Motivation

AI for Conservation refers to the general framework to model the repeated and strategic interaction in green security domains such as wildlife protection and fishery protection. In the AI for Conservation framework, the problem in these domains is cast as a repeated game. While our predictive analytics effort focuses on predicting where adversaries (e.g., poachers) will strike, our prescriptive analytics work provides recommendations to defenders (e.g., rangers) to conduct strategic, randomized patrols.

Milind Tambe

Debarun Kar

Benjamin Ford

Shahrzad Gholami

Fei Fang

Thanh Hong Nguyen

Rong Yang

Francesco Maria Delle Fave

Rob Pickles, Panthera

Wai Y. Lam Gopalasamy R. Clements, Panthera & Rimba

Andrew Lemieux, Nethelands Institute for the Study of Crime and Law Enforcement

Andrew J, Plumptre, Wildlife Conservation Society

Lucas Joppa, Microsoft Research

Arnaud Lyet, World Wildlife Fund

Nicole Sintov, Sol Price School of Public Policy, USC

Bo An, Nanyang Technological University

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