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Improving Temporal Event Scheduling through STEP Perpetual Learning

Author

Listed:
  • Jiahua Tang

    (Faculty of Innovation Engineering, Macau University of Science and Technology, Macau SAR 999078, China)

  • Du Zhang

    (Faculty of Innovation Engineering, Macau University of Science and Technology, Macau SAR 999078, China)

  • Xibin Sun

    (Computer Engineering Technical College, Guangdong Polytechnic of Science and Technology, Guangzhou 510640, China)

  • Haiou Qin

    (School of Information Engineering, Nanchang Institute of Technology, Nanchang 330029, China)

Abstract

Currently, most machine learning applications follow a one-off learning process: given a static dataset and a learning algorithm, generate a model for a task. These applications can neither adapt to a dynamic and changing environment, nor accomplish incremental task performance improvement continuously. STEP perpetual learning, by continuous knowledge refinement through sequential learning episodes, emphasizes the accomplishment of incremental task performance improvement. In this paper, we describe how a personalized temporal event scheduling system SmartCalendar, can benefit from STEP perpetual learning. We adopt the interval temporal logic to represent events’ temporal relationships and determine if events are temporally inconsistent. To provide strategies that approach user preferences for handling temporal inconsistencies, we propose SmartCalendar to recognize, resolve and learn from temporal inconsistencies based on STEP perpetual learning. SmartCalendar has several cornerstones: similarity measures for temporal inconsistency; a sparse decomposition method to utilize historical data; and a loss function based on cross-entropy to optimize performance. The experimental results on the collected dataset show that SmartCalendar incrementally improves its scheduling performance and substantially outperforms comparison methods.

Suggested Citation

  • Jiahua Tang & Du Zhang & Xibin Sun & Haiou Qin, 2022. "Improving Temporal Event Scheduling through STEP Perpetual Learning," Sustainability, MDPI, vol. 14(23), pages 1-23, December.
    • Handle: RePEc:gam:jsusta:v:14:y:2022:i:23:p:16178-:d:992923
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    References listed on IDEAS

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    1. Mahmoud Javanmardi & Mehran Fasihozaman Langerudi & Ramin Shabanpour & Abolfazl Mohammadian, 2016. "An optimization approach to resolve activity scheduling conflicts in ADAPTS activity-based model," Transportation, Springer, vol. 43(6), pages 1023-1039, November.
    2. Auld, Joshua & Mohammadian, Abolfazl(Kouros), 2012. "Activity planning processes in the Agent-based Dynamic Activity Planning and Travel Scheduling (ADAPTS) model," Transportation Research Part A: Policy and Practice, Elsevier, vol. 46(8), pages 1386-1403.
    3. Auld, Joshua & Mohammadian, Abolfazl (Kouros) & Doherty, Sean T., 2009. "Modeling activity conflict resolution strategies using scheduling process data," Transportation Research Part A: Policy and Practice, Elsevier, vol. 43(4), pages 386-400, May.
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