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

Andrew McInnis Jr, Mohammad Alshibli, Ahmad Alzaghal, Samir Hamada
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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.
用决策树扩展人工智能系统的能力范围
本文提出了一个独特的视角。它不是专注于改进现有人工智能算法已经取得的巨大成就,而是研究了合并各种算法以增强其整体能力的潜力。研究了这种融合所需的基本设计方面,并开发了一个原型系统来演示这些设计原则的实际应用。这种方法旨在拓宽系统的能力范围,解决当代人工智能普遍存在的焦点狭窄的局限性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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