Earticle

초록

영어

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.

목차

Abstract
 1. Introduction and Background
 2. Artificial K-lines
 3. Reflective Thinking and Artificial Creativity
  3.1. Robot & Assembly Line Example
  3.2 Artificial K-lines and Artificial Creativity
 4. Artificial K-lines and Machine Learning
 5. Artificial K-lines and User Authentication
 6. Comparison with Neural Networks
 7. Conclusion
 References

저자

• Anestis A. Toptsis [ Dept. of Computer Science and Engineering, York University ]

참고문헌

발행기관

간행물

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• 간행물명

  International Journal of Advanced Science and Technology

• 간기

  월간

• pISSN

  2005-4238

• 수록기간

  2008~2016

• 십진분류

  KDC 505 DDC 605