Automated Generation of Understandable Contingency Plans
Abstract
Markov Decision Processes (MDPs) and contingency planning (CP) are two widely used approaches to planning under uncertainty. MDPs are attractive because the model is extremely general and because many algorithms exist for deriving optimal plans. In contrast, CP is normally performed using heuristic techniques that do not guarantee optimality, but the resulting plans are more compact and more understandable. The inability to present MDP policies in a clear, intuitive way has limited their applicability in some important domains. We introduce an anytime algorithm for deriving contingency plans that combines the advantages of the two approaches.
Cite
Text
Horstmann and Zilberstein. "Automated Generation of Understandable Contingency Plans." International Joint Conference on Artificial Intelligence, 2003.Markdown
[Horstmann and Zilberstein. "Automated Generation of Understandable Contingency Plans." International Joint Conference on Artificial Intelligence, 2003.](https://mlanthology.org/ijcai/2003/horstmann2003ijcai-automated/)BibTeX
@inproceedings{horstmann2003ijcai-automated,
title = {{Automated Generation of Understandable Contingency Plans}},
author = {Horstmann, Max and Zilberstein, Shlomo},
booktitle = {International Joint Conference on Artificial Intelligence},
year = {2003},
pages = {1518-1519},
url = {https://mlanthology.org/ijcai/2003/horstmann2003ijcai-automated/}
}