Planning and learning in security games
Abstract
We present two new critical domains where security games are applied to generate randomized patrol schedules. For each setting, we present the current research that we have produced. We then propose two new challenges to build accurate schedules that can be deployed effectively in the real world. The first is a planning challenge. Current schedules cannot handle interruptions. Thus, more expressive models, that allow for reasoning over stochastic actions, are needed. The second is a learning challenge. In several security domains, data can be used to extract information about both the environment and the attacker. This information can then be used to improve the defender's strategies.
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Index Terms
- Planning and learning in security games
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Copyright © 2013 Authors.
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Association for Computing Machinery
New York, NY, United States
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Published: 01 June 2013
Published in SIGECOM Volume 12, Issue 1
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