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Abstract: Hidden Markov models (hmms) and partially observable Markov decision processes (pomdps) provide a useful tool for modeling dynamical systems. They are particularly useful for representing environments such as road networks and office buildings, which are typical for robot navigation and planning. In a previous paper [SK97] we have empirically shown that by taking advantage of readily available odometric information, learning hmms/pomdps can be made faster and better. This paper extends our... (Update)
Context of citations to this paper: More
.... Dark bright regions and vertical edges are used in [13, 74] and hallways, openings and doors are used by the approach described in [41, 62, 63]. Others have proposed methods for learning what feature to extract, through a training phase in which the robot it told its...
.... Dark bright regions and vertical edges are used in [31,159] and hallways, openings and doors are used by the approach described in [82,135,138]. Others have proposed methods for learning what feature to extract, through a training phase in which the robot is told its...
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BibTeX entry: (Update)
Shatkay, H., & Kaelbling, L. (1997b). Learning hidden Markov models with geometric information (Tech. rep. http://citeseer.ist.psu.edu/article/shatkay97learning.html More
@techreport{ shatkay97learning,
author = "Hagit Shatkay and Leslie Pack Kaelbling",
title = "Learning Hidden Markov Models with Geometric Information",
number = "CS-97-04",
year = "1997",
url = "citeseer.ist.psu.edu/article/shatkay97learning.html" }
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