@conference {1499814,
	title = {Don{\textquoteright}t Put All Your Strategies in One Basket: Playing Green Security Games with Imperfect Prior Knowledge },
	booktitle = {International Conference on Autonomous Agents and Multiagent Systems (AAMAS-19)},
	year = {2019},
	abstract = {Security efforts for wildlife monitoring and protection of endangered species (e.g., elephants, rhinos, etc.) are constrained by limited
resources available to law enforcement agencies. Recent progress in
Green Security Games (GSGs) has led to patrol planning algorithms
for strategic allocation of limited patrollers to deter adversaries
in environmental settings. Unfortunately, previous approaches to
these problems suffer from several limitations. Most notably, (i) previous work in GSG literature relies on exploitation of error-prone
machine learning (ML) models of poachers{\textquoteright} behavior trained on
(spatially) biased historical data; and (ii) online learning approaches
for repeated security games (similar to GSGs) do not account for
spatio-temporal scheduling constraints while planning patrols, potentially causing significant shortcomings in the effectiveness of
the planned patrols. Thus, this paper makes the following novel
contributions: (I) We propose MINION-sm, a novel online learning
algorithm for GSGs which does not rely on any prior error-prone
model of attacker behavior, instead, it builds an implicit model of
the attacker on-the-fly while simultaneously generating schedulingconstraint-aware patrols. MINION-sm achieves a sublinear regret
against an optimal hindsight patrol strategy. (II) We also propose
MINION, a hybrid approach where our MINION-sm model and an
ML model (based on historical data) are considered as two patrol
planning experts and we obtain a balance between them based on
their observed empirical performance. (III) We show that our online
learning algorithms significantly outperform existing state-of-theart solvers for GSGs.},
	author = {Gholami, Shahrzad and Amulya Yadav and Long Tran-Thanh and Dilkina, Bistra and Tambe, Milind}
}
