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A Scheduler for Smart Homes with Probabilistic User Preferences

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PRIMA 2019: Principles and Practice of Multi-Agent Systems (PRIMA 2019)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 11873))

Abstract

Scheduling appliances is a challenging and interesting problem aimed at reducing energy consumption at a residential level. Previous work on appliance scheduling for smart homes assumes that user preferences have no uncertainty. In this paper, we study two approaches to address this problem when user preferences are uncertain. More specifically, we assume that user preferences in turning on or off a device are represented by Normal distributions. The first approach uses sample average approximation, a mathematical model, in computing a schedule. The second one relies on the fact that a scheduling problem could be viewed as a constraint satisfaction problem and uses depth-first search to identify a solution. We also conduct an experimental evaluation of the two approaches to investigate the scalability of each approach in different problem variants. We conclude by discussing computational challenges of our approaches and some possible directions for future work.

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Notes

  1. 1.

    Our proposed transformation from a p-scheduling problem \(\mathcal {P}= (P, \mathcal {C})\) to a MILP does satisfy Conditions (3), (4), and (5), but ignores the dependency matrix D in P. We leave a proposal for a complete transformation for future work.

  2. 2.

    One could also use other search algorithms as well because our core contribution here is to formulate the problem into a search problem and propose a number of pruning conditions that can be used with any search strategy.

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Acknowledgment

This research is partially supported by NSF grants 1242122, 1345232, 1619273, 1623190, 1757207, 1812618, and 1812619. The views and conclusions contained in this document are those of the authors and should not be interpreted as representing the official policies, either expressed or implied, of the sponsoring organizations, agencies, or the U.S. government. We would also like to thank Long Tran-Thanh for initial discussions that influenced the direction of this research.

Author information

Authors and Affiliations

  1. New Mexico State University, Las Cruces, NM, USA

    Van Nguyen, Tran Cao Son & Tiep Le

  2. Washington University in St. Louis, St. Louis, MO, USA

    William Yeoh

  3. University of Texas at El Paso, El Paso, TX, USA

    Vladik Kreinovich

  4. Viome, Inc., Santa Clara, CA, USA

    Tiep Le

Authors
  1. Van Nguyen
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  2. William Yeoh
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  3. Tran Cao Son
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  4. Vladik Kreinovich
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  5. Tiep Le
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Corresponding author

Correspondence to Vladik Kreinovich .

Editor information

Editors and Affiliations

  1. Università degli Studi di Torino, Turin, Italy

    Matteo Baldoni

  2. Utrecht University, Utrecht, The Netherlands

    Mehdi Dastani

  3. Zhejiang University, Hangzhou, China

    Beishui Liao

  4. National Institute of Advanced Industrial Science and Technology, Tokyo, Japan

    Yuko Sakurai

  5. University of Texas at Dallas, Richardson, TX, USA

    Rym Zalila Wenkstern

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Nguyen, V., Yeoh, W., Son, T.C., Kreinovich, V., Le, T. (2019). A Scheduler for Smart Homes with Probabilistic User Preferences. In: Baldoni, M., Dastani, M., Liao, B., Sakurai, Y., Zalila Wenkstern, R. (eds) PRIMA 2019: Principles and Practice of Multi-Agent Systems. PRIMA 2019. Lecture Notes in Computer Science(), vol 11873. Springer, Cham. https://doi.org/10.1007/978-3-030-33792-6_9

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  • DOI: https://doi.org/10.1007/978-3-030-33792-6_9

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-33791-9

  • Online ISBN: 978-3-030-33792-6

  • eBook Packages: Computer ScienceComputer Science (R0)

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