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