In the status selection planning system, which is a kind of knowledge-based planning system, the quality of the solution depends on the status selection rules. However, it is usually difficult to acquire useful knowledge from human experts. The learning method of a status selection rule using inductive learning is proposed. The status selection rules are divided into several stages according to the planning process. Gathering a training set and learning a part of the knowledge inductively are repeated one by one from the previous stage rules. From the result of application to a job-shop problem, the effectiveness of the proposed method is shown.
Similar content being viewed by others
References
Araki, D. and Kojima, S. (1992) Inductive decision tree learning from numerical data. Journal of Japanese Society for Artificial Intelligence, 7(6), 992–1000 (in Japanese).
Ikkai, Y., Ohkawa, T. and Komoda, N. (1993) Dispatching rule exchangeable optimization planning system, in Proceedings of 2nd IEEE International Workshop on ETFA, Cairns, November, pp. 137–143.
Ikkai, Y., Ohkawa, T. and Komoda, N. (1994) Knowledge acquisition for status selection planning system, in Proceedings of 1994 International Conference on Data and Knowledge Systems for Manufacturing and Engineering, Hong Kong, May, pp. 56–61.
Ikkai, Y., Ohkawa, T. and Komoda, N. (1995) Recursive type learning method for knowledge-based planning system, in Proceedings of the 3rd International Conference on Computer Integrated Manufacturing, Singapore, July, pp. 1517–1524.
Kawashima, K. and Komoda, N. (1991) Muitivariate analytical knowledge acquisition method for knowledge based planning system, in Proceedings of International Conference on Industrial Electronics, Control and Instrumentation, Kobe, October, pp. 1622–1627.
Nakasuka, S. and Yoshida, T. (1992) Dynamic scheduling system utilizing machine learning as a knowledge acquisition tool. International Journal of Production Research, 30(2), 411–431.
Noronha, S. J. (1991) Knowledge-based approaches for scheduling problems: a survey. IEEE Transactions on Knowledge and Data Engineering, 3(2), 160–171.
Pesch, E. (1994) Learning in Automated Manufacturing, Springer-Verlag, Heidelberg.
Quinlan, J. R. (1986) Induction of decision trees. Machine Learning, 1(1), 81–106.
Zweben, M. and Fox, M. S. (eds) (1994) Intelligent Scheduling, Morgan Kaufmann Publishers, San Francisco, CA.
Author information
Authors and Affiliations
Rights and permissions
About this article
Cite this article
Ikkai, Y., Ohkawa, T. & Komoda, N. Recursive learning method for knowledge-based planning system. J Intell Manuf 7, 405–410 (1996). https://doi.org/10.1007/BF00123918
Issue Date:
DOI: https://doi.org/10.1007/BF00123918