Andrew Perrault, Bryan Wilder, Eric Ewing, Aditya Mate, Bistra Dilkina, and Milind Tambe. 2019. “Decision-Focused Learning of Adversary Behavior in Security Games.” In GAIW: Games, Agents and Incentives Workshop at International Conference on Autonomous Agents and Multiagent Systems (AAMAS-19).
Abstract:Stackelberg security games are a critical tool for maximizing the
utility of limited defense resources to protect important targets
from an intelligent adversary. Motivated by green security, where
the defender may only observe an adversary’s response to defense
on a limited set of targets, we study the problem of defending
against the same adversary on a larger set of targets from the same
distribution. We give a theoretical justification for why standard
two-stage learning approaches, where a model of the adversary is
trained for predictive accuracy and then optimized against, may
fail to maximize the defender’s expected utility in this setting. We
develop a decision-focused learning approach, where the adversary behavior model is optimized for decision quality, and show
empirically that it achieves higher defender expected utility than
the two-stage approach when there is limited training data and a
large number of target features.