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  A Predictive Model for Imitation Learning in Partially Observable Environments

Boularias, A. (2008). A Predictive Model for Imitation Learning in Partially Observable Environments. In A. Wani, X.-W. Chen, D. Casasent, L. Kurgan, T. Hu, & K. Hafeez (Eds.), 2008 Seventh International Conference on Machine Learning and Applications (pp. 83-90). Piscataway, NJ, USA: IEEE.

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Item Permalink: http://hdl.handle.net/11858/00-001M-0000-0013-C629-C Version Permalink: http://hdl.handle.net/21.11116/0000-0003-36A3-8
Genre: Conference Paper

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 Creators:
Boularias, A1, Author              
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 Abstract: Learning by imitation has shown to be a powerful paradigm for automated learning in autonomous robots. This paper presents a general framework of learning by imitation for stochastic and partially observable systems. The model is a Predictive Policy Representation (PPR) whose goal is to represent the teacher‘s policies without any reference to states. The model is fully described in terms of actions and observations only. We show how this model can efficiently learn the personal behavior and preferences of an assistive robot user.

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 Dates: 2008-12
 Publication Status: Published in print
 Pages: -
 Publishing info: -
 Table of Contents: -
 Rev. Method: -
 Identifiers: DOI: 10.1109/ICMLA.2008.142
BibTex Citekey: 6830
 Degree: -

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Title: Seventh International Conference on Machine Learning and Applications
Place of Event: San Diego, CA, USA
Start-/End Date: 2008-12-11 - 2008-12-13

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Title: 2008 Seventh International Conference on Machine Learning and Applications
Source Genre: Proceedings
 Creator(s):
Wani, AM, Editor
Chen, X-W, Editor
Casasent, D, Editor
Kurgan, LA, Editor
Hu, T, Editor
Hafeez, K, Editor
Affiliations:
-
Publ. Info: Piscataway, NJ, USA : IEEE
Pages: - Volume / Issue: - Sequence Number: - Start / End Page: 83 - 90 Identifier: ISBN: 978-0-7695-3495-4