Using Game Theory in Real Time in the Real World: A Conservation Case Study (Demonstration)


Elizabeth Bondi, Hoon Oh, Haifeng Xu, Fei Fang, Bistra Dilkina, and Milind Tambe. 2019. “Using Game Theory in Real Time in the Real World: A Conservation Case Study (Demonstration) .” In International Conference on Autonomous Agents and Multiagent Systems (Demonstration) (AAMAS-19).


In the real world, real-time data are now widely available, especially in security domains. Security cameras, aerial imagery, and even social media keep defenders informed when protecting important events, locations, and people. Further, advances in artificial intelligence have led to tools that can interpret these data automatically. Game theoretic models, for example, have shown great success in security. However, most of them ignore real-time information. In this paper, we demonstrate the potential to use real-time information from imagery to better inform our decisions in game theoretic models for security. As a concrete example, a conservation group called Air Shepherd uses conservation drones equipped with thermal infrared cameras to locate poachers at night and alert park rangers. They have also used lights aboard the drones, or signaled, to warn poachers of their presence, which often deters the poachers. We propose a system that (i) allocates drones and humans strategically throughout a protected area, (ii) detects poachers in the thermal infrared videos recorded by the conservation drones flying through the protected area in the predetermined location, and (iii) recommends moving to the location and/or signaling to the poacher that a patroller is nearby depending on real-time detections. View the demonstration.
See also: 2019