Online Algorithm Selection
Online Algorithm Selection
Hans Degroote
Proceedings of the Twenty-Sixth International Joint Conference on Artificial Intelligence
Doctoral Consortium. Pages 5173-5174.
https://doi.org/10.24963/ijcai.2017/746
Algorithm selection approaches have achieved impressive performance improvements in many areas of AI. Most of the literature considers the offline algorithm selection problem, where the initial selection model is never updated after training. However, new data from running algorithms on instances becomes available while an algorithm selection method is in use. In this extended abstract, the online algorithm selection problem is considered. In online algorithm selection, additional data can be processed, and the selection model can change over time. This abstract details the online algorithm setting, shows that it is a contextual multi-armed bandit, proposes a solution methodology, and empirically validates it.
Keywords:
Artificial Intelligence: artificial intelligence
Artificial Intelligence: machine learning
Artificial Intelligence: other