走向数据和知识驱动的人工智能:关于神经符号计算的调查
IEEE transactions on pattern analysis and machine intelligence
|October 17, 2024
概括
神经符号计算 (NeSy) 将符号推理与神经网络学习集成为先进的人工智能. 这项调查概述了NeSySy的情况.
科学领域:
- 人工智能的人工智能
- 认知科学 认知科学
背景情况:
- 神经符号计算 (NeSy) 旨在将象征性AI的可解释性与神经网络的学习能力合并.
- 这种整合对于开发下一代人工智能至关重要.
- NeSy的研究解决了对人工智能系统的需求,这些系统既可以有效地推理,也可以有效地学习.
研究的目的:
- 提供关于神经符号计算近期进展的全面概述.
- 系统地分类和分析关键的NeSy方法及其特征.
- 确定未来的研究方向和该领域的开放问题.
主要方法:
- 对NeSy研究的历史审查,包括基础工作.
- 基于整合,知识表示,嵌入和功能性的最近的NeSy方法的分类.
- 在代表性应用任务上对选定的NeSy方法进行基准测试.
主要成果:
- 一个结构化的概述NeSy的演变,关键概念和驱动因素.
- 现代NeSy技术的分类及其成功应用.
- 在不同任务中对几种NeSy方法的实证评估.
结论:
- NeSy通过结合推理和学习来推进人工智能的巨大潜力.
- 该调查突出了成功的应用程序,并为NeSy方法提供了一个基准.
- 识别未解决的问题和未来的方向旨在加速数据和知识驱动人工智能的进展.
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