Learning to Plan in Complex Domains
Abstract
Most real-world domains involve large search spaces with large amounts of subgoal interaction. Problems in these domains usually require finding a solution that best meets a set of goals and constraints. Finding such a solution often requires optimizing several real-valued performance measures, as well as satisfying a set of Boolean constraints. Most learning planners have concentrated on problems completely defined by Boolean constraints and have ignored real-valued performance measures. These problems have had low subgoal interaction and small search spaces. We introduce a new general learning problem solver for complex real-world problems.
Cite
Text
Rudy and Kibler. "Learning to Plan in Complex Domains." International Conference on Machine Learning, 1989. doi:10.1016/B978-1-55860-036-2.50051-5Markdown
[Rudy and Kibler. "Learning to Plan in Complex Domains." International Conference on Machine Learning, 1989.](https://mlanthology.org/icml/1989/rudy1989icml-learning/) doi:10.1016/B978-1-55860-036-2.50051-5BibTeX
@inproceedings{rudy1989icml-learning,
title = {{Learning to Plan in Complex Domains}},
author = {Rudy, David and Kibler, Dennis F.},
booktitle = {International Conference on Machine Learning},
year = {1989},
pages = {180-182},
doi = {10.1016/B978-1-55860-036-2.50051-5},
url = {https://mlanthology.org/icml/1989/rudy1989icml-learning/}
}