Balancing Unpredictability and Coverage in Adversarial Patrolling Settings

  • Nicola Basilico
  • Stefano CarpinEmail author
Conference paper
Part of the Springer Proceedings in Advanced Robotics book series (SPAR, volume 14)


We present a novel strategy for a patroller defending a set of heterogeneous assets from the attacks carried by an attacker that through repeated observations attempts to learn the strategy followed by the patroller. Implemented through a Markov chain whose stationary distribution is a function of the values of the assets being defended and the topology of the environment, the strategy is biased towards providing more protection to valuable assets, yet is provably hard to learn for an opponent. After having studied its properties, we show that our proposed method outperforms strategies commonly used for this type of problems.


Security games Learning Patrolling 


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© Springer Nature Switzerland AG 2020

Authors and Affiliations

  1. 1.Department of Computer ScienceUniversity of MilanMilanItaly
  2. 2.Department of Computer Science and EngineeringUniversity of CaliforniaMercedUSA

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