Artificial intelligence and conservation:
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 natura...
Gespeichert in:
| Weitere Verfasser: | , , , |
|---|---|
| Format: | Buch |
| Sprache: | English |
| Veröffentlicht: |
Cambridge ; New York ; Melbourne ; New Delhi ; Singapore
Cambridge University Press
2019
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| Schriftenreihe: | Artificial intelligence for social good
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| Schlagworte: | |
| Zusammenfassung: | 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 |
| Beschreibung: | x, 236 Seiten Illustrationen, Diagramme |
| ISBN: | 9781316512920 1316512924 9781108464734 1108464734 |
Internformat
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| 505 | 8 | |a Law enforcement for wildlife conservation -- Wildlife poaching forecasting based on ranger-collected data and evaluation through field tests -- Optimal patrol planning against black-box attackers -- Automatic detection of poachers and wildlife with UAVs -- Protecting coral reef ecosystems via efficient patrols -- Simultaneous optimization of strategic and tactical planning for environmental sustainability and security -- NECTAR: enforcing environmental compliance through strategically randomized factory inspections -- Connecting conservation research and implementation: building a wildfire assistant -- Probablistic inference with generating functions for animal populations -- Engaging citizen scientists in data collection for conservation -- Simulator-defined markov decision processes: a case study in managing bio-invasions | |
| 520 | 3 | |a 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 | |
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| 653 | 0 | |a Artificial intelligence | |
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Datensatz im Suchindex
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| author2 | Fang, Fei 1989- Tambe, Milind Dilkina, Bistra Plumptre, Andrew J. |
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| author_facet | Fang, Fei 1989- Tambe, Milind Dilkina, Bistra Plumptre, Andrew J. |
| building | Verbundindex |
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| contents | Law enforcement for wildlife conservation -- Wildlife poaching forecasting based on ranger-collected data and evaluation through field tests -- Optimal patrol planning against black-box attackers -- Automatic detection of poachers and wildlife with UAVs -- Protecting coral reef ecosystems via efficient patrols -- Simultaneous optimization of strategic and tactical planning for environmental sustainability and security -- NECTAR: enforcing environmental compliance through strategically randomized factory inspections -- Connecting conservation research and implementation: building a wildfire assistant -- Probablistic inference with generating functions for animal populations -- Engaging citizen scientists in data collection for conservation -- Simulator-defined markov decision processes: a case study in managing bio-invasions |
| ctrlnum | (OCoLC)1102274786 (DE-599)BVBBV045872581 |
| discipline | Informatik |
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| isbn | 9781316512920 1316512924 9781108464734 1108464734 |
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| spelling | Artificial intelligence and conservation edited by Fei Fang (Carnegie Mellon University) Milind Tambe (University of Southern California) Bistra Dilkina (Georgia Institute of Technology), Andrew J. Plumptre (Key Biodiversity Areas Secretariat) Cambridge ; New York ; Melbourne ; New Delhi ; Singapore Cambridge University Press 2019 x, 236 Seiten Illustrationen, Diagramme txt rdacontent n rdamedia nc rdacarrier Artificial intelligence for social good Law enforcement for wildlife conservation -- Wildlife poaching forecasting based on ranger-collected data and evaluation through field tests -- Optimal patrol planning against black-box attackers -- Automatic detection of poachers and wildlife with UAVs -- Protecting coral reef ecosystems via efficient patrols -- Simultaneous optimization of strategic and tactical planning for environmental sustainability and security -- NECTAR: enforcing environmental compliance through strategically randomized factory inspections -- Connecting conservation research and implementation: building a wildfire assistant -- Probablistic inference with generating functions for animal populations -- Engaging citizen scientists in data collection for conservation -- Simulator-defined markov decision processes: a case study in managing bio-invasions 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 Künstliche Intelligenz (DE-588)4033447-8 gnd rswk-swf Artenschutz (DE-588)4112598-8 gnd rswk-swf Wildlife conservation / Technological innovations Artificial intelligence 1\p (DE-588)4143413-4 Aufsatzsammlung gnd-content Künstliche Intelligenz (DE-588)4033447-8 s Artenschutz (DE-588)4112598-8 s 2\p DE-604 Fang, Fei 1989- (DE-588)1186842245 edt Tambe, Milind (DE-588)1161469362 edt Dilkina, Bistra edt Plumptre, Andrew J. edt 1\p cgwrk 20201028 DE-101 https://d-nb.info/provenance/plan#cgwrk 2\p cgwrk 20201028 DE-101 https://d-nb.info/provenance/plan#cgwrk |
| spellingShingle | Artificial intelligence and conservation Law enforcement for wildlife conservation -- Wildlife poaching forecasting based on ranger-collected data and evaluation through field tests -- Optimal patrol planning against black-box attackers -- Automatic detection of poachers and wildlife with UAVs -- Protecting coral reef ecosystems via efficient patrols -- Simultaneous optimization of strategic and tactical planning for environmental sustainability and security -- NECTAR: enforcing environmental compliance through strategically randomized factory inspections -- Connecting conservation research and implementation: building a wildfire assistant -- Probablistic inference with generating functions for animal populations -- Engaging citizen scientists in data collection for conservation -- Simulator-defined markov decision processes: a case study in managing bio-invasions Künstliche Intelligenz (DE-588)4033447-8 gnd Artenschutz (DE-588)4112598-8 gnd |
| subject_GND | (DE-588)4033447-8 (DE-588)4112598-8 (DE-588)4143413-4 |
| title | Artificial intelligence and conservation |
| title_auth | Artificial intelligence and conservation |
| title_exact_search | Artificial intelligence and conservation |
| title_full | Artificial intelligence and conservation edited by Fei Fang (Carnegie Mellon University) Milind Tambe (University of Southern California) Bistra Dilkina (Georgia Institute of Technology), Andrew J. Plumptre (Key Biodiversity Areas Secretariat) |
| title_fullStr | Artificial intelligence and conservation edited by Fei Fang (Carnegie Mellon University) Milind Tambe (University of Southern California) Bistra Dilkina (Georgia Institute of Technology), Andrew J. Plumptre (Key Biodiversity Areas Secretariat) |
| title_full_unstemmed | Artificial intelligence and conservation edited by Fei Fang (Carnegie Mellon University) Milind Tambe (University of Southern California) Bistra Dilkina (Georgia Institute of Technology), Andrew J. Plumptre (Key Biodiversity Areas Secretariat) |
| title_short | Artificial intelligence and conservation |
| title_sort | artificial intelligence and conservation |
| topic | Künstliche Intelligenz (DE-588)4033447-8 gnd Artenschutz (DE-588)4112598-8 gnd |
| topic_facet | Künstliche Intelligenz Artenschutz Aufsatzsammlung |
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