Adversarial Resilience Learning - Towards Systemic Vulnerability Analysis for Large and Complex Systems

15 Nov 2018Lars FischerJan-Menno MemmenEric MSP VeithMartin Tröschel

This paper introduces Adversarial Resilience Learning (ARL), a concept to model, train, and analyze artificial neural networks as representations of competitive agents in highly complex systems. In our examples, the agents normally take the roles of attackers or defenders that aim at worsening or improving-or keeping, respectively-defined performance indicators of the system... (read more)

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