Manipulating a Learning Defender and Ways to Counteract

NeurIPS 2019 Jiarui GanQingyu GuoLong Tran-ThanhBo AnMichael Wooldridge

In Stackelberg security games when information about the attacker's payoffs is uncertain, algorithms have been proposed to learn the optimal defender commitment by interacting with the attacker and observing their best responses. In this paper, we show that, however, these algorithms can be easily manipulated if the attacker responds untruthfully... (read more)

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