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/}
}