Representing and Using Knowledge with the Contextual Evaluation Model
  This paper introduces the Contextual Evaluation Model (CEM), a novel method
for knowledge representation and manipulation. The CEM differs from existing
models in that it integrates facts, patterns and sequences into a single
contextual framework. V5, an implementation of the model is presented and
demonstrated with multiple annotated examples. The paper includes simulations
demonstrating how the model reacts to pleasure/pain stimuli. The 'thought' is
defined within the model and examples are given converting thoughts to
language, converting language to thoughts and how 'meaning' arises from
thoughts. A pattern learning algorithm is described. The algorithm is applied
to multiple problems ranging from recognizing a voice to the autonomous
learning of a simplified natural language.
