Semi-Human Instinctive Artificial Intelligence (SHIAI)

Providing robots (or any other intelligent embedded system) with manlike instincts will bring major issues of today artificial intelligence out of a deadlock. This paper proposes a nondeterministic decision making theory based on Semi Human Instincts implemented by learned potential fields, using neural networks and fuzzy logic offline and online learning algorithms, which enable the agent to perform in anonymous, dynamic and nondeterministic environments. SHI-AI is like a newly born baby who uses his/her instincts and will gradually become more and more intelligent as the brain learns more about its environment. The use of a new world modeling method called ARPL (Agent Relative Polar Localization) in SHI-AI enables the agent to perform better within anonymous environments where positioning is an important and complex issue.

Keywords:

SHI-AI , M2LAI , Potential Fields , Electric Fields , ARPL , LEFG , IVLWM , Fuzzy , Learning , Neural Networks