ML Anthology

Learning Abstract Planning Cases

Ralph Bergmann, Wolfgang Wilke
ECML-PKDD 1995 pp. 55-76
doi:10.1007/3-540-59286-5_49 /ecmlpkdd/1995/bergmann1995ecml-learning/

Abstract

In this paper, we propose the PARIS approach for improving complex problem solving by learning from previous cases. In this approach, abstract planning cases are learned from given concrete cases. For this purpose, we have developed a new abstraction methodology that allows to completely change the representation language of a planning case, when the concrete and abstract languages are given by the user. Furthermore, we present a learning algorithm which is correct and complete with respect to the introduced model. An empirical study in the domain of process planning in mechanical engineering shows significant improvements in planning efficiency through learning abstract cases while an explanation-based learning method only causes a very slight improvement.

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Cite

Text

Bergmann and Wilke. "Learning Abstract Planning Cases." European Conference on Machine Learning, 1995. doi:10.1007/3-540-59286-5_49

Markdown

[Bergmann and Wilke. "Learning Abstract Planning Cases." European Conference on Machine Learning, 1995.](https://mlanthology.org/ecmlpkdd/1995/bergmann1995ecml-learning/) doi:10.1007/3-540-59286-5_49

BibTeX

@inproceedings{bergmann1995ecml-learning,
  title     = {{Learning Abstract Planning Cases}},
  author    = {Bergmann, Ralph and Wilke, Wolfgang},
  booktitle = {European Conference on Machine Learning},
  year      = {1995},
  pages     = {55-76},
  doi       = {10.1007/3-540-59286-5_49},
  url       = {https://mlanthology.org/ecmlpkdd/1995/bergmann1995ecml-learning/}
}