Opening the Black Box: Automatically Characterizing Software for Algorithm Selection (Student Abstract)

Authors

  • Damir Pulatov University of Wyoming
  • Lars Kotthoff University of Wyoming

DOI:

https://doi.org/10.1609/aaai.v34i10.7222

Abstract

Meta-algorithmics, the field of leveraging machine learning to use algorithms more efficiently, has achieved impressive performance improvements in many areas of AI. It treats the algorithms to improve on as black boxes – nothing is known about their inner workings. This allows meta-algorithmic techniques to be deployed in many applications, but leaves potential performance improvements untapped by ignoring information that the algorithms could provide. In this paper, we open the black box without sacrificing the universal applicability of meta-algorithmic techniques by automatically analyzing the source code of the algorithms under consideration and show how to use it to improve algorithm selection performance. We demonstrate improvements of up to 82% on the standard ASlib benchmark library.

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Published

2020-04-03

How to Cite

Pulatov, D., & Kotthoff, L. (2020). Opening the Black Box: Automatically Characterizing Software for Algorithm Selection (Student Abstract). Proceedings of the AAAI Conference on Artificial Intelligence, 34(10), 13899-13900. https://doi.org/10.1609/aaai.v34i10.7222

Issue

Section

Student Abstract Track