Article

Personalized Course Schedule Planning Using Answer Set Programming

Authors:

Muhammed Kerem Kahraman,

Esra ErdemAuthors Info & Claims

Practical Aspects of Declarative Languages: 21th International Symposium, PADL 2019, Lisbon, Portugal, January 14-15, 2019, Proceedings

Pages 37 - 45

https://doi.org/10.1007/978-3-030-05998-9_3

Published: 14 January 2019 Publication History

Abstract

Course scheduling or timetabling is a well-known problem that is generally studied from the perspective of schools; the goal is to schedule the courses, considering, e.g., the expected number of students, the sizes of the available classrooms, time conflicts between courses of the same category. We study a complementary problem to help the students during the course registration periods; the goal is to plan personalized course schedules for students, considering, e.g., their preferences over sections, instructors, distribution of the courses. We present a declarative method to compute personalized course schedules, and an application of this method using answer set programming, and discuss promising results of some preliminary user evaluations via surveys.

References

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Cited By

Sugimori IInoue KNabeshima HSchaub TSoh TTamura NBanbara M(2024)ASP-Based Large Neighborhood Prioritized Search for Course TimetablingLogic Programming and Nonmonotonic Reasoning10.1007/978-3-031-74209-5_5(57-70)Online publication date: 11-Oct-2024
https://dl.acm.org/doi/10.1007/978-3-031-74209-5_5
Francescutto GSchekotihin KEl-Kholany M(2021)Solving a Multi-resource Partial-Ordering Flexible Variant of the Job-Shop Scheduling Problem with Hybrid ASPLogics in Artificial Intelligence10.1007/978-3-030-75775-5_21(313-328)Online publication date: 17-May-2021
https://dl.acm.org/doi/10.1007/978-3-030-75775-5_21

Index Terms

Personalized Course Schedule Planning Using Answer Set Programming
1. Applied computing
2. Theory of computation
  1. Design and analysis of algorithms
    1. Approximation algorithms analysis
      1. Scheduling algorithms
    2. Online algorithms
      1. Online learning algorithms
        Scheduling algorithms
  2. Theory and algorithms for application domains
    1. Machine learning theory
      1. Reinforcement learning
        Sequential decision making

Index terms have been assigned to the content through auto-classification.

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Information & Contributors

Information

Published In

cover image Guide Proceedings

Practical Aspects of Declarative Languages: 21th International Symposium, PADL 2019, Lisbon, Portugal, January 14-15, 2019, Proceedings

Jan 2019

247 pages

ISBN:978-3-030-05997-2

DOI:10.1007/978-3-030-05998-9

Editors:
José Júlio Alferes
Universidade Nova de Lisboa, Lisbon, Portugal
,
Moa Johansson
Chalmers University of Technology, Gothenburg, Sweden

© Springer Nature Switzerland AG 2019.

Publisher

Springer-Verlag

Berlin, Heidelberg

Publication History

Published: 14 January 2019

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Cited By

Sugimori IInoue KNabeshima HSchaub TSoh TTamura NBanbara M(2024)ASP-Based Large Neighborhood Prioritized Search for Course TimetablingLogic Programming and Nonmonotonic Reasoning10.1007/978-3-031-74209-5_5(57-70)Online publication date: 11-Oct-2024
https://dl.acm.org/doi/10.1007/978-3-031-74209-5_5
Francescutto GSchekotihin KEl-Kholany M(2021)Solving a Multi-resource Partial-Ordering Flexible Variant of the Job-Shop Scheduling Problem with Hybrid ASPLogics in Artificial Intelligence10.1007/978-3-030-75775-5_21(313-328)Online publication date: 17-May-2021
https://dl.acm.org/doi/10.1007/978-3-030-75775-5_21

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