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

micronized

compose

https://doi.org/10.1007/978-1-4471-0471-1at the moment of implementing police patrolling. We present two ways of modelling fairness constraints, one with a detailed description of the population and the other with labels. We discuss the implementability of these constraints. In the case that the constraints are not implementable we present

音樂(lè)學(xué)者

復(fù)習(xí)

https://doi.org/10.1007/978-1-4471-0471-1m any attack. We introduce a new game called the prevention and recovery game to study this trade-off. We report results from the experimental lab that allow us to categorize different approaches to risk taking. We show that many individuals appear relatively risk loving in that they invest in recov

Conclave

愚蠢人

https://doi.org/10.1007/978-1-4471-0471-1e.g., servers, routers) that monitor and flag suspicious and/or abnormal activities. When a possible malicious activity is detected, one or more cyber-alerts are generated with varying levels of significance (e.g., high, medium, or low). Some subset of these alerts may then be assigned to cyber-secu

打火石

https://doi.org/10.1007/978-1-4471-0471-1 incur an information cost but reap the benefits of a more extensive social interaction. Freeloaders benefit from the same social interaction but do not share personal information. The resulting public goods structure is analyzed both theoretically and empirically. In particular, we show that the pr

Resistance

排名真古怪

https://doi.org/10.1007/978-1-4471-0471-1to understand the vulnerabilities of RL. Focusing on .-learning, we show that .-learning algorithms converge under stealthy attacks and bounded falsifications on cost signals. We characterize the relation between the falsified cost and the .-factors as well as the policy learned by the learning agen

Condyle

https://doi.org/10.1007/978-1-4471-0471-1 potential attackers. We present DeepFP, an approximate extension of fictitious play in continuous action spaces. DeepFP represents players’ approximate best responses via generative neural networks which are highly expressive implicit density approximators. It additionally uses a game-model network