Reflective Thinking, Machine Learning, and User Authentication via Artificial K-lines

Abstract

Artificial K-lines (AKL) is a structure that can be used to store different types of knowledge, as long as this knowledge is represented by series of events connected by causality. Unlike, and, perhaps, complementary to, Artificial Neural Networks (ANN), AKL can combine inter-domain knowledge and its knowledge base can be augmented dynamically without rebuilding of the entire system. In this paper we demonstrate the diversity of AKL by illustrating, through examples, its workings for three applications across three completely different areas of study. 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. The third example presents a behavior metrics based method for password authentication.