# Representing and Using Knowledge with the Contextual Evaluation Model

## TL;DR

The paper presents the Contextual Evaluation Model (CEM), a new knowledge representation framework that integrates facts, patterns, and sequences, demonstrated through an implementation called V5 with various applications.

## Contribution

It introduces the CEM, a novel unified model for knowledge representation and manipulation, along with an implementation and algorithms for pattern learning and natural language understanding.

## Key findings

- CEM effectively integrates facts, patterns, and sequences.
- V5 demonstrates practical applications like voice recognition.
- The model supports autonomous learning of simplified natural language.

## Abstract

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.