title:
 
Feature Dynamic Bayesian Networks
publication:
 
AGI-09
part of series:
  Advances in Intelligent Systems Research
ISBN:
  978-90-78677-24-6
ISSN:
  1951-6851
DOI:
  doi:10.2991/agi.2009.6 (how to use a DOI)
author(s):
 
Marcus Hutter
publication date:
 
May 2009
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:
 
Atlantis Press. This article is distributed under the terms of the Creative Commons Attribution License, which permits non-commercial use, distribution and reproduction in any medium, provided the original work is properly cited.
full text: