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Learning operators while planning

  • José L. S. Ferreira
  • Ernesto J. F. Costa
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 727)

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.

Keywords

Learning Planning 

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References

  1. Carbonnel, J. G & Gill, Y. (1987). Learning by Experimentation, Proceedings of the Fourth International Machine Learning Workshop.Google Scholar
  2. 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.Google Scholar
  3. DeJong, G. F. & Mooney, R. J. (1986). Explanation-Based Learning: An Alternative View, Machine Learning 1,pp 145–176.Google Scholar
  4. Drummond, M. & Currie, K. (1989). Goal Ordering in Partially Ordered Plans, Proceedings of the Nineth International Joint Conference on Artificial Intelligence, pp 960–965.Google Scholar
  5. Fikes, R. E. & Nilsson, N. J. (1972). Learning and Executing Generalized Robot Plans, Artificial Intelligence 3, pp 251–288.Google Scholar
  6. Gil, Y. (1991) A Domain Independent Framework for Effective Experimentation in Planning, Proceedings of th Eighth International Workshop on Machine Learning, pp 13–17.Google Scholar
  7. Kadie, Carl M. (1988). Diffy-S: Learning Robot Operator Schemata from Examples, Proceedings of the Fifth International Conference on Machine Learning, pp 430–436.Google Scholar
  8. Korf, R. E. (1985), Macro-Operators: A weak Method for Learning, Artificial Intelligence 26, pp 35–77.Google Scholar
  9. 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.Google Scholar
  10. Mitchell, T. M., Keller, R. & Kedar-Cabelli, S. (1986). Explanation-Based Generalization: A Unifying View, Machine Learning 1, pp 47–80.Google Scholar
  11. 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.Google Scholar
  12. Mooney, R. J. (1988). Generalizing the Order of Operators in Macro-Operators, Proceedings of th Fifth International Workshop on Machine Learning, pp 270–283.Google Scholar
  13. Sacerdoti, E. D. (1974). Planning in a Hierarchy of Abstraction Spaces, Artificial Intelligence 4, pp 115–135.Google Scholar
  14. 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.Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 1993

Authors and Affiliations

  • José L. S. Ferreira
    • 1
  • Ernesto J. F. Costa
    • 1
  1. 1.Laboratório de Informática e SistemasUniv. CoimbraCoimbra

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