Faster Heuristic Search Algorithms for Planning with Uncertainty and Full Feedback
Abstract
Recent algorithms like RTDP and LAO* combine the strength of Heuristic Search (HS) and Dynamic Programming (DP) methods by exploiting knowledge of the initial state and an admissible heuristic function for producing optimal policies without evaluating the entire space. In this paper, we introduce and analyze three new HS/DP algorithms.
Cite
Text
Bonet and Geffner. "Faster Heuristic Search Algorithms for Planning with Uncertainty and Full Feedback." International Joint Conference on Artificial Intelligence, 2003.Markdown
[Bonet and Geffner. "Faster Heuristic Search Algorithms for Planning with Uncertainty and Full Feedback." International Joint Conference on Artificial Intelligence, 2003.](https://mlanthology.org/ijcai/2003/bonet2003ijcai-faster/)BibTeX
@inproceedings{bonet2003ijcai-faster,
title = {{Faster Heuristic Search Algorithms for Planning with Uncertainty and Full Feedback}},
author = {Bonet, Blai and Geffner, Hector},
booktitle = {International Joint Conference on Artificial Intelligence},
year = {2003},
pages = {1233-1238},
url = {https://mlanthology.org/ijcai/2003/bonet2003ijcai-faster/}
}