A General Model for Online Probabilistic Plan Recognition
Proceedings of the Eighteenth International Joint Conference on Artificial Intelligence. Pages 1309-1317.
Abstract
We present a new general framework for online probabilistic plan recognition called the Abstract Hidden Markov Memory Model (AHMEM). The new model is an extension of the existing Abstract 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 represent a richer class of probabilistic plans, and at the same time derive an efficient algorithm for plan recognition in the AHMEM based on the RaoBiackwellised Particle Filter approximate inference method.
@inproceedings{ijcai2003p186,
title = {A General Model for Online Probabilistic Plan Recognition},
author = {Bui, Hung Hai},
booktitle = {Proceedings of the Eighteenth International Joint Conference on
Artificial Intelligence, {IJCAI-03}},
publisher = {International Joint Conferences on Artificial Intelligence},
editor = {Georg Gottlob},
pages = {1309--1317},
year = {2003},
url = {https://ijcai.org/proceedings/2003/186},
}
TY - CPAPER TI - A General Model for Online Probabilistic Plan Recognition AU - Bui, Hung Hai T2 - Proceedings of the Eighteenth International Joint Conference on Artificial Intelligence, IJCAI-03 PB - International Joint Conferences on Artificial Intelligence PY - 2003 SP - 1309 EP - 1317 UR - https://ijcai.org/proceedings/2003/186 ER -Copied
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