Feature Dynamic Bayesian Networks

Details Feature Dynamic Bayesian Networks Feature Dynamic Bayesian Networks

2008-12-25

arxiv.org/abs/0812.4581v1

Proc. 2nd Conf. on Artificial General Intelligence (AGI 2009) pages 67-73

Computer Science

Artificial Intelligence

7 pages

Scientific paper

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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