Abstract
This paper describes a mixed-initiative constraint satisfaction system for planning the academic schedules of university students. Our model is distinguished from traditional planning systems by applying mixed-initiative constraint reasoning algorithms which provide flexibility in satisfying individual student preferences and needs. The graphical interface emphasizes visualization and direct manipulation capabilities to provide an efficient interactive environment for easy communication between the system and the end user. The planning process is split into two phases. The first phase builds an initial plan using a systematic search method based on a variant of dynamic backtracking. The second phase involves a semi-systematic local search algorithm which supports mixed-initiative user interaction and control of the search process. Generated curriculum schedules satisfy both academic program constraints and user constraints and preferences. Part of the challenge in curriculum scheduling is handling multiple possible schedules which are equivalent under symmetry. We show to overcome these symmetries in the search process. Experiments with actual course planning data show that our mixed-initiative systems generates effective curriculum plans efficiently.
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References
Schaerf, A.: A survey of automated timetabling. Artificial Intelligence Review 13(2), 87–127 (1999)
Castro, C., Manzano, S.: Variable and Value Ordering: When Solving Balanced Academic Curriculum Problems. In: Proceedings of 6th Workshop of the ERCIM WG on Constraints, Prague (June 2001)
Walker, M., Whittaker, S.: Mixed-initiative in dialogue: an investiation into discourse segmentation. In: Proceedings of ACL 1990, Pittsburgh, PA, pp. 70–76 (1990)
Guinn, C.: Mechanisms for mixed-initiative human-computer collaborative discourse. In: Proceedings of ACL 1996, Santa Cruz, CA, pp. 27–205 (1996)
Allen, J.: Mixed-initiative planning: position paper. In: Presented at ARPA/Rome Labs Planning Initiative Workshop (1994)
Miller, B.: Is explicit representation of initiative desirable? In: Working Notes of AAAI 1997 Spring Symposium on Mixed Initiative Interaction, Stanford, CA (1997)
Prosser, P.: Hybrid algorithms for the constraint satisfaction problem. Computational Intelligence 9(3), 268–299 (1993)
Ginsberg, M.L.: Dynamic backtracking. Journal of Artificial Intelligence Research 1, 25–46 (1993)
Shaw, P.: Using constraint programming and local search methods to solve vehicle routing problems. In: Proceedings of fourth Conference on Principles and practice of Constraint Programming, Pisa, pp. 417–431 (1998)
Havens, W.S., Dilkina, B.N.: A Hybrid Schema for Systematic Local Search. In: Proceedings of Canadian Conference on AI 2004, London, ON, Canada, pp. 248–260 (2004)
Jussien, N., Lhomme, O.: Local search with constraint propagation and conflict-based heuristics. Journal of Artificial Intelligence 139, 21–45 (2002)
Flener, P., Frisch, A., et al.: Breaking row and column symmetries in matrix models. In: Van Hentenryck, P. (ed.) CP 2002. LNCS, vol. 2470, pp. 94–107. Springer, Heidelberg (2002)
Frisch, A., Hnich, B., et al.: Global Constraints for Lexicographic Orderings. In: Constraint Programming 2002, pp. 93–108 (2002)
Actenum Corporation, ConstraintWorks, Vancouver, British Columbia, Canada, http://www.actenum.com
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Wu, K., Havens, W.S. (2005). Modelling an Academic Curriculum Plan as a Mixed-Initiative Constraint Satisfaction Problem. In: Kégl, B., Lapalme, G. (eds) Advances in Artificial Intelligence. Canadian AI 2005. Lecture Notes in Computer Science(), vol 3501. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11424918_10
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DOI: https://doi.org/10.1007/11424918_10
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-25864-3
Online ISBN: 978-3-540-31952-8
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