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.
@article{arxiv.1906.03253,
title = {Representing and Using Knowledge with the Contextual Evaluation Model},
author = {Victor E Hansen},
journal= {arXiv preprint arXiv:1906.03253},
year = {2019}
}