Maximum a Posteriori Path Estimation with Input Trace Perturbation: Algorithms and Application to Credible Rating of Human Routines
Abstract
Rating how well a routine activity is performed can be valuable in a variety of domains. Making the rating inexpensive and credible is a key aspect of the problem. We formalize the problem as MAP estimation in HMMs where the incoming trace needs repair. We present polynomial time algorithms for computing minimal repairs with maximal likelihood for HMMs, Hidden Semi-Markov Models (HSMMs) and a form of HMMs constrained with a fragment of the temporal logic LTL. We present some results to show the promise of our approach.
Cite
Text
Wilson and Philipose. "Maximum a Posteriori Path Estimation with Input Trace Perturbation: Algorithms and Application to Credible Rating of Human Routines." International Joint Conference on Artificial Intelligence, 2005.Markdown
[Wilson and Philipose. "Maximum a Posteriori Path Estimation with Input Trace Perturbation: Algorithms and Application to Credible Rating of Human Routines." International Joint Conference on Artificial Intelligence, 2005.](https://mlanthology.org/ijcai/2005/wilson2005ijcai-maximum/)BibTeX
@inproceedings{wilson2005ijcai-maximum,
title = {{Maximum a Posteriori Path Estimation with Input Trace Perturbation: Algorithms and Application to Credible Rating of Human Routines}},
author = {Wilson, Daniel H. and Philipose, Matthai},
booktitle = {International Joint Conference on Artificial Intelligence},
year = {2005},
pages = {895-901},
url = {https://mlanthology.org/ijcai/2005/wilson2005ijcai-maximum/}
}