PASCAL - Pattern Analysis, Statistical Modelling and Computational Learning

Feature Dynamic Bayesian Networks
Marcus Hutter
In: Second Conference on Artificial General Intelligence, 6-9 March 2009, Arlington, United States.

Abstract

Feature Markov Decision Processes (PhiMDPs) are well-suited for learning agents in general environments. Nevertheless, unstructured (Phi)MDPs are limited to relatively simple environments. Structured MDPs like Dynamic Bayesian Networks (DBNs) are used for large-scale real-world problems. In this article I extend PhiMDP to PhiDBN. The primary contribution is to derive a cost criterion that allows 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.

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EPrint Type:Conference or Workshop Item (Paper)
Project Keyword:Project Keyword UNSPECIFIED
Subjects:Computational, Information-Theoretic Learning with Statistics
Theory & Algorithms
ID Code:5849
Deposited By:Marcus Hutter
Deposited On:08 March 2010