We propose a new approach to value function approximation which combines linear temporal difference reinforcement learning with subspace identification. In practical applications...
If you have ever watched movies or television shows, you know how easy it is to tell the good characters from the bad ones. Little, however, is known “whether” or “how” com...
We introduce a new method for automatically constructing concept hierarchies where the concept nodes follow a generalization / specialization relation. Starting from a set of conc...
In this paper we show how a natural language system can learn to find the antecedents of relative pronouns. We use a well-known conceptual clustering system to create a case-based...
We present a method for parameter learning in relational Bayesian networks (RBNs). Our approach consists of compiling the RBN model into a computation graph for the likelihood fun...