesco Delle Fave, Nicole Sintov, Milind Tambe, Arlette van Wissen. Effectiveness of Probability Perception Modeling and Defender Strategy Generation Algorithms in Repeated Stackelberg Games: An Initial Report. In Computational Sustainability Workshop held at The Twenty-Ninth AAAI Conference on Artificial Intelligence (AAAI-15), January 2015.
Our current work on PAWS utilizes the SUQR behavioral model detailed in the following paper by Thanh Ngyuen, et al. [ pdf ]