On Learning from Human Expert Knowledge for Automated Scheduling

Yorke-Smith, N.

On Learning from Human Expert Knowledge for Automated Scheduling

Conference paper (2018)

Authors

N. Yorke-Smith Algorithmics

Research Group

Algorithmics

To reference this document use:

http://resolver.tudelft.nl/uuid:9e3cb4e1-89d8-40cb-8e07-f4faf85c434c

More Info

expand_more

Published Date

2018-6

Language

English

Reuse Rights

Other than for strictly personal use, it is not permitted to download, forward or distribute the text or part of it, without the consent of the author(s) and/or copyright holder(s), unless the work is under an open content license such as Creative Commons.

Research Group

Algorithmics

Abstract

Automated scheduling systems and decision support tools require at least four kinds of knowledge: 1) domain knowledge, 2) problem instance knowledge, 3) control knowledge, and 4) solving knowledge. This short paper draws attention to learning from human experts for these different kinds of knowledge, and advocates a complementarity of knowledge acquisition by automated techniques and by human knowledge engineers.

Files

KEPS_proceedings1.pdf

(pdf | 0.129 Mb)

Unspecified