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Making the Most of Our Regrets: Regret-Based Solutions to Handle Payoff Uncertainty and Elicitation in Green Security Games

  • Thanh H. Nguyen
  • Francesco M. Delle Fave
  • Debarun Kar
  • Aravind S. Lakshminarayanan
  • Amulya Yadav
  • Milind Tambe
  • Noa Agmon
  • Andrew J. Plumptre
  • Margaret Driciru
  • Fred Wanyama
  • Aggrey Rwetsiba
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9406)

Abstract

Recent research on Green Security Games (GSG), i.e., security games for the protection of wildlife, forest and fisheries, relies on the promise of an abundance of available data in these domains to learn adversary behavioral models and determine game payoffs. This research suggests that adversary behavior models (capturing bounded rationality) can be learned from real-world data on where adversaries have attacked, and that game payoffs can be determined precisely from data on animal densities. However, previous work has, as yet, failed to demonstrate the usefulness of these behavioral models in capturing adversary behaviors based on real-world data in GSGs. Previous work has also been unable to address situations where available data is insufficient to accurately estimate behavioral models or to obtain the required precision in the payoff values.

In addressing these limitations, as our first contribution, this paper, for the first time, provides validation of the aforementioned adversary behavioral models based on real-world data from a wildlife park in Uganda. Our second contribution addresses situations where real-world data is not precise enough to determine exact payoffs in GSG, by providing the first algorithm to handle payoff uncertainty in the presence of adversary behavioral models. This algorithm is based on the notion of minimax regret. Furthermore, in scenarios where the data is not even sufficient to learn adversary behaviors, our third contribution is to provide a novel algorithm to address payoff uncertainty assuming a perfectly rational attacker (instead of relying on a behavioral model); this algorithm allows for a significant scaleup for large security games. Finally, to reduce the problems due to paucity of data, given mobile sensors such as Unmanned Aerial Vehicles (UAV), we introduce new payoff elicitation strategies to strategically reduce uncertainty.

Keywords

Unman Aerial Vehicle Online Appendix Uncertainty Interval Piecewise Linear Approximation Animal Density 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

Notes

Acknowledgements

This research was supported by MURI Grant W911NF-11-1-0332 and by CREATE under grant number 2010-ST-061-RE0001. We wish to acknowledge the contribution of all the rangers and wardens in Queen Elizabeth National Park to the collection of law enforcement monitoring data in MIST and the support of Uganda Wildlife Authority, Wildlife Conservation Society and MacArthur Foundation, US State Department and USAID in supporting these data collection financially.

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Copyright information

© Springer International Publishing Switzerland 2015

Authors and Affiliations

  • Thanh H. Nguyen
    • 1
  • Francesco M. Delle Fave
    • 1
  • Debarun Kar
    • 1
  • Aravind S. Lakshminarayanan
    • 2
  • Amulya Yadav
    • 1
  • Milind Tambe
    • 1
  • Noa Agmon
    • 3
  • Andrew J. Plumptre
    • 4
  • Margaret Driciru
    • 5
  • Fred Wanyama
    • 5
  • Aggrey Rwetsiba
    • 5
  1. 1.University of Southern CaliforniaLos AngelesUSA
  2. 2.Indian Institute of Technology MadrasChennaiIndia
  3. 3.Bar-Ilan UniversityRamat GanIsrael
  4. 4.Wildlife Conservation Society New YorkUSA
  5. 5.Uganda Wildlife AuthorityKampalaUganda

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