Artificial Intelligence and Conservation

Explains how artificial intelligence methods can be used to aid conservation of wildlife, forests, coral reefs, rivers, and other natural resources.

Fei Fang (Edited by), Milind Tambe (Edited by), Bistra Dilkina (Edited by), Andrew J. Plumptre (Edited by)

9781108464734, Cambridge University Press

Paperback / softback, published 28 March 2019

246 pages, 53 b/w illus.
22.8 x 15.1 x 1.5 cm, 0.37 kg

'The book Artificial Intelligence and Conservation brings together, for the first time, examples of computational research that address problems in conservation. The contributors articulate the challenges of using artificial intelligence to solve biodiversity conservation problems, as well as the urgency of the necessity to do so. It is a sweeping exposition, ranging from using game theory to fight poaching, to optimizing decisions through Markov modeling to manage invasive species. The collection showcases how rigorous computational research can make an impact helping save our planet's wildlife. The readers will not only discover interesting computational problems and solutions, but will be inspired to work in this unique application of artificial intelligence and, I hope, will solve the many pressing open problems posed by the authors.' Tanya Berger-Wolf, University of Illinois, Chicago

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.

Part I: 1. Introduction Fei Fang, Milind Tambe, Bistra Dilkina and Andrew J. Plumptre
Part II: 2. Law enforcement for wildlife conservation Andrew J. Plumptre
3. Wildlife poaching forecasting based on ranger-collected data and evaluation through field tests Shahrzad Gholami, Benjamin Ford, Debarun Kar, Fei Fang, Andrew J. Plumptre, Milind Tambe, Margaret Driciru, Fred Wanyama, Aggrey Rwetsiba, Mustafa Nsubuga and Joshua Mabonga
4. Optimal patrol planning against black-box attackers Haifeng Xu, Benjamin Ford, Fei Fang, Bistra Dilkina, Andrew J. Plumptre, Milind Tambe, Margaret Driciru, Fred Wanyama, Aggrey Rwetsiba, Mustafa Nsubuga and Joshua Mabonga
5. Automatic detection of poachers and wildlife with UAVs Elizabeth Bondi, Fei Fang, Mark Hamilton, Debarun Kar, Donnabell Dmello, Venil Noronha, Jongmoo Choi, Robert Hannaford, Arvind Iyer, Lucas Joppa, Milind Tambe and Ram Nevatia
Part III: 6. Protecting coral reef ecosystems via efficient patrols Yue Yin and Bo An
7. Simultaneous optimization of strategic and tactical planning for environmental sustainability and security Sara M. McCarthy, Milind Tambe, Christopher Kiekintveld, Meredith L. Gore and Alex Killion
8. NECTAR Benjamin Ford, Matthew Brown, Amulya Yadav, Amandeep Singh, Arunesh Sinha, Biplav Srivastava, Christopher Kiekintveld and Milind Tambe
9. Connecting conservation research and implementation Sean McGregor, Rachel M. Houtman, Ronald Metoyer and Thomas G. Dietterich
10. Probabilistic inference with generating functions for animal populations Daniel Sheldon, Kevin Winner and Debora Sujono
11. Engaging citizen scientists in data collection for conservation Yexiang Xue and Carla P. Gomes
12. Simulator-defined Markov decision processes H. Jo Albers, Thomas G. Dietterich, Kim Hall, Majid A. Taleghan and Katherine Lee.

Subject Areas: Artificial intelligence [UYQ], Environmental science, engineering & technology [TQ], Social impact of environmental issues [RNT], Conservation of the environment [RNK], Applied ecology [RNC], Plant ecology [PSTS]