CAPTURE: A new predictive anti-poaching tool for wildlife protection

Wildlife poaching presents a serious extinction threat to many animalspecies. Agencies (“defenders”) focused on protecting suchanimals need tools that help analyze, model and predict poacheractivities, so they can more effectively combat such poaching; suchtools could also assist in planning effecti...

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Main Authors: NGUYEN, Thanh H., SINHA, Arunesh, GHOLAMI, Shahrzad, PLUMPTRE, Andrew, JOPPA, Lucas, TAMBE, Milind, DRICIRU, Margaret, WANYAMA, Fred, RWETSIBA, Aggrey, CRITCHLOW, Rob
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Language:English
Published: Institutional Knowledge at Singapore Management University 2016
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Online Access:https://ink.library.smu.edu.sg/sis_research/4660
https://ink.library.smu.edu.sg/context/sis_research/article/5663/viewcontent/48b21abb018d81af83aa5b19537a0f044136.pdf
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spelling sg-smu-ink.sis_research-56632020-11-16T01:04:49Z CAPTURE: A new predictive anti-poaching tool for wildlife protection NGUYEN, Thanh H. SINHA, Arunesh GHOLAMI, Shahrzad PLUMPTRE, Andrew JOPPA, Lucas TAMBE, Milind DRICIRU, Margaret WANYAMA, Fred RWETSIBA, Aggrey CRITCHLOW, Rob Wildlife poaching presents a serious extinction threat to many animalspecies. Agencies (“defenders”) focused on protecting suchanimals need tools that help analyze, model and predict poacheractivities, so they can more effectively combat such poaching; suchtools could also assist in planning effective defender patrols, buildingon the previous security games research.To that end, we have built a new predictive anti-poaching tool,CAPTURE (Comprehensive Anti-Poaching tool with Temporaland observation Uncertainty REasoning). CAPTURE providesfour main contributions. First, CAPTURE’s modeling of poachersprovides significant advances over previous models from behavioralgame theory and conservation biology. This accounts for:(i) the defender’s imperfect detection of poaching signs; (ii) complextemporal dependencies in the poacher’s behaviors; (iii) lackof knowledge of numbers of poachers. Second, we provide twonew heuristics: parameter separation and target abstraction to reducethe computational complexity in learning the poacher models.Third, we present a new game-theoretic algorithm for computingthe defender’s optimal patrolling given the complex poachermodel. Finally, we present detailed models and analysis of realworldpoaching data collected over 12 years in Queen ElizabethNational Park in Uganda to evaluate our new model’s predictionaccuracy. This paper thus presents the largest dataset of real-worlddefender-adversary interactions analyzed in the security games literature.CAPTURE will be tested in Uganda in early 2016. 2016-05-01T07:00:00Z text application/pdf https://ink.library.smu.edu.sg/sis_research/4660 https://ink.library.smu.edu.sg/context/sis_research/article/5663/viewcontent/48b21abb018d81af83aa5b19537a0f044136.pdf http://creativecommons.org/licenses/by-nc-nd/4.0/ Research Collection School Of Computing and Information Systems eng Institutional Knowledge at Singapore Management University Databases and Information Systems
institution Singapore Management University
building SMU Libraries
continent Asia
country Singapore
Singapore
content_provider SMU Libraries
collection InK@SMU
language English
topic Databases and Information Systems
spellingShingle Databases and Information Systems
NGUYEN, Thanh H.
SINHA, Arunesh
GHOLAMI, Shahrzad
PLUMPTRE, Andrew
JOPPA, Lucas
TAMBE, Milind
DRICIRU, Margaret
WANYAMA, Fred
RWETSIBA, Aggrey
CRITCHLOW, Rob
CAPTURE: A new predictive anti-poaching tool for wildlife protection
description Wildlife poaching presents a serious extinction threat to many animalspecies. Agencies (“defenders”) focused on protecting suchanimals need tools that help analyze, model and predict poacheractivities, so they can more effectively combat such poaching; suchtools could also assist in planning effective defender patrols, buildingon the previous security games research.To that end, we have built a new predictive anti-poaching tool,CAPTURE (Comprehensive Anti-Poaching tool with Temporaland observation Uncertainty REasoning). CAPTURE providesfour main contributions. First, CAPTURE’s modeling of poachersprovides significant advances over previous models from behavioralgame theory and conservation biology. This accounts for:(i) the defender’s imperfect detection of poaching signs; (ii) complextemporal dependencies in the poacher’s behaviors; (iii) lackof knowledge of numbers of poachers. Second, we provide twonew heuristics: parameter separation and target abstraction to reducethe computational complexity in learning the poacher models.Third, we present a new game-theoretic algorithm for computingthe defender’s optimal patrolling given the complex poachermodel. Finally, we present detailed models and analysis of realworldpoaching data collected over 12 years in Queen ElizabethNational Park in Uganda to evaluate our new model’s predictionaccuracy. This paper thus presents the largest dataset of real-worlddefender-adversary interactions analyzed in the security games literature.CAPTURE will be tested in Uganda in early 2016.
format text
author NGUYEN, Thanh H.
SINHA, Arunesh
GHOLAMI, Shahrzad
PLUMPTRE, Andrew
JOPPA, Lucas
TAMBE, Milind
DRICIRU, Margaret
WANYAMA, Fred
RWETSIBA, Aggrey
CRITCHLOW, Rob
author_facet NGUYEN, Thanh H.
SINHA, Arunesh
GHOLAMI, Shahrzad
PLUMPTRE, Andrew
JOPPA, Lucas
TAMBE, Milind
DRICIRU, Margaret
WANYAMA, Fred
RWETSIBA, Aggrey
CRITCHLOW, Rob
author_sort NGUYEN, Thanh H.
title CAPTURE: A new predictive anti-poaching tool for wildlife protection
title_short CAPTURE: A new predictive anti-poaching tool for wildlife protection
title_full CAPTURE: A new predictive anti-poaching tool for wildlife protection
title_fullStr CAPTURE: A new predictive anti-poaching tool for wildlife protection
title_full_unstemmed CAPTURE: A new predictive anti-poaching tool for wildlife protection
title_sort capture: a new predictive anti-poaching tool for wildlife protection
publisher Institutional Knowledge at Singapore Management University
publishDate 2016
url https://ink.library.smu.edu.sg/sis_research/4660
https://ink.library.smu.edu.sg/context/sis_research/article/5663/viewcontent/48b21abb018d81af83aa5b19537a0f044136.pdf
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