visakhapatnam researchers at 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 save wildlife from poachingthe algorithm works by handling resource allocation and strategising patrolling after the extent of resources available have been identified it utilises data on animal population in the conserved area and assumes that poachers are aware of patrolling at various sitesthe researchers found that combined and coordinated use of forest rangers and drones was a good way to protect wildlife from poaching as these resources are limited this new algorithm provides efficient strategies that are more scalable than earlier onesprofessor department of computer science and engineering and the head of robert bosch centre for data science and artificial intelligence iit madras prof balaraman ravindran collaborated with prof milind tambes research group teamcore at harvard university to carry out this studyprof 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 and illegal logging and fishing the resources we consider are human patrollers forest rangers and surveillance drones which have object detectors mounted on them for animals and poachers he addedthis algorithm utilises the game theorybased model created by the researchers in the context of wildlife protection game theory pertains to predicting the areas where poaching may take place these predictions are based on earlier poaching incidents and the interaction between poachers and defendersfirst author of the study and a postbaccalaureate fellow rbcdsai iit madras aravind venugopal said the game model and the kind of resources they use to simulate such a poaching game between the defender forest rangers and drones and attackers poachers are based on the widelystudied stackelberg security game model