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On repeated stackelberg security game with the cooperative human behavior model for wildlife protection

  • Binru Wang
  • Yuan Zhang
  • Zhi-Hua Zhou
  • Sheng Zhong
Article
  • 32 Downloads

Abstract

Inspired by successful deployments of Stackelberg Security Game in real life, researchers are working hard to optimize the game models to make them more practical. Recent security game work on wildlife protection makes a step forward by taking the possible cooperation among attackers into consideration. However, it models attackers to have complete rationality, which is not always possible in practice given they are human beings. We aim to tackle attackers’ bounded rationality in the complicated, cooperation-enabled and multi-round security game for wildlife protection. Specifically, we construct a repeated Stackelberg game, and propose a novel adaptive human behavior model for attackers based on it. Despite generating defender’s optimal strategy requires to solve a non-linear and non-convex optimization problem, we are able to propose an efficient algorithm that approximately solve this problem. We perform extensive real-life experiments, and results from over 25,000 game plays show our solution effectively helps the defender to deal with attackers who might cooperate.

Keywords

Defender strategy Cooperation mechanism Repeated stackelberg Games Human behavior Wildlife protection 

References

  1. 1.
    Abbasi YD, Short M, Sinha A, Sintov N, Zhang C, Tambe M (2015) Human adversaries in opportunistic crime security games: Evaluating competing bounded rationality models. In: Proceedings of the third annual conference on advances in cognitive systems ACS, p 2Google Scholar
  2. 2.
    Breton M, Alj A, Haurie A (1988) Sequential stackelberg equilibria in two-person games. J Optim Theory Appl 59(1):71–97MathSciNetCrossRefGoogle Scholar
  3. 3.
    Camerer CF, Ho TH, Chong JK (2004) A cognitive hierarchy model of games. Q J Econ 119(3):861–898CrossRefGoogle Scholar
  4. 4.
    Conitzer V, Sandholm T (2006) Computing the optimal strategy to commit to. In: Proceedings of the 7th ACM conference on electronic commerce. ACM, pp 82–90Google Scholar
  5. 5.
    Costa-Gomes M, Crawford VP, Broseta B (2001) Cognition and behavior in normal-form games: an experimental study. Econometrica 69(5):1193–1235CrossRefGoogle Scholar
  6. 6.
    Fang F, Nguyen TH, Pickles R, Lam WY, Clements GR, An B, Singh A, Tambe M, Lemieux A (2016) Deploying paws: Field optimization of the protection assistant for wildlife security. In: Proceedings of the twenty-eighth innovative applications of artificial intelligence conference, pp 3966–3973Google Scholar
  7. 7.
    Fang F, Stone P, Tambe M (2015) When security games go green: Designing defender strategies to prevent poaching and illegal fishing. In: Proceedings of the 24th international conference on artificial intelligence, pp 2589–2595Google Scholar
  8. 8.
    Gholami S, Wilder B, Brown M, Sinha A, Sintov N, Tambe M (2016) A game theoretic approach on addressing cooperation among human adversaries. In: Proceedings of the 15th international conference on autonomous agents and multiagent systemsGoogle Scholar
  9. 9.
    Haskell WB, Kar D, Fang F, Tambe M, Cheung S, Denicola E (2014) Robust protection of fisheries with compass. In: AAAI, pp 2978–2983Google Scholar
  10. 10.
    Huw D (2001) Some thoughts on artificial intelligence and economic theory. In: Surfing economics: essays for the enquiring economist. PalgraveGoogle Scholar
  11. 11.
    Kahneman D (2003) Maps of bounded rationality: psychology for behavioral economics. Amer Econ Rev 93 (5):1449–1475CrossRefGoogle Scholar
  12. 12.
    Kar D, Fang F, Delle Fave F, Sintov N, Tambe M (2015) A game of thrones: when human behavior models compete in repeated stackelberg security games. In: Proceedings of the 2015 international conference on autonomous agents and multiagent systems. International Foundation for Autonomous Agents and Multiagent Systems, pp 1381–1390Google Scholar
  13. 13.
    Leitmann G (1978) On generalized Stackelberg strategies. J Optim Theory Appl 26(4):637–643MathSciNetCrossRefGoogle Scholar
  14. 14.
    Letchford J, Conitzer V, Munagala K (2009) Learning and approximating the optimal strategy to commit to. In: International symposium on algorithmic game theory. Springer, pp 250– 262Google Scholar
  15. 15.
    Marecki J, Tesauro G, Segal R (2012) Playing repeated stackelberg games with unknown opponents. In: Proceedings of the 11th international conference on autonomous agents and multiagent systems-volume 2. International Foundation for Autonomous Agents and Multiagent Systems, pp 821–828Google Scholar
  16. 16.
    McFadden DL (1976) Quantal choice analaysis: a survey. In: Annals of economic and social measurement, vol 5, no 4. NBER, pp 363–390Google Scholar
  17. 17.
    Misener R, Floudas CA (2013) Glomiqo: global mixed-integer quadratic optimizer. J Glob Optim 57(1):3–50MathSciNetCrossRefGoogle Scholar
  18. 18.
    Montesh M (2013) Rhino poaching: a new form of organised crime. Technical report, College of Law Research and Innovation Committee of the University of South AfricaGoogle Scholar
  19. 19.
    Nguyen TH, Yang R, Azaria A, Kraus S, Tambe M (2013) Analyzing the effectiveness of adversary modeling in security games. In: Proceedings of the twenty-seventh AAAI conference on artificial intelligence, pp 718–724Google Scholar
  20. 20.
    Paruchuri P, Pearce JP, Marecki J, Tambe M, Ordonez F, Kraus S (2008) Playing games for security: an efficient exact algorithm for solving bayesian stackelberg games. In: Proceedings of the 7th international joint conference on autonomous agents and multiagent systems-volume 2. International Foundation for Autonomous Agents and Multiagent Systems, pp 895– 902Google Scholar
  21. 21.
    Payne JW, Bettman JR, Johnson EJ (1992) Behavioral decision research: a constructive processing perspective. Ann Rev Psychol 43(1):87–131CrossRefGoogle Scholar
  22. 22.
    Rubinstein A (1998) Modeling bounded rationality. MIT press, CambridgeGoogle Scholar
  23. 23.
    Secretariat GTI (2013) Global tiger recovery program implementation plan: 2013–14. Report, The World Bank, WashingtonGoogle Scholar
  24. 24.
    Simon HA (1955) A behavioral model of rational choice. Quart J Econ 69(1):99–118CrossRefGoogle Scholar
  25. 25.
    Simon HA (1979) Rational decision making in business organizations. Amer Econ Rev 69(4):493–513Google Scholar
  26. 26.
    Tambe M (2011) Security and game theory: algorithms, deployed systems, lessons learned. University Press, CambridgeCrossRefGoogle Scholar
  27. 27.
    Vigerske S, Gleixner A (2016) Scip: global optimization of mixed-integer nonlinear programs in a branch-and-cut framework. Technical Report, Technical Report 16-24, ZIB, Takustr. 7, Berlin, p 14195Google Scholar
  28. 28.
    Wyler LS, Sheikh PA (2013) International illegal trade in wildlife: Threats and US policy. BiblioGovGoogle Scholar
  29. 29.
    Yang R, Ford B, Tambe M, Lemieux A (2014) Adaptive resource allocation for wildlife protection against illegal poachers. In: Proceedings of the 2014 international conference on autonomous agents and multi-agent systems, pp 453–460Google Scholar
  30. 30.
    Yang R, Ordonez F, Tambe M (2012) Computing optimal strategy against quantal response in security games. In: Proceedings of the 11th international conference on autonomous agents and multiagent systems-volume 2. International Foundation for Autonomous Agents and Multiagent Systems, pp 847–854Google Scholar

Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2018

Authors and Affiliations

  1. 1.State Key Laboratory for Novel Software TechnologyNanjing UniversityNanjingChina

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