Automated Generation of Understandable Contingency Plans
Proceedings of the Eighteenth International Joint Conference on Artificial Intelligence. Pages 1518-1518.
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.
@inproceedings{ijcai2003p257,
title = {Automated Generation of Understandable Contingency Plans},
author = {Horstmann, Max and Zilberstein, Shlomo},
booktitle = {Proceedings of the Eighteenth International Joint Conference on
Artificial Intelligence, {IJCAI-03}},
publisher = {International Joint Conferences on Artificial Intelligence},
editor = {Georg Gottlob},
pages = {1518--1518},
year = {2003},
url = {https://ijcai.org/proceedings/2003/257},
}
TY - CPAPER TI - Automated Generation of Understandable Contingency Plans AU - Horstmann, Max AU - Zilberstein, Shlomo T2 - Proceedings of the Eighteenth International Joint Conference on Artificial Intelligence, IJCAI-03 PB - International Joint Conferences on Artificial Intelligence PY - 2003 SP - 1518 EP - 1518 UR - https://ijcai.org/proceedings/2003/257 ER -Copied
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