Navigate Like a Cabbie: Probabilistic Reasoning from Observed Context-Aware Behavior
Conference Paper, Proceedings of 10th International Conference on Ubiquitous Computation (UbiComp '08), pp. 322 - 331, September, 2008
Abstract
We present PROCAB, an efficient method for Probabilistically Reasoning from Observed Context-Aware Behavior. It models the context-dependent utilities and underlying reasons that people take different actions. The model generalizes to unseen situations and scales to incorporate rich contextual information. We train our model using the route preferences of over 25 taxi drivers demonstrated in over 100,000 miles of collected data, and demonstrate the performance of our model inferring: (1) decision at next intersection, (2) route to known destination and (3) destination given partially traveled route.
BibTeX
@conference{Ziebart-2008-17065,author = {Brian D. Ziebart and Andrew Maas and Anind Dey and J. Andrew (Drew) Bagnell},
title = {Navigate Like a Cabbie: Probabilistic Reasoning from Observed Context-Aware Behavior},
booktitle = {Proceedings of 10th International Conference on Ubiquitous Computation (UbiComp '08)},
year = {2008},
month = {September},
pages = {322 - 331},
keywords = {Inverse Optimal Control, Probabilistic Reasoning, Navigation, Machine Learning},
}
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