Improving Resource Allocation Strategy against Human Adversaries in Security Games

Recent real-world deployments of Stackelberg security games make it critical that we address human adversaries' bounded rationality in computing optimal strategies. To that end, this paper provides three key contributions: (i) new efficient algorithms for computing optimal strategic solutions using Prospect Theory and Quantal Response Equilibrium; (ii) the most comprehensive experiment to date studying the effectiveness of different models against human subjects for security games; and (iii) new techniques for generating representative payoff structures for behavioral experiments in generic classes of games. Our results with human subjects show that our new techniques outperform the leading contender for modeling human behavior in security games.

[1]  Vincent Conitzer,et al.  Security Games with Multiple Attacker Resources , 2011, IJCAI.

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

[3]  Sarit Kraus,et al.  Multi-robot perimeter patrol in adversarial settings , 2008, 2008 IEEE International Conference on Robotics and Automation.

[4]  Peter P. Wakker,et al.  On the Intuition of Rank-Dependent Utility , 2000 .

[5]  R. Wilcox Applying Contemporary Statistical Techniques , 2003 .

[6]  D. McFadden Econometric analysis of qualitative response models , 1984 .

[7]  Ya'akov Gal,et al.  Modeling Reciprocal Behavior in Human Bilateral Negotiation , 2007, AAAI.

[8]  A. Tversky,et al.  Advances in prospect theory: Cumulative representation of uncertainty , 1992 .

[9]  Bo An,et al.  GUARDS and PROTECT: next generation applications of security games , 2011, SECO.

[10]  Nicola Basilico,et al.  Leader-follower strategies for robotic patrolling in environments with arbitrary topologies , 2009, AAMAS.

[11]  James R. Callan,et al.  Nonlinear least squares methods: A direct grid search approach , 1968 .

[12]  C. Starmer Developments in Non-expected Utility Theory: The Hunt for a Descriptive Theory of Choice under Risk , 2000 .

[13]  Milind Tambe,et al.  GUARDS: game theoretic security allocation on a national scale , 2011, AAMAS.

[14]  Avi Pfeffer,et al.  Simultaneously modeling humans' preferences and their beliefs about others' preferences , 2008, AAMAS.

[15]  D. McFadden Quantal Choice Analysis: A Survey , 1976 .

[16]  Sarit Kraus,et al.  Adversarial Uncertainty in Multi-Robot Patrol , 2009, IJCAI.

[17]  R. McKelvey,et al.  Quantal Response Equilibria for Normal Form Games , 1995 .

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

[19]  Manish Jain,et al.  Risk-Averse Strategies for Security Games with Execution and Observational Uncertainty , 2011, AAAI.

[20]  Kevin Leyton-Brown,et al.  Beyond equilibrium: predicting human behaviour in normal form games , 2010, AAAI.

[21]  Mrinal K. Sen,et al.  Global Optimization Methods in Geophysical Inversion , 1995 .

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

[23]  Miguel A. Costa-Gomes,et al.  Cognition and Behavior in Normal-Form Games: An Experimental Study , 1998 .

[24]  Nicola Gatti Game Theoretical Insights in Strategic Patrolling: Model and Algorithm in Normal-Form , 2008, ECAI.

[25]  Jonathan Gratch,et al.  The effect of expression of anger and happiness in computer agents on negotiations with humans , 2011, AAMAS.

[26]  Kelly E. See,et al.  Between ignorance and truth: Partition dependence and learning in judgment under uncertainty. , 2006, Journal of experimental psychology. Learning, memory, and cognition.

[27]  Dimitris Bertsimas,et al.  Robust game theory , 2006, Math. Program..

[28]  Colin Camerer,et al.  A Cognitive Hierarchy Model of Games , 2004 .

[29]  H. Simon Rational choice and the structure of the environment. , 1956, Psychological review.

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

[31]  Vincent Conitzer,et al.  Complexity of Computing Optimal Stackelberg Strategies in Security Resource Allocation Games , 2010, AAAI.

[32]  E. C. O. N. Ometrica Prospect theory: an analysis of decision under risk — Source link , 2007 .

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

[34]  Milind Tambe,et al.  Urban security: game-theoretic resource allocation in networked physical domains , 2010, AAAI 2010.

[35]  Sarit Kraus,et al.  Robust solutions to Stackelberg games: Addressing bounded rationality and limited observations in human cognition , 2010, Artif. Intell..

[36]  D. Stahl,et al.  Experimental evidence on players' models of other players , 1994 .

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

[38]  K. Train Discrete Choice Methods with Simulation , 2003 .

[39]  Yevgeniy Vorobeychik,et al.  Computing Randomized Security Strategies in Networked Domains , 2011, Applied Adversarial Reasoning and Risk Modeling.