IIT-M & Harvard Develop Algorithm to Automate Rangers & Drones, Reduce Poaching

In a bid to protect wildlife from poachers in India and around the world, researchers at the Indian Institute of Technology, Madras, and Harvard University have developed a novel machine learning (ML) algorithm called ‘CombSGPO’ (Combined Security Game Policy Optimization).

This ML algorithm was developed following a study led by Professor Balaraman Ravindran from the Department of Computer Science and Engineering at IIT Madras, who is also the head of the institute’s Robert Bosch Centre for Data Science and Artificial Intelligence, in collaboration with Professor Milind Tambe’s Research Group– Teamcore– at Harvard University.

In their peer-reviewed paper they co-authored titled, ‘Reinforcement Learning for Unified Allocation and Patrolling in Signaling Games with Uncertainty’, the authors note, “CombSGPO depends on knowledge of an underlying animal density and on assumptions of certain knowledge by poachers, such as the poacher knowing an initial allocation in the patrolling stage. If a poacher knew the defender’s location at all times, he may be able to evade the rangers, and a better strategy for the defender may be to simply protect high-value(d) targets.”

They go on to add, “We also assume that poachers know that signalling implies rangers are responding to prevent poaching from occurring. In the real world, it is possible that poachers would not know this is the case and may fear worse, such as the drone being armed. There may also be adverse effects of signalling on animals. Any deployment must therefore be done with transparency and collaboration with local stakeholders, especially in traditionally marginalized communities. Depending on this local context, other interventions may be better, or maybe used together with strategic signalling and CombSGPO.”

Researchers found that combined and coordinated use of forest rangers and drones was a good way to protect wildlife from poaching. As the resources (Rangers and drones) are limited, the researchers developed this algorithm which provides a good strategy to protect wildlife with the resources available. “This new algorithm provides highly efficient strategies that are more scalable than the earlier ones created for the same purpose,” the authors claim.

Highlighting the need for such Research, Professor Balaraman Ravindran, said, “The work was motivated by the need to perform strategic resource allocation and patrolling in green security domains to prevent illegal activities such as wildlife poaching, illegal logging and illegal fishing. The resources we consider are human patrollers (forest rangers) and surveillance drones, which have object detectors mounted on them for animals and poachers and can perform strategic signalling and communicate with each other as well as the human patrollers.”

This developed algorithm utilizes a game theory-based model created by the researchers. To the uninitiated, according to Investopedia, “Game theory is a theoretical framework for conceiving social situations among competing players.” In the context of wildlife protection, game theory helps predict the areas where poaching may take place. These predictions are based on the earlier poaching incidents and the interaction between poachers and defenders.