Abstract
Machine Learning has been used by a number of planning systems, allowing to learn macro operators and plan schemata. Most previous research, however, has not addressed the aspect of learning the operators for the planners to apply. We present a first version of PLOP, Planning and Learning OPerators, a system that acquires operators by interacting with a user while planning. Empirical results seem to suggest that this is a promising and useful use of learning systems in the context of planning. We present the first results of our work and discuss future goals of this research.
This is a preview of subscription content, log in via an institution to check access.
Preview
Unable to display preview. Download preview PDF.
References
Carbonnel, J. G & Gill, Y. (1987). Learning by Experimentation, Proceedings of the Fourth International Machine Learning Workshop.
Carbonnel, J. G. & Gill, Y. (1990). Learning by Experimentation: the Operator Refinement Method, Machine Learning: An Artificial Intelligence Approach, Volume III, Morgan Kaufmann, Irvine 1990.
DeJong, G. F. & Mooney, R. J. (1986). Explanation-Based Learning: An Alternative View, Machine Learning 1,pp 145–176.
Drummond, M. & Currie, K. (1989). Goal Ordering in Partially Ordered Plans, Proceedings of the Nineth International Joint Conference on Artificial Intelligence, pp 960–965.
Fikes, R. E. & Nilsson, N. J. (1972). Learning and Executing Generalized Robot Plans, Artificial Intelligence 3, pp 251–288.
Gil, Y. (1991) A Domain Independent Framework for Effective Experimentation in Planning, Proceedings of th Eighth International Workshop on Machine Learning, pp 13–17.
Kadie, Carl M. (1988). Diffy-S: Learning Robot Operator Schemata from Examples, Proceedings of the Fifth International Conference on Machine Learning, pp 430–436.
Korf, R. E. (1985), Macro-Operators: A weak Method for Learning, Artificial Intelligence 26, pp 35–77.
Laird, J.E., Rosenbloom, P. S. & Newell, A. (1986). Chunking in SOAR: The Anatomy of a General Learning Mechanism, Machine Learning 1, pp 11–46.
Mitchell, T. M., Keller, R. & Kedar-Cabelli, S. (1986). Explanation-Based Generalization: A Unifying View, Machine Learning 1, pp 47–80.
Mitchell, T., Utgoff, P. & Banerji, R. (1983). Learning by Experimentation: Acquiring and Refining Problem Solving Heuristics, Machine Learning: An Artificial Intelligence Approach, Volume I, Tioga, Palo Alto 1983.
Mooney, R. J. (1988). Generalizing the Order of Operators in Macro-Operators, Proceedings of th Fifth International Workshop on Machine Learning, pp 270–283.
Sacerdoti, E. D. (1974). Planning in a Hierarchy of Abstraction Spaces, Artificial Intelligence 4, pp 115–135.
Shell, P. & Carbonnel, J. (1989). Towards a General Framework for Composing Disjunctive and Iterative Macro-operators, Proceedings of the Nineth International Joint Conference on Artificial Intelligence, pp 596–602.
Author information
Authors and Affiliations
Editor information
Rights and permissions
Copyright information
© 1993 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Ferreira, J.L.S., Costa, E.J.F. (1993). Learning operators while planning. In: Filgueiras, M., Damas, L. (eds) Progress in Artificial Intelligence. EPIA 1993. Lecture Notes in Computer Science, vol 727. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-57287-2_57
Download citation
DOI: https://doi.org/10.1007/3-540-57287-2_57
Published:
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-57287-9
Online ISBN: 978-3-540-48036-5
eBook Packages: Springer Book Archive