Expanding the Breadth of Ability in Artificial Intelligence Systems with Decision Trees

Abstract

This paper introduces a unique perspective. Rather than focusing on improving the already significant achievements of existing artificial intelligence algorithms, it investigates the potential of merging various algorithms to enhance their overall capabilities. Essential design aspects required for this integration are examined, and a prototype system is developed to demonstrate the practical application of these design principles. This method aims to broaden the range of capabilities accessible to a system, addressing the limitation of the narrow focus prevalent in contemporary artificial intelligence.