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We test all the proposed algorithms on Green Security Games motivated by scenarios in green security domains such as defending against poaching and illegal fishing

When Security Games Go Green: Designing Defender Strategies to Prevent Poaching and Illegal Fishing.

IJCAI, pp.2589-2595, (2015)

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Abstract

Building on the successful applications of Stackelberg Security Games (SSGs) to protect infrastructure, researchers have begun focusing on applying game theory to green security domains such as protection of endangered animals and fish stocks. Previous efforts in these domains optimize defender strategies based on the standard Stackelberg...More

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Introduction
  • Poaching and illegal over-fishing are critical international problems leading to destruction of ecosystems.
  • Previous work in green security domains [Yang et al, 2014; Haskell et al, 2014] models the problem as a game with multiple rounds and each round is a SSG [Yin et al, 2010] where the defender commits to a mixed strategy and the attackers respond to it.
Highlights
  • Poaching and illegal over-fishing are critical international problems leading to destruction of ecosystems
  • We aim to provide automated decision aid to defenders in green security domains who defend against human adversaries such as poachers who have no automated tools — we model the poachers as being boundedly rational and having bounded memory
  • We test all the proposed algorithms on Green Security Games motivated by scenarios in green security domains such as defending against poaching and illegal fishing
  • Each round corresponds to 30 days and each poacher/fisherman will choose a target to place snares/fishnets every day
  • FS-1 is equivalent to calculating the defender strategy with a perfect Stackelberg assumption, which is used in previous work [Yang et al, 2014; Haskell et al, 2014], as the defender uses the same strategy in every round and the attackers’ belief coincides with the defender strategy
  • The field had been lacking an appropriate gametheoretic model for green security domains: this paper provides Green Security Games (GSG) to fill this gap
Results
  • Previous models in green security domains, e.g., such as [Yang et al, 2014; Haskell et al, 2014] can be seen as a special case of GSGs, as they assume that the attackers always have up-to-date information, whereas GSGs allow for more generality and planning of defender strategies.
  • An alternative is the myopic strategy, i.e., the defender can always protect the targets with the highest expected utility in the current round.
  • Previous work in green security domains [Yang et al, 2014; Haskell et al, 2014] treats each data point, e.g., each snare or fishnet, as an independent attacker and applies MLE to select the most probable parameter vector.
  • The input of the algorithm includes the number of attacks χi found on each target i ∈ [N ], the attackers’ belief of the defender strategy η, and the prior distribution p = p1, ..., pS over a discrete set of parameter values {ω} = {ω1, ..., ωS}, each of which is a 3-element tuple.
  • In the extreme case of α0 = 1, i.e., the attackers have perfect knowledge of the current defender strategy, the problem reduces to a repeated Stackelberg game and all approaches provide similar solution quality.
  • ĂůƉŚĂϬ ϬϬϬϬ ϬϭϮρ ϬϮρϬ Ϭϯϳρ ϬρϬϬ ϬςϮρ ϬϳρϬ ϬΘϳρ ϭϬϬϬ
  • GSG’s generalization of the Stackelberg assumption which is commonly used in previous work has led it provide two new planning algorithms as well as a new learning framework, providing a significant advance over previous work in green security domains [Yang et al, 2014; Haskell et al, 2014].
Conclusion
  • Most of the work considers the case where each player chooses one action from his finite action set in each round of the game, while the authors focus on the problem motivated by real-world green security challenges where the players can choose a mixed strategy and implement it for multiple episodes in each round; previous approaches fail to apply in the domains.
  • The authors generalize the Stackelberg assumption to fit green security domains and provide algorithms to learn the parameters in the attackers’ bounded rationality model.
Tables
  • Table1: Summary of key notations
  • Table2: Payoff structure of Example 3
Download tables as Excel
Related work
  • So far, the field had been lacking an appropriate gametheoretic model for green security domains: this paper provides Green Security Games (GSG) to fill this gap. GSG’s generalization of the Stackelberg assumption which is commonly used in previous work has led it provide two new planning algorithms as well as a new learning framework, providing a significant advance over previous work in green security domains [Yang et al, 2014; Haskell et al, 2014].

    Additional related work includes criminological work on poaching and illegal fishing [Lemieux, 2014; Beirne and South, 2007], but a game-theoretic approach is completely missing in this line of research. Planning and learning in repeated games against opponents with bounded memory has been studied [Sabourian, 1998; Powers and Shoham, 2005; Chakraborty et al, 2013; de Cote and Jennings, 2010; Banerjee and Peng, 2005]. However, most of the work considers the case where each player chooses one action from his finite action set in each round of the game, while we focus on the problem motivated by real-world green security challenges where the players can choose a mixed strategy and implement it for multiple episodes in each round; thus previous approaches fail to apply in our domains. We further handle multiple boundedly rational attackers each with a different SUQR model, leading to a need to learn heterogeneous parameters in the SUQR model, which was not addressed in this prior work which assume a single fully rational attacker. Previous work on learning in repeated SSGs [Marecki et al, 2012; Letchford et al, 2009; Blum et al, 2014] has mainly focused on learning the payoffs of attackers assuming perfectly rational attackers. In contrast, we not only generalize the Stackelberg assumption to fit green security domains but also provide algorithms to learn the parameters in the attackers’ bounded rationality model. By embedding models of bounded rationality in GSG, we complement previous work that focus on modeling human bounded rationality and bounded memory [Rubinstein, 1997; Cowan, 2005].
Funding
  • This research was supported by MURI Grant W911NF-11-10332 and by the United States Department of Homeland Security through the Center for Risk and Economic Analysis of Terrorism Events (CREATE) under grant number 2010-ST061-RE0001
  • LARG research is supported in part by grants from the National Science Foundation (CNS-1330072, CNS-1305287), ONR (21C184-01), AFOSR (FA8750-14-1-0070, FA955014-1-0087), and Yujin Robot
Reference
  • [Banerjee and Peng, 2005] Bikramjit Banerjee and Jing Peng. Efficient learning of multi-step best response. In AAMAS, pages 60–66, 2005.
    Google ScholarLocate open access versionFindings
  • [Beirne and South, 2007] Piers Beirne and Nigel South, editors. Issues in Green Criminology. Willan Publishing, 2007.
    Google ScholarFindings
  • [Blum et al., 2014] Avrim Blum, Nika Haghtalab, and Ariel D. Procaccia. Learning optimal commitment to overcome insecurity. In NIPS, 2014.
    Google ScholarLocate open access versionFindings
  • [Brown et al., 2014] Matthew Brown, William B. Haskell, and Milind Tambe. Addressing scalability and robustness in security games with multiple boundedly rational adversaries. In Conference on Decision and Game Theory for Security (GameSec), 2014.
    Google ScholarLocate open access versionFindings
  • [Chakraborty et al., 2013] Doran Chakraborty, Noa Agmon, and Peter Stone. Targeted opponent modeling of memorybounded agents. In Proceedings of the Adaptive Learning Agents Workshop (ALA), 2013.
    Google ScholarLocate open access versionFindings
  • [Cowan, 2005] N. Cowan. Working Memory Capacity. Essays in cognitive psychology. Psychology Press, 2005.
    Google ScholarFindings
  • [de Cote and Jennings, 2010] Enrique Munoz de Cote and Nicholas R. Jennings. Planning against fictitious players in repeated normal form games. In AAMAS, pages 1073– 1080, 2010.
    Google ScholarLocate open access versionFindings
  • [Eliason, 1993] Scott Eliason. Maximum Likelihood Estimation. Logic and Practice., volume 96 of Quantitative Applications in the Social Sciences. Sage Publications, 1993.
    Google ScholarLocate open access versionFindings
  • [Haskell et al., 2014] William B. Haskell, Debarun Kar, Fei Fang, Milind Tambe, Sam Cheung, and Lt. Elizabeth Denicola. Robust protection of fsheries with COmPASS. In IAAI, 2014.
    Google ScholarLocate open access versionFindings
  • [Kiekintveld et al., 2009] Christopher Kiekintveld, Manish Jain, Jason Tsai, James Pita, Fernando Ordonez, and Milind Tambe. Computing optimal randomized resource allocations for massive security games. In AAMAS, 2009.
    Google ScholarLocate open access versionFindings
  • [Korzhyk et al., 2010] Dmytro Korzhyk, Vincent Conitzer, and Ronald Parr. Complexity of computing optimal stackelberg strategies in security resource allocation games. In AAAI, pages 805–810, 2010.
    Google ScholarLocate open access versionFindings
  • [Lemieux, 2014] Andrew M Lemieux, editor. Situational Prevention of Poaching. Crime Science Series. Routledge, 2014.
    Google ScholarFindings
  • [Letchford et al., 2009] Joshua Letchford, Vincent Conitzer, and Kamesh Munagala. Learning and approximating the optimal strategy to commit to. In Proceedings of the 2nd International Symposium on Algorithmic Game Theory, pages 250–262, 2009.
    Google ScholarLocate open access versionFindings
  • [Marecki et al., 2012] Janusz Marecki, Gerry Tesauro, and Richard Segal. Playing repeated stackelberg games with unknown opponents. In AAMAS, pages 821–828, 2012.
    Google ScholarLocate open access versionFindings
  • [Nguyen et al., 2013] Thanh H. Nguyen, Rong Yang, Amos Azaria, Sarit Kraus, and Milind Tambe. Analyzing the effectiveness of adversary modeling in security games. In AAAI, 2013.
    Google ScholarFindings
  • [Pita et al., 2008] James Pita, Manish Jain, Craig Western, Christopher Portway, Milind Tambe, Fernando Ordonez, Sarit Kraus, and Praveen Paruchuri. Deployed ARMOR protection: The application of a game theroetic model for security at the los angeles international airport. In AAMAS, 2008.
    Google ScholarLocate open access versionFindings
  • [Powers and Shoham, 2005] Rob Powers and Yoav Shoham. Learning against opponents with bounded memory. In IJCAI, IJCAI’05, pages 817–822, San Francisco, CA, USA, 2005. Morgan Kaufmann Publishers Inc.
    Google ScholarLocate open access versionFindings
  • [Rubinstein, 1997] Ariel Rubinstein. Modeling Bounded Rationality, volume 1 of MIT Press Books. The MIT Press, December 1997.
    Google ScholarFindings
  • [Sabourian, 1998] Hamid Sabourian. Repeated games with m-period bounded memory (pure strategies). Journal of Mathematical Economics, 30(1):1 – 35, 1998.
    Google ScholarLocate open access versionFindings
  • [Secretariat, 2013] G. T. I. Secretariat. Global tiger recovery program implementation plan: 2013-14. Technical report, The World Bank, Washington, D.C., 2013.
    Google ScholarFindings
  • [Shieh et al., 2012] Eric Shieh, Bo An, Rong Yang, Milind Tambe, Craig Baldwin, Joseph DiRenzo, Ben Maule, and Garrett Meyer. PROTECT: A deployed game theoretic system to protect the ports of the United States. In AAMAS, 2012.
    Google ScholarLocate open access versionFindings
  • [Yang et al., 2014] Rong Yang, Benjamin Ford, Milind Tambe, and Andrew Lemieux. Adaptive resource allocation for wildlife protection against illegal poachers. In AAMAS, 2014.
    Google ScholarLocate open access versionFindings
  • [Yin et al., 2010] Zhengyu Yin, Dmytro Korzhyk, Christopher Kiekintveld, Vincent Conitzer, and Milind Tambe. Stackelberg vs. nash in security games: Interchangeability, equivalence, and uniqueness. In AAMAS, 2010.
    Google ScholarLocate open access versionFindings
  • [Yin et al., 2012] Zhengyu Yin, Albert Jiang, Matthew Johnson, Milind Tambe, Christopher Kiekintveld, Kevin Leyton-Brown, Tuomas Sandholm, and John Sullivan. TRUSTS: Scheduling randomized patrols for fare inspection in transit systems. In IAAI, 2012.
    Google ScholarLocate open access versionFindings
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