Artificial K-Lines and Applications

Abstract

We propose Artificial K-lines (AKL), a structure that can be used to capture knowledge through events associated by causality. Like Artificial Neural Networks (ANN), AKL facilitates learning by capturing knowledge based on training. Unlike and perhaps complimentary to ANN, AKL can combine knowledge from different domains and also it does not require that the entire knowledge base is available prior to the AKL usage. We present AKL and illustrate its workings for applications through two examples. The first example demonstrates that our structure can generate a solution where most other known technologies are either incapable of, or very complicated in doing so. The second example illustrates a novel, human-like, way of machine learning.