Abstract
Factorial Hierarchical Hidden Markov Models (FHHMM) provides a powerful way to endow an autonomous mobile robot with efficient map-building and map-navigation behaviors. However, the inference mechanism in FHHMM has seldom been studied. In this paper, we suggest an algorithm that transforms a FHHMM into a Bayesian Network in order to be able to perform inference. As a matter of fact, inference in Bayesian Network is a well-known mechanism and this representation formalism provides a well grounded theoretical background that may help us to achieve our goal. The algorithm we present can handle two problems arising in such a representation change: (1) the cost due to taking into account multiple dependencies between variables (e.g. compute P(Y|X 1,X 2,...,X n )), and (2) the removal of the directed cycles that may be present in the source graph. Finally, we show that our model is able to learn faster than a classical Bayesian network based representation when few (or unreliable) data is available, which is a key feature when it comes to mobile robotics.
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© 2005 Springer-Verlag Berlin Heidelberg
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Gelly, S., Bredeche, N., Sebag, M. (2005). From Factorial and Hierarchical HMM to Bayesian Network: A Representation Change Algorithm. In: Zucker, JD., Saitta, L. (eds) Abstraction, Reformulation and Approximation. SARA 2005. Lecture Notes in Computer Science(), vol 3607. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11527862_8
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DOI: https://doi.org/10.1007/11527862_8
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-27872-6
Online ISBN: 978-3-540-31882-8
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