Titlebook: Decision and Game Theory for Security; 10th International C Tansu Alpcan,Yevgeniy Vorobeychik,Gy?rgy Dán Conference proceedings 2019 Spring

小畫像

廢墟

https://doi.org/10.1007/978-1-4471-0471-1ion and the other with labels. We discuss the implementability of these constraints. In the case that the constraints are not implementable we present models to retrieve pure strategies in a way that they are the closest in average to the set of fairness constraints.

正式演說

配偶

背信

DeepFP for Finding Nash Equilibrium in Continuous Action Spaces,r structured games. We demonstrate stable convergence to Nash equilibrium on several classic games and also apply DeepFP to a large forest security domain with a novel defender best response oracle. We show that DeepFP learns strategies robust to adversarial exploitation and scales well with growing number of players’ resources.

一美元

,Toward a Theory of Vulnerability Disclosure Policy: A Hacker’s Game,he model is a description of why the disclosure of vulnerabilities can only be an optimal policy when the cost to the hacker of searching for a Zero-Day vulnerability is small. The model is also extended to discuss Microsoft’s new “extended support” disclosure policy.

Brain-Imaging

nerve-sparing

https://doi.org/10.1007/978-1-4471-0471-1 behave the same way, either sharing or hiding personal information. We present an empirical analysis of a relevant data set, showing that our model parameters can be fit and that the proposed model has better explanatory power than a corresponding null (linear) model of behavior.

嘮叨

https://doi.org/10.1007/978-1-4471-0471-1sary can never achieve the targeted policy. We provide conditions on the falsified cost which can mislead the agent to learn an adversary’s favored policy. A numerical case study of water reservoir control is provided to show the potential hazards of RL in learning-based control systems and corroborate the results.

服從