Green Security Games (GSGs) have been successfully used in the protection of fisheries, forests, and wildlife.

In the past decade, game-theoretic applications have been successfully deployed in the real world, where there aren’t large budgets or plan outlays for security services. . Inspired by the success, researchers have begun focusing on applying game theory to green security domains such as protection of forests, fish, and wildlife, forming a stream of research on Green Security Games (GSGs).

Researchers from the Indian Institute of Technology Madras and Harvard University have developed a novel machine-learning algorithm named ‘CombSGPO’ (Combined Security Game Policy Optimisation) that can help in saving wildlife from poachers.

Green Security Games (GSGs) is a general framework employing Game Theory for tackling wildlife poaching and other crimes against the environment. Various influential organizations like Microsoft, Simons Institute, and Harvard are working on this. Here, in this case, it is being used for unified resource allocation and patrolling in the real world in real-time amidst all uncertainties.

As per the World Wide Fund for Nature (WWF), the wildlife trade poses the second-biggest direct threat to the survival of species after habitat destruction. While several organizations and regulatory authorities are trying to curb the incidences of poaching, the poachers seem to have always remained one step ahead of the patrollers. This collaborative research work by two esteemed universities will help in keeping poaching incidents in check.

What’s different? This new model with help of the CombSGPO algorithm will take care of resource allocation and patrolling stages simultaneously. It will find the best-case scenario for the defender using previous incidents and interactions between poachers and the defenders. It will strategically signal the other drones and human patrollers.

The 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 algorithm works by handling resource allocation and strategizing patrolling after the extent of resources available had been identified. For this task, it utilises data on the animal population in the conserved area and assumes that poachers are aware of the patrolling being done at various sites.

Prof. Balaraman Ravindran from IIT Madras, collaborated with Prof. Milind Tambe’s Research Group – Teamcore – at Harvard University, U.S., to carry out this study.

“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,” said Prof. Balaraman Ravindran, Head, Robert Bosch Centre for Data Science and Artificial Intelligence (RBCDSAI), IIT Madras.

This developed algorithm utilises a Game Theory-based model created by the researchers. (Game theory is a theoretical framework for conceiving social situations among competing players.) In the context of wildlife protection, Game Theory pertains to predicting the areas where poaching may take place. These predictions are based on the earlier poaching incidents and the interaction between poachers and defenders.

Inputs from INDIAai , a MeitY based Nasscom initiative

  Artificial Intelligence, development, IIT, World Wide Fund for Nature (WWF)