Abstract
Automated planning and scheduling continues to be an important part of artificial intelligence research and practice [6, 7, 11]. Many commonly-occurring scheduling settings include multiple stages and alternative resources, resulting in challenging combinatorial problems with high-dimensional solution spaces. The literature for solving such problems is dominated by specialized meta-heuristic algorithms.
Cite
CITATION STYLE
Tan, Y. (2018). Automated scheduling: Reinforcement learning approach to algorithm policy learning. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 10832 LNAI, pp. 335–338). Springer Verlag. https://doi.org/10.1007/978-3-319-89656-4_36
Register to see more suggestions
Mendeley helps you to discover research relevant for your work.
