On Learning from Human Expert Knowledge for Automated Scheduling

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.Algorithmic