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.
This is a preview of subscription content, log in via an institution.
Buying options
Tax calculation will be finalised at checkout
Purchases are for personal use only
Learn about institutional subscriptionsPreview
Unable to display preview. Download preview PDF.
References
Boger, J., Poupart, P., Hoey, J., Boutilier, C., Fernie, G., Mihailidis, A.: A decision-theoretic approach to task assistance for persons with dementia. In: IJCAI (2005)
Bui, H., Phung, D., Venkatesh, S.: Hierarchical hidden markov models with general state hierarchy. In: Proceedings of AAAI 2004 (2004)
Bui, H., Venkatesh, S., West, G.: Policy recognition in the abstract hidden markov models. JAIR 17 (2002)
Doucet, A., De Freitas, N., Gordon, N. (eds.): Sequential Monte Carlo methods in practice (2001)
Fine, S., Singer, Y., Tishby, N.: The hierarchical hidden markov model: Analysis and applications. Machine Learning 32, 41–62 (1998)
Getoor, L., Taskar, B.: Introduction to Statistical Relational Learning. MIT Press, Cambridge (2007)
Kersting, K., De Raedt, L., Raiko, T.: Logical hidden markov models. JAIR 25, 425–456 (2006)
Leo, M., D’Orazio, T., Spagnolo, P.: Human activity recognition for automatic visual surveillance of wide areas. In: VSSN 2004: Proceedings of the ACM 2nd international workshop on Video surveillance & sensor networks (2004)
Milch, B., Russell, S.: General-purpose mcmc inference over relational structures. In: Proceedings of the 22nd Annual Conference on Uncertainty in Artificial Intelligence (UAI 2006) (2006)
Murphy, K., Paskin, M.: Linear time inference in hierarchical HMMs. In: Proceedings of Neural Information Proceesing Systems (2001)
Myers, K., Berry, P., Blythe, J., Conleyn, K., Gervasio, M., McGuinness, D., Morley, D., Pfeffer, A., Pollack, M., Tambe, M.: An intelligent personal assistant for task and time management. AI Magazine (2007)
Natarajan, S., Tadepalli, P., Fern, A.: A relational hierarchical model for decision-theoretic assistance. In: Blockeel, H., Ramon, J., Shavlik, J., Tadepalli, P. (eds.) ILP 2007. LNCS (LNAI), vol. 4894, pp. 175–190. Springer, Heidelberg (2008)
Zettlemoyer, L.S., Pasula, H.M., Kaelbling, L.P.: Logical particle filtering. In: Proceedings of the Dagstuhl Seminar on Probabilistic, Logical, and Relational Learning (2007)
Author information
Authors and Affiliations
Editor information
Rights and permissions
Copyright information
© 2008 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Natarajan, S., Bui, H.H., Tadepalli, P., Kersting, K., Wong, WK. (2008). Logical Hierarchical Hidden Markov Models for Modeling User Activities. In: Železný, F., Lavrač, N. (eds) Inductive Logic Programming. ILP 2008. Lecture Notes in Computer Science(), vol 5194. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-85928-4_17
Download citation
DOI: https://doi.org/10.1007/978-3-540-85928-4_17
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-85927-7
Online ISBN: 978-3-540-85928-4
eBook Packages: Computer ScienceComputer Science (R0)