Modeling Transportation Routines using Hybrid Dynamic Mixed Networks
Vibhav Gogate, Rina Dechter, Bozhena Bidyuk, Craig Rindt, James Marca
This paper describes a general framework called Hybrid Dynamic Mixed Networks (HDMNs) which are Hybrid Dynamic Bayesian Networks that allow representation of discrete deterministic information in the form of constraints. We propose approximate inference algorithms that integrate and adjust well known algorithmic principles such as Generalized Belief Propagation, Rao-Blackwellised Particle Filtering and Constraint Propagation to address the complexity of modeling and reasoning in HDMNs. We use this framework to model a person's travel activity over time and to predict destination and routes given the current location. We present a preliminary empirical evaluation demonstrating the effectiveness of our modeling framework and algorithms using several variants of the activity model.
PDF Link: /papers/05/p217-gogate.pdf
AUTHOR = "Vibhav Gogate
and Rina Dechter and Bozhena Bidyuk and Craig Rindt and James Marca",
TITLE = "Modeling Transportation Routines using Hybrid Dynamic Mixed Networks",
BOOKTITLE = "Proceedings of the Twenty-First Conference Annual Conference on Uncertainty in Artificial Intelligence (UAI-05)",
PUBLISHER = "AUAI Press",
ADDRESS = "Arlington, Virginia",
YEAR = "2005",
PAGES = "217--224"