A human-like approach to learning from examples
Sekou L. Remy · 2014
In this paper, we describe the system components, present the implemented architecture, and show the effect of an interactive learning system in action. We evaluate the system's ability to learning with two datasets, one synthetic and the other from writing samples gathered from human subjects. With both datasets, the respective test and training sets are the same so as to permit the process of interactive learning to be observed as it occurs. At it's core, this learning approach transforms sensory input and actuator output into rank P = 1 spaces, and uses learn a probabilistic mapping between these two “states” to perform the target task. In the future P >1 will be used internally, and we conclude this work with a brief treatment on why we believe this to be a useful trajectory.