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Prediction-Directed Compression of POMDPs

2008

Conference Paper

ei


High dimensionality of belief space in partially observable Markov decision processes (POMDPs) is one of the major causes that severely restricts the applicability of this model. Previous studies have demonstrated that the dimensionality of a POMDP can eventually be reduced by transforming it into an equivalent predictive state representation (PSR). In this paper, we address the problem of finding an approximate and compact PSR model corresponding to a given POMDP model. We formulate this problem in an optimization framework. Our algorithm tries to minimize the potential error that missing some core tests may cause. We also present an empirical evaluation on benchmark problems, illustrating the performance of this approach.

Author(s): Boularias, A. and Izadi, M. and Chaib-Draa, B.
Book Title: ICMLA 2008
Journal: Proceedings of the Seventh International Conference on Machine Learning and Applications (ICMLA 2008)
Pages: 99-105
Year: 2008
Month: December
Day: 0
Editors: Wani, M. A., X.-W. Chen, D. Casasent, L. A. Kurgan, T. Hu, K. Hafeez
Publisher: IEEE

Department(s): Empirical Inference
Bibtex Type: Conference Paper (inproceedings)

DOI: 10.1109/ICMLA.2008.115
Event Name: Seventh International Conference on Machine Learning and Applications
Event Place: San Diego, CA, USA

Address: Piscataway, NJ, USA
Digital: 0
Language: en
Organization: Max-Planck-Gesellschaft
School: Biologische Kybernetik

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BibTex

@inproceedings{6829,
  title = {Prediction-Directed Compression of POMDPs},
  author = {Boularias, A. and Izadi, M. and Chaib-Draa, B.},
  journal = {Proceedings of the Seventh International Conference on Machine Learning and Applications (ICMLA  2008)},
  booktitle = {ICMLA 2008},
  pages = {99-105},
  editors = {Wani, M. A., X.-W. Chen, D. Casasent, L. A. Kurgan, T. Hu, K. Hafeez},
  publisher = {IEEE},
  organization = {Max-Planck-Gesellschaft},
  school = {Biologische Kybernetik},
  address = {Piscataway, NJ, USA},
  month = dec,
  year = {2008},
  month_numeric = {12}
}