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

TY - GEN

T1 - CAPTURE

T2 - 15th International Conference on Autonomous Agents and Multiagent Systems, AAMAS 2016

AU - Nguyen, Thanh H.

AU - Sinha, Arunesh

AU - Gholami, Shahrzad

AU - Plumptre, Andrew

AU - Joppa, Lucas

AU - Tambe, Milind

AU - Driciru, Margaret

AU - Wanyama, Fred

AU - Rwetsiba, Aggrey

AU - Critchlow, Rob

AU - Beale, Colin M.

PY - 2016

Y1 - 2016

N2 - Wildlife poaching presents a serious extinction threat to many animal species. Agencies ("defenders") focused on protecting such animals need tools that help analyze, model and predict poacher activities, so they can more effectively combat such poaching; such tools could also assist in planning effective defender patrols, building on the previous security games research. To that end, we have built a new predictive anti-poaching tool, CAPTURE (Comprehensive Anti-Poaching tool with Temporal and observation Uncertainty REasoning). CAPTURE provides four main contributions. First, CAPTURE's modeling of poachers provides significant advances over previous models from behavioral game theory and conservation biology. This accounts for: (i) the defender's imperfect detection of poaching signs; (ii) complex temporal dependencies in the poacher's behaviors; (iii) lack of knowledge of numbers of poachers. Second, we provide two new heuristics: parameter separation and target abstraction to reduce the computational complexity in learning the poacher models. Third, we present a new game-theoretic algorithm for computing the defender's optimal patrolling given the complex poacher model. Finally, we present detailed models and analysis of real-world poaching data collected over 12 years in Queen Elizabeth National Park in Uganda to evaluate our new model's prediction accuracy. This paper thus presents the largest dataset of real-world defender-adversary interactions analyzed in the security games literature. CAPTURE will be tested in Uganda in early 2016.

AB - Wildlife poaching presents a serious extinction threat to many animal species. Agencies ("defenders") focused on protecting such animals need tools that help analyze, model and predict poacher activities, so they can more effectively combat such poaching; such tools could also assist in planning effective defender patrols, building on the previous security games research. To that end, we have built a new predictive anti-poaching tool, CAPTURE (Comprehensive Anti-Poaching tool with Temporal and observation Uncertainty REasoning). CAPTURE provides four main contributions. First, CAPTURE's modeling of poachers provides significant advances over previous models from behavioral game theory and conservation biology. This accounts for: (i) the defender's imperfect detection of poaching signs; (ii) complex temporal dependencies in the poacher's behaviors; (iii) lack of knowledge of numbers of poachers. Second, we provide two new heuristics: parameter separation and target abstraction to reduce the computational complexity in learning the poacher models. Third, we present a new game-theoretic algorithm for computing the defender's optimal patrolling given the complex poacher model. Finally, we present detailed models and analysis of real-world poaching data collected over 12 years in Queen Elizabeth National Park in Uganda to evaluate our new model's prediction accuracy. This paper thus presents the largest dataset of real-world defender-adversary interactions analyzed in the security games literature. CAPTURE will be tested in Uganda in early 2016.

KW - Security game

KW - Temporal behavorial model

KW - Wildlife protection

UR - http://www.scopus.com/inward/record.url?scp=85013662316&partnerID=8YFLogxK

M3 - Conference contribution

AN - SCOPUS:85013662316

SP - 767

EP - 775

BT - AAMAS 2016 - Proceedings of the 2016 International Conference on Autonomous Agents and Multiagent Systems

PB - International Foundation for Autonomous Agents and Multiagent Systems (IFAAMAS)

Y2 - 9 May 2016 through 13 May 2016

ER -