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Logical hierarchical hidden markov models for modeling user activities (2008)
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| Venue: | In Proc. of ILP-08 |
| Citations: | 15 - 1 self |
BibTeX
@INPROCEEDINGS{Natarajan08logicalhierarchical,
author = {Sriraam Natarajan and Hung H. Bui and Prasad Tadepalli and Kristian Kersting and Weng-keen Wong},
title = {Logical hierarchical hidden markov models for modeling user activities},
booktitle = {In Proc. of ILP-08},
year = {2008}
}
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Abstract
Abstract. Hidden Markov Models (HMM) have been successfully used in applications such as speech recognition, activity recognition, bioinformatics etc. There have been previous attempts such as Hierarchical HMMs and Abstract HMMs to elegantly extend HMMs at multiple levels of temporal abstraction (for example to represent the user’s activities). Similarly, there has been previous work such as Logical HMMs on extending HMMs to domains with relational structure. In this work we develop a representation that naturally combines the power of both relational and hierarchical models in the form of Logical Hierarchical Hidden Markov Models (LoHiHMMs). LoHiHMMs inherit the compactness of representation from Logical HMMs and the tractability of inference from Hierarchical HMMs. We outline two inference algorithms: one based on grounding the LoHiHMM to a propositional HMM and the other based on particle filtering adapted for this setting. We present the results of our experiments with the model in two simulated domains. 1
Keyphrases
logical hierarchical hidden markov model user activity logical hmms hierarchical hmms speech recognition propositional hmm relational structure abstract hmms multiple level activity recognition inference algorithm hierarchical model particle filtering previous work previous attempt simulated domain hidden markov model temporal abstraction
