A General Model for Online Probabilistic Plan Recognition
Abstract
We present a new general framework for online Hidden Markov Memory Model (AHMEM). The Hidden Markov Model to allow the policy to have internal memory which can be updated in a Markov fashion. We show that the AHMEM can repre-sent a richer class of probabilistic plans, and at the same time derive an efficient algorithm for plan recognition in the AHMEM based on the Rao-Blackwellised Particle Filter approximate inference method. 1
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Text
Bui. "A General Model for Online Probabilistic Plan Recognition." International Joint Conference on Artificial Intelligence, 2003.Markdown
[Bui. "A General Model for Online Probabilistic Plan Recognition." International Joint Conference on Artificial Intelligence, 2003.](https://mlanthology.org/ijcai/2003/bui2003ijcai-general/)BibTeX
@inproceedings{bui2003ijcai-general,
title = {{A General Model for Online Probabilistic Plan Recognition}},
author = {Bui, Hung Hai},
booktitle = {International Joint Conference on Artificial Intelligence},
year = {2003},
pages = {1309-1318},
url = {https://mlanthology.org/ijcai/2003/bui2003ijcai-general/}
}