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|Title:||Approximation approaches for solving security games with surveillance cost : a preliminary study||Authors:||Guo, Qingyu
|Keywords:||Engineering::Computer science and engineering||Issue Date:||2015||Source:||Guo, Q., An, B. & Kolobov, A. (2015). Approximation approaches for solving security games with surveillance cost : a preliminary study. 14th International Conference on Autonomous Agents and Multiagent Systems (AAMAS 2015).||Project:||MOE RG33/13||Abstract:||Security game models have been deployed to allocate limited security resources for critical infrastructure protection. Much work on this topic assumes that attackers have perfect knowledge of the defender’s randomized strategy. However, this assumption is not realistic, considering surveillance cost, since attackers may only have partial knowledge of the defender’s strategies, and may dynamically decide whether to attack or conduct more surveillance. Recently, a model called OPTS (OPtimal sTopping Security games) considered surveillance cost and formulated the attacker’s optimal decision making problem as a finite state space MDP (Markov Decision Process). However, the known exact algorithms for this MDP cannot scale up. In this paper, we extend several approximation approaches based on the MCTS (MonteCarlo Tree Search) method and RTDP (Real-Time Dynamic Programming) to MDPs of the OPTS model. We also propose an iterative deepening based approximation algorithm. Experimental results show that these approaches can solve much larger security games with surveillance cost.||URI:||https://hdl.handle.net/10356/155034||URL:||https://dl.acm.org/doi/proceedings/10.5555/2772879||ISBN:||978-1-4503-3413-6||Rights:||© 2015 International Foundation for Autonomous Agents and Multiagent Systems (www.ifaamas.org). All rights reserved. This paper was published in Proceedings of the 14th International Conference on Autonomous Agents and Multiagent Systems (AAMAS 2015) and is made available with permission of International Foundation for Autonomous Agents and Multiagent Systems.||Fulltext Permission:||open||Fulltext Availability:||With Fulltext|
|Appears in Collections:||SCSE Conference Papers|
Updated on May 20, 2022
Updated on May 20, 2022
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