Dynamic Algorithm Configuration

A versatile DAC can handle any situation at any time. (Image credit: Ina Lindauer)

Hyperparameter optimization is a powerful approach to achieve the best performance on many different problems. However classical approaches to solve this problem all ignore the iterative nature of many algorithms. Dynamic algorithm configuration (DAC) is capable of generalizing over prior optimization approaches, as well as handling optimization of hyperparameters that need to be adjusted over multiple time-steps. To allow us to use this framework, we need to move from the classical view of algorithms as a black-box to more of a gray or even white-box view to unleash the full potential of AI algorithms with DAC.