By the same authors

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

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Author(s)

  • Thanh H. Nguyen
  • Arunesh Sinha
  • Shahrzad Gholami
  • Andrew Plumptre
  • Lucas Joppa
  • Milind Tambe
  • Margaret Driciru
  • Fred Wanyama
  • Aggrey Rwetsiba
  • Rob Critchlow
  • Colin M. Beale

Department/unit(s)

Publication details

Title of host publicationAAMAS 2016 - Proceedings of the 2016 International Conference on Autonomous Agents and Multiagent Systems
DatePublished - 2016
Pages767-775
Number of pages9
PublisherInternational Foundation for Autonomous Agents and Multiagent Systems (IFAAMAS)
Original languageEnglish
ISBN (Electronic)9781450342391

Abstract

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.

    Research areas

  • Security game, Temporal behavorial model, Wildlife protection

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