On the Inducibility of Stackelberg Equilibrium for Security Games

Strong Stackelberg equilibrium (SSE) is the standard solution concept of Stackelberg security games. As opposed to the weak Stackelberg equilibrium (WSE), the SSE assumes that the follower breaks ties in favor of the leader and this is widely acknowledged and justified by the assertion that the defender can often induce the attacker to choose a preferred action by making an infinitesimal adjustment to her strategy. Unfortunately, in security games with resource assignment constraints, the assertion might not be valid; it is possible that the defender cannot induce the desired outcome. As a result, many results claimed in the literature may be overly optimistic. To remedy, we first formally define the utility guarantee of a defender strategy and provide examples to show that the utility of SSE can be higher than its utility guarantee. Second, inspired by the analysis of leader's payoff by Von Stengel and Zamir (2004), we provide the solution concept called the inducible Stackelberg equilibrium (ISE), which owns the highest utility guarantee and always exists. Third, we show the conditions when ISE coincides with SSE and the fact that in general case, SSE can be extremely worse with respect to utility guarantee. Moreover, introducing the ISE does not invalidate existing algorithmic results as the problem of computing an ISE polynomially reduces to that of computing an SSE. We also provide an algorithmic implementation for computing ISE, with which our experiments unveil the empirical advantage of the ISE over the SSE.

[1]  Milind Tambe,et al.  Security and Game Theory: IRIS – A Tool for Strategic Security Allocation in Transportation Networks , 2011, AAMAS 2011.

[2]  Vincent Conitzer,et al.  Stackelberg vs. Nash in Security Games: An Extended Investigation of Interchangeability, Equivalence, and Uniqueness , 2011, J. Artif. Intell. Res..

[3]  Zhi Yuan,et al.  Scalable Randomized Patrolling for Securing Rapid Transit Networks , 2013, IAAI.

[4]  Bo An Game Theoretic Analysis of Security and Sustainability , 2017, IJCAI.

[5]  Manish Jain,et al.  Security Games with Arbitrary Schedules: A Branch and Price Approach , 2010, AAAI.

[6]  Steven Okamoto,et al.  Solving non-zero sum multiagent network flow security games with attack costs , 2012, AAMAS.

[7]  Tuomas Sandholm Solving imperfect-information games , 2015, Science.

[8]  G. Leitmann On generalized Stackelberg strategies , 1978 .

[9]  Vincent Conitzer,et al.  Computing the optimal strategy to commit to , 2006, EC '06.

[10]  Bo An,et al.  Game-Theoretic Resource Allocation for Protecting Large Public Events , 2014, AAAI.

[11]  Milind Tambe,et al.  Effective solutions for real-world Stackelberg games: when agents must deal with human uncertainties , 2009, AAMAS 2009.

[12]  Nicola Basilico,et al.  Coordinating Multiple Defensive Resources in Patrolling Games with Alarm Systems , 2017, AAMAS.

[13]  Rong Yang,et al.  Adaptive resource allocation for wildlife protection against illegal poachers , 2014, AAMAS.

[14]  F. Hohn,et al.  Elementary Matrix Algebra , 1959 .

[15]  Haifeng Xu,et al.  Optimal Patrol Planning for Green Security Games with Black-Box Attackers , 2017, GameSec.

[16]  Tsuyoshi Murata,et al.  {m , 1934, ACML.

[17]  Sarit Kraus,et al.  Playing games for security: an efficient exact algorithm for solving Bayesian Stackelberg games , 2008, AAMAS.

[18]  Amos Azaria,et al.  Analyzing the Effectiveness of Adversary Modeling in Security Games , 2013, AAAI.

[19]  Sarit Kraus,et al.  Effective solutions for real-world Stackelberg games: when agents must deal with human uncertainties , 2009, AAMAS.

[20]  B. Stengel,et al.  Leadership with commitment to mixed strategies , 2004 .

[21]  G. G. Stokes "J." , 1890, The New Yale Book of Quotations.

[22]  Bo An,et al.  Security Games on a Plane , 2017, AAAI.

[23]  Bernhard von Stengel,et al.  Leadership games with convex strategy sets , 2010, Games Econ. Behav..

[24]  Bo An,et al.  Security Games with Protection Externalities , 2015, AAAI.

[25]  Milind Tambe,et al.  Security and Game Theory - Algorithms, Deployed Systems, Lessons Learned , 2011 .

[26]  Bo An,et al.  PROTECT: a deployed game theoretic system to protect the ports of the United States , 2012, AAMAS.

[27]  Bo An,et al.  Deploying PAWS: Field Optimization of the Protection Assistant for Wildlife Security , 2016, AAAI.

[28]  Sarit Kraus,et al.  Deployed ARMOR protection: the application of a game theoretic model for security at the Los Angeles International Airport , 2008, AAMAS.

[29]  Manish Jain,et al.  Computing optimal randomized resource allocations for massive security games , 2009, AAMAS.

[30]  Milind Tambe,et al.  Preventing Illegal Logging: Simultaneous Optimization of Resource Teams and Tactics for Security , 2016, AAAI.