  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.
