Adversarial Modeling and Reasoning in the Maritime Domain - Year 1 Report

A number of real-world security scenarios can be cast as a problem of transiting an area patrolled by a mobile adversary, where the transiting agent aims to choose its route so as to minimize the probability of encountering the patrolling agent, and vice versa. We model this problem as a twoplayer zero-sum game on a graph, termed the transit game. In contrast to the existing models of area transit, where one of the players is stationary, we assume both players are mobile. We also explicitly model the limited endurance of the patroller and the notion of a base to which the patroller has to repeatedly return. Noting the prohibitive size of the strategy spaces of both players, we develop singleand double-oracle based algorithms including a novel acceleration scheme, to obtain optimum route selection strategies for both players. We evaluate the developed approach on a range of transit game instances inspired by real-world security problems in the urban and naval security domains.

[1]  Nicola Basilico,et al.  Extending Algorithms for Mobile Robot Patrolling in the Presence of Adversaries to More Realistic Settings , 2009, 2009 IEEE/WIC/ACM International Joint Conference on Web Intelligence and Intelligent Agent Technology.

[2]  Jean B. Lasserre,et al.  Global Optimization with Polynomials and the Problem of Moments , 2000, SIAM J. Optim..

[3]  Mohammad Emtiyaz Khan,et al.  Game Theory Models for Pursuit Evasion Games , 2006 .

[4]  Tim J. Ellis,et al.  Learning semantic scene models from observing activity in visual surveillance , 2005, IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics).

[5]  TanTieniu,et al.  A System for Learning Statistical Motion Patterns , 2006 .

[6]  Aaas News,et al.  Book Reviews , 1893, Buffalo Medical and Surgical Journal.

[7]  M. Dufwenberg Game theory. , 2011, Wiley interdisciplinary reviews. Cognitive science.

[8]  Mark R. Morelande,et al.  Statistical analysis of motion patterns in AIS Data: Anomaly detection and motion prediction , 2008, 2008 11th International Conference on Information Fusion.

[9]  Colin L. Mitchell Countering Maritime Terrorism in the Caribbean Sea and the Atlantic Ocean: Implications of Possible Maritime Terrorism in the Caribbean , 2012 .

[10]  Claudia Copeland Ocean Dumping Act: A Summary of the Law , 2007 .

[11]  Farmey A. Joseph,et al.  Path-Planning Strategies for Ambush Avoidance , 2005 .

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

[13]  B R Aune Maritime drug trafficking: an underrated problem. , 1990, Bulletin on narcotics.

[14]  Bradley J. Rhodes,et al.  Probabilistic associative learning of vessel motion patterns at multiple spatial scales for maritime situation awareness , 2007, 2007 10th International Conference on Information Fusion.

[15]  Sarit Kraus,et al.  A graph-theoretic approach to protect static and moving targets from adversaries , 2010, AAMAS.

[16]  Allen M. Waxman,et al.  Associative Learning of Vessel Motion Patterns for Maritime Situation Awareness , 2006, 2006 9th International Conference on Information Fusion.

[17]  Yoav Shoham,et al.  Multiagent Systems - Algorithmic, Game-Theoretic, and Logical Foundations , 2009 .

[18]  Satya N. Nandan,et al.  Oceans and the Law of the Sea , 2002 .

[19]  Vincent Conitzer,et al.  Stackelberg vs. Nash in security games: interchangeability, equivalence, and uniqueness , 2010, AAMAS.

[20]  Micah Adler,et al.  Randomized Pursuit-Evasion in Graphs , 2002, Combinatorics, Probability and Computing.

[21]  Milind Tambe,et al.  Optimal defender allocation for massive security games : A branch and price approach , 2010 .

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

[23]  John C. Harsanyi,et al.  Games with Incomplete Information Played by "Bayesian" Players, I-III: Part I. The Basic Model& , 2004, Manag. Sci..

[24]  S. Alpern Infiltration games on arbitrary graphs , 1992 .

[25]  L. Baum,et al.  A Maximization Technique Occurring in the Statistical Analysis of Probabilistic Functions of Markov Chains , 1970 .

[26]  Manish Jain,et al.  Software Assistants for Randomized Patrol Planning for the LAX Airport Police and the Federal Air Marshal Service , 2010, Interfaces.

[27]  Hein de Haas,et al.  Trans-Saharan Migration to North Africa and the EU: Historical Roots and Current Trends , 2006 .

[28]  Tieniu Tan,et al.  A system for learning statistical motion patterns , 2006, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[29]  Sarit Kraus,et al.  The impact of adversarial knowledge on adversarial planning in perimeter patrol , 2008, AAMAS.

[30]  Nicola Basilico,et al.  Asynchronous Multi-Robot Patrolling against Intrusions in Arbitrary Topologies , 2010, AAAI.

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

[32]  D. Rubin,et al.  Maximum likelihood from incomplete data via the EM - algorithm plus discussions on the paper , 1977 .

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

[34]  Alan Washburn,et al.  Two-Person Zero-Sum Games for Network Interdiction , 1995, Oper. Res..

[35]  Branislav Bosanský,et al.  Transiting areas patrolled by a mobile adversary , 2010, Proceedings of the 2010 IEEE Conference on Computational Intelligence and Games.

[36]  Jianbo Shi,et al.  Detecting unusual activity in video , 2004, Proceedings of the 2004 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2004. CVPR 2004..

[37]  Avrim Blum,et al.  Planning in the Presence of Cost Functions Controlled by an Adversary , 2003, ICML.

[38]  Kristin P. Bennett,et al.  Bilinear separation of two sets inn-space , 1993, Comput. Optim. Appl..

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

[40]  M. M. Flood THE HIDE AND SEEK GAME OF VON NEUMANN , 1972 .

[41]  Martin W. P. Savelsbergh,et al.  Branch-and-Price: Column Generation for Solving Huge Integer Programs , 1998, Oper. Res..

[42]  Jason M. Schwier,et al.  Markovian Search Games in Heterogeneous Spaces , 2009, IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics).

[43]  P. W. Jones,et al.  Bandit Problems, Sequential Allocation of Experiments , 1987 .

[44]  P. Hudson Search Games , 1982 .

[45]  Takeo Kanade,et al.  A System for Video Surveillance and Monitoring , 2000 .

[46]  William H. Ruckle,et al.  Ambushing Random Walks I: Finite Models , 1976, Oper. Res..

[47]  Vincent Conitzer,et al.  Multi-Step Multi-Sensor Hider-Seeker Games , 2009, IJCAI.

[48]  M. Dorn Fishing behavior of factory trawlers: a hierarchical model of information processing and decision-making , 2001 .

[49]  Sarit Kraus,et al.  Using Game Theory for Los Angeles Airport Security , 2009, AI Mag..

[50]  Lawrence R. Rabiner,et al.  A tutorial on hidden Markov models and selected applications in speech recognition , 1989, Proc. IEEE.

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

[52]  P. Goutal,et al.  On the infiltration game , 1997, Int. J. Game Theory.

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