Jiao Licheng, Zhao Jiaxuan, Wang Chao, Liu Xu, Liu Fang, Li Lingling, Shang Ronghua, Li Yangyang, Ma Wenping, Yang Shuyuan
School of Artificial Intelligence, Xidian University, Xi'an, China.
Research (Wash D C). 2024 Aug 16;7:0442. doi: 10.34133/research.0442. eCollection 2024.
Nature, with its numerous surprising rules, serves as a rich source of creativity for the development of artificial intelligence, inspiring researchers to create several nature-inspired intelligent computing paradigms based on natural mechanisms. Over the past decades, these paradigms have revealed effective and flexible solutions to practical and complex problems. This paper summarizes the natural mechanisms of diverse advanced nature-inspired intelligent computing paradigms, which provide valuable lessons for building general-purpose machines capable of adapting to the environment autonomously. According to the natural mechanisms, we classify nature-inspired intelligent computing paradigms into 4 types: evolutionary-based, biological-based, social-cultural-based, and science-based. Moreover, this paper also illustrates the interrelationship between these paradigms and natural mechanisms, as well as their real-world applications, offering a comprehensive algorithmic foundation for mitigating unreasonable metaphors. Finally, based on the detailed analysis of natural mechanisms, the challenges of current nature-inspired paradigms and promising future research directions are presented.
大自然有着众多令人惊叹的规律,是人工智能发展的丰富创意源泉,激励着研究人员基于自然机制创建了多种受自然启发的智能计算范式。在过去几十年里,这些范式为实际的复杂问题揭示了有效且灵活的解决方案。本文总结了各种先进的受自然启发的智能计算范式的自然机制,这些机制为构建能够自主适应环境的通用机器提供了宝贵经验。根据自然机制,我们将受自然启发的智能计算范式分为四类:基于进化的、基于生物的、基于社会文化的和基于科学的。此外,本文还阐述了这些范式与自然机制之间的相互关系及其在现实世界中的应用,为减少不合理隐喻提供了全面的算法基础。最后,在对自然机制进行详细分析的基础上,提出了当前受自然启发范式面临的挑战以及未来有前景的研究方向。