Abstract
Recently, several models for autonomous robotic patrolling have been proposed and analysed on a game-theoretic basis. The common drawback of such models are the assumptions required to apply game theory analysis. Such assumptions do not usually hold in practice, especially perfect knowledge of the adversary’s strategy, and the belief that we are facing always a best-responser. However, the agents in the patrolling scenario may take advantage of that fact. In this work, we try to analyse from an empirical perspective a patrolling model with an explicit topology, and take advantage of the adversarial uncertainty caused by the limited, imperfect knowledge an agent can acquire through simple observation. The first results we report are encouraging.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Agmon, N., Kraus, S., Kaminka, G.: Multi-robot perimeter patrol in adversarial settings. In: Proc. of the IEEE Conf. on Robotics and Automation, pp. 2339–2345 (2008)
Amigoni, F., Basilico, N., Gatti, N.: Finding the optimal strategies for robotic patrolling with adversaries in topologically-represented environments. In: Proc. of the IEEE Conf. on Robotics and Automation, pp. 819–824 (2009)
Amigoni, F., Basilico, N., Gatti, N., Saporiti, A., Troiani, S.: Moving Game Theoretical Patrolling Strategies from Theory to Practice: an USARSim Simulation. In: Proc. of the IEEE Conf. on Robotics and Automation, pp. 426–431 (2010)
Basilico, N., Gatti, N., Rossi, T.: Capturing augmented sensing capabilities and intrusion delay in patrolling-intrusion games. In: Proc. of the 5th Int. Conf. on Computational Intelligence and Games, pp. 186–193 (2009)
Osborne, M., Rubinstein, A.: A Course in Game Theory. MIT Press, Cambridge (1994)
Paruchuri, P., Pearce, J., Tambe, M., Ordonez, F., Kraus, S.: An efficient heuristic approach for security against multiple adversaries. In: Proc. of the 6th Int. Conf. on Autonomous Agents and Multiagent Systems, pp. 311–318 (2007)
Pelta, D., Yager, R.: On the conflict between inducing confusion and attaining payoff in adversarial decision making. Information Sciences 179, 33–40 (2009)
Villacorta, P.J., Pelta, D.A.: Theoretical analysis of expected payoff in an adversarial domain. Information Sciences 186(1), 93–104 (2012)
Villacorta, P., Pelta, D.: Expected payoff analysis of dynamic mixed strategies in an adversarial domain. In: Proc. of the IEEE Symposium on Intelligent Agents (IA 2011). IEEE Symposium Series on Computational Intelligence, pp. 116–122 (2011)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2012 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Villacorta, P.J., Pelta, D.A. (2012). Exploiting Adversarial Uncertainty in Robotic Patrolling: A Simulation-Based Analysis. In: Greco, S., Bouchon-Meunier, B., Coletti, G., Fedrizzi, M., Matarazzo, B., Yager, R.R. (eds) Advances in Computational Intelligence. IPMU 2012. Communications in Computer and Information Science, vol 300. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-31724-8_55
Download citation
DOI: https://doi.org/10.1007/978-3-642-31724-8_55
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-31723-1
Online ISBN: 978-3-642-31724-8
eBook Packages: Computer ScienceComputer Science (R0)