Approximately Optimal Monitoring of Plan Preconditions
Abstract
Monitoring plan preconditions can allow for replanning when a precondition fails, generally far in advance of the point in the plan where the precondition is relevant. However, monitoring is generally costly, and some precondition failures have a very small impact on plan quality. We formulate a model for optimal precondition monitoring, using partially-observable Markov decisions processes, and describe methods for solving this model effectively, though approximately. Specifically, we show that the single-precondition monitoring problem is generally tractable, and the multiple-precondition monitoring policies can be effectively approximated using single-precondition solutions.
Cite
Text
Boutilier. "Approximately Optimal Monitoring of Plan Preconditions." Conference on Uncertainty in Artificial Intelligence, 2000.Markdown
[Boutilier. "Approximately Optimal Monitoring of Plan Preconditions." Conference on Uncertainty in Artificial Intelligence, 2000.](https://mlanthology.org/uai/2000/boutilier2000uai-approximately/)BibTeX
@inproceedings{boutilier2000uai-approximately,
title = {{Approximately Optimal Monitoring of Plan Preconditions}},
author = {Boutilier, Craig},
booktitle = {Conference on Uncertainty in Artificial Intelligence},
year = {2000},
pages = {54-62},
url = {https://mlanthology.org/uai/2000/boutilier2000uai-approximately/}
}