Proceedings of the 2nd Conference on Artificial General Intelligence (2009)

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Feature Dynamic Bayesian Networks

Authors
Marcus Hutter
Corresponding Author
Marcus Hutter
Available Online June 2009.
DOI
10.2991/agi.2009.6How to use a DOI?
Abstract

Feature Markov Decision Processes (MDPs) [Hut09] are well-suited for learning agents in general environments. Nevertheless, unstructured ()MDPs are limited to rela- tively simple environments. Structured MDPs like Dynamic Bayesian Networks (DBNs) are used for large-scale real- world problems. In this article I extend MDP to DBN. The primary contribution is to derive a cost criterion that al- lows to automatically extract the most relevant features from the environment, leading to the "best" DBN representation. I discuss all building blocks required for a complete general learning algorithm.

Copyright
© 2009, the Authors. Published by Atlantis Press.
Open Access
This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).

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Volume Title
Proceedings of the 2nd Conference on Artificial General Intelligence (2009)
Series
Advances in Intelligent Systems Research
Publication Date
June 2009
ISBN
10.2991/agi.2009.6
ISSN
1951-6851
DOI
10.2991/agi.2009.6How to use a DOI?
Copyright
© 2009, the Authors. Published by Atlantis Press.
Open Access
This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).

Cite this article

risenwbib
TY  - CONF
AU  - Marcus Hutter
PY  - 2009/06
DA  - 2009/06
TI  - Feature Dynamic Bayesian Networks
BT  - Proceedings of the 2nd Conference on Artificial General Intelligence (2009)
PB  - Atlantis Press
SP  - 26
EP  - 31
SN  - 1951-6851
UR  - https://doi.org/10.2991/agi.2009.6
DO  - 10.2991/agi.2009.6
ID  - Hutter2009/06
ER  -