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Canadian Conference on Artificial Intelligence

Canadian AI 2018: Advances in Artificial Intelligence pp 335–338Cite as

Automated Scheduling: Reinforcement Learning Approach to Algorithm Policy Learning

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Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 10832))

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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References

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Author information

Authors and Affiliations

  1. Mechanical, Industrial and Aerospace Engineering, Concordia University, Montréal, Canada

    Yingcong Tan

Authors
  1. Yingcong Tan
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Correspondence to Yingcong Tan .

Editor information

Editors and Affiliations

  1. Ryerson University, Toronto, Ontario, Canada

    Ebrahim Bagheri

  2. McGill University, Montréal, Québec, Canada

    Jackie C.K. Cheung

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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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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-89655-7

  • Online ISBN: 978-3-319-89656-4

  • eBook Packages: Computer ScienceComputer Science (R0)

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