A Game-Theoretical Model Applied to an Active Patrolling Camera

In patrolling, an agent perceives portions of an environment to detect the presence of an intruder. Usually, the agent cannot perceive the whole environment at once, but can change over time the observed portion. Finding an optimal patrolling strategy that minimizes the possibility of intrusions constitutes one of the main scientific problems in this field. Game theoretical models have been recently employed to compute effective patrolling strategies that explicitly consider the presence of a rational intruder. In this paper, we present a general game theoretical framework for computing patrolling strategies for different kinds of patrollers. In particular, we study the case of an active patrolling camera.

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

[2]  Sampath Kannan,et al.  Randomized pursuit-evasion in a polygonal environment , 2005, IEEE Transactions on Robotics.

[3]  Brahim Chaib-draa,et al.  A Markov Model for Multiagent Patrolling in Continuous Time , 2009, ICONIP.

[4]  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.

[5]  J. Filar,et al.  Competitive Markov Decision Processes , 1996 .

[6]  Sarit Kraus,et al.  An efficient heuristic approach for security against multiple adversaries , 2007, AAMAS '07.

[7]  Eric Sommerlade,et al.  Information-theoretic active scene exploration , 2008, 2008 IEEE Conference on Computer Vision and Pattern Recognition.

[8]  Yann Chevaleyre,et al.  A theoretical analysis of multi-agent patrolling strategies , 2004, Proceedings of the Third International Joint Conference on Autonomous Agents and Multiagent Systems, 2004. AAMAS 2004..

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

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

[11]  S. Shankar Sastry,et al.  Probabilistic pursuit-evasion games: theory, implementation, and experimental evaluation , 2002, IEEE Trans. Robotics Autom..

[12]  Nicola Basilico,et al.  Finding the optimal strategies for robotic patrolling with adversaries in topologically-represented environments , 2009, 2009 IEEE International Conference on Robotics and Automation.