ASlib: A Benchmark Library for Algorithm Selection
This addresses a community-wide bottleneck for researchers and practitioners in AI by providing a standardized platform, though it is incremental as it builds on existing data and methods.
The authors tackled the lack of standardized data for algorithm selection research by introducing ASlib, a benchmark library with a standardized format and repository of datasets, enabling effective sharing and comparison of approaches and demonstrating significant performance improvements across various problems.
The task of algorithm selection involves choosing an algorithm from a set of algorithms on a per-instance basis in order to exploit the varying performance of algorithms over a set of instances. The algorithm selection problem is attracting increasing attention from researchers and practitioners in AI. Years of fruitful applications in a number of domains have resulted in a large amount of data, but the community lacks a standard format or repository for this data. This situation makes it difficult to share and compare different approaches effectively, as is done in other, more established fields. It also unnecessarily hinders new researchers who want to work in this area. To address this problem, we introduce a standardized format for representing algorithm selection scenarios and a repository that contains a growing number of data sets from the literature. Our format has been designed to be able to express a wide variety of different scenarios. Demonstrating the breadth and power of our platform, we describe a set of example experiments that build and evaluate algorithm selection models through a common interface. The results display the potential of algorithm selection to achieve significant performance improvements across a broad range of problems and algorithms.