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.
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Tan, Y. (2018). Automated Scheduling: Reinforcement Learning Approach to Algorithm Policy Learning. In: Bagheri, E., Cheung, J. (eds) Advances in Artificial Intelligence. Canadian AI 2018. Lecture Notes in Computer Science(), vol 10832. Springer, Cham. https://doi.org/10.1007/978-3-319-89656-4_36
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DOI: https://doi.org/10.1007/978-3-319-89656-4_36
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