With the increasing public interest in artificial intelligence (AI), there is also increasing interest in learning about the benefits that AI can deliver to society. This book focuses on research advances in AI that benefit the conservation of wildlife, forests, coral reefs, rivers, and other natural resources. It presents how the joint efforts of researchers in computer science, ecology, economics, and psychology help address the goals of the United Nations' 2030 Agenda for Sustainable Development. Written at a level accessible to conservation professionals and AI researchers, the book offers both an overview of the field and an in-depth view of how AI is being used to understand patterns in wildlife poaching and enhance patrol efforts in response, covering research advances, field tests and real-world deployments. The book also features efforts in other major conservation directions, including protecting natural resources, ecosystem monitoring, and bio-invasion management through the use of game theory, machine learning, and optimization.
Author(s): Fei Fang, Milind Tambe, Bistra Dilkina, Andrew J. Plumptre
Series: Artificial Intelligence for Social Good
Publisher: Cambridge University Press
Year: 2019
Language: English
Pages: 248
Contents......Page 5
Contributors......Page 7
1 Introduction......Page 12
PART I......Page 26
2 Law Enforcement for Wildlife Conservation......Page 28
3 Wildlife Poaching Forecasting Based on Ranger-Collected Data and Evaluation through Field Tests......Page 40
4 Optimal Patrol Planning Against Black-Box Attackers......Page 68
5 Automatic Detection of Poachers and Wildlife with UAVs......Page 88
PART II......Page 112
6 Protecting Coral Reef Ecosystems via Efficient Patrols......Page 114
7 Simultaneous Optimization of Strategic and Tactical Planning for Environmental Sustainability and Security......Page 129
8 NECTAR: Enforcing Environmental Compliance through Strategically Randomized Factory Inspections......Page 147
9 Connecting Conservation Research and Implementation: Building a Wildfire Assistant......Page 162
10 Probabilistic Inference with Generating Functions for Animal Populations......Page 188
11 Engaging Citizen Scientists in Data Collection for Conservation......Page 205
12 Simulator-Defined Markov Decision Processes: A Case Study in Managing Bio-invasions......Page 221
Glossary......Page 242
Index......Page 246
