Representing and Using Knowledge with the Contextual Evaluation Model
This work addresses knowledge representation for AI systems, but it appears incremental as it builds on existing models with a new contextual integration approach.
The paper tackles the problem of knowledge representation and manipulation by introducing the Contextual Evaluation Model (CEM), which integrates facts, patterns, and sequences into a contextual framework, and demonstrates its application through simulations and examples, including pattern learning for tasks like voice recognition and autonomous language learning.
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.