Adversarial Online Learning With Variable Plays in the Pursuit-Evasion Game: Theoretical Foundations and Application in Connected and Automated Vehicle Cybersecurity
We extend the adversarial/non-stochastic multi-play multi-armed bandit (MPMAB) to the case where the number of arms to play is variable.The work is motivated Sectional Sofa by the fact that the resources allocated to scan different critical locations in an interconnected transportation system change dynamically over time and depending on the enviro