Machine Learning Techniques for Stackelberg Security Games: a Survey
The present survey aims at presenting the current machine learning techniques employed in security games domains. Specifically, we focused on papers and works developed by the Teamcore of University of Southern California, which deepened different directions in this field. After a brief introduction on Stackelberg Security Games (SSGs) and the poaching setting, the rest of the work presents how to model a boundedly rational attacker taking into account her human behavior, then describes how to face the problem of having attacker's payoffs not defined and how to estimate them and, finally, presents how online learning techniques have been exploited to learn a model of the attacker.
Code (0)
등록된 구현이 없습니다.
Tasks
BIG-bench Machine LearningSurveySimilar Papers 제목 키워드 기반
A Survey of Decision Making in Adversarial Games
Game theory has by now found numerous applications in various fields, including economics, industry, jurisprudence, and artificial intelligence, where each player only cares about its own interest in a noncooperative or …
Decision MakingJurisprudenceSurveySafe Search for Stackelberg Equilibria in Extensive-Form Games
Stackelberg equilibrium is a solution concept in two-player games where the leader has commitment rights over the follower. In recent years, it has become a cornerstone of many security applications, including airport pa…
FormTargets in Reinforcement Learning to solve Stackelberg Security Games
Reinforcement Learning (RL) algorithms have been successfully applied to real world situations like illegal smuggling, poaching, deforestation, climate change, airport security, etc. These scenarios can be framed as Stac…
reinforcement-learningReinforcement LearningReinforcement Learning (RL)Multi-agent Reinforcement Learning in Bayesian Stackelberg Markov Games for Adaptive Moving Target Defense
The field of cybersecurity has mostly been a cat-and-mouse game with the discovery of new attacks leading the way. To take away an attacker's advantage of reconnaissance, researchers have proposed proactive defense metho…
Multi-agent Reinforcement LearningQ-LearningReinforcement Learning (RL)Riemannian Manifold Learning for Stackelberg Games with Neural Flow Representations
We present a novel framework for online learning in Stackelberg general-sum games, where two agents, the leader and follower, engage in sequential turn-based interactions. At the core of this approach is a learned diffeo…