基于湿网络的AI:一种化学方法来实现机器人和人工智能的体内认知
Luisa Damiano1, Antonio Fleres1, Andrea Roli2,3
1Research Center for Complex Systems (CRiSiCo), Department of Communication, Arts and Media, IULM University, Milan, Italy.
Frontiers in robotics and AI
|January 21, 2026
概括
湿器基于网络的人工智能 (WNAI) 开创了使用合成化学网络的自主认知代理,超越了和生物混合系统. 这种方法侧重于自组织化学网络,用于自适应机器人智能.
科学领域:
- 机器人和人工智能 机器人和人工智能
- 合成生物学 合成生物学
- 计算神经科学是一种神经科学.
背景情况:
- 目前的人工智能研究往往侧重于基于的计算或生物模拟.
- 湿器神经形态工程探索合成化学网络用于认知功能.
- 现有的体内人工智能和外来机器人技术的方法可以通过合成领域来扩展.
研究的目的:
- 介绍Wetware基于网络的人工智能 (WNAI) 作为机器人认知的新方法.
- 框架认知作为一个物质地接地,新兴现象使用网络网络学,自构理论,和动作.
- 在AI和机器人学中为湿器神经形态工程提出一个程序框架.
主要方法:
- 整合了来自网络网络学,自动构造理论和动作理论的理论见解.
- 将WNAI的启发性作用定义为对体内AI,外来机器人和神经网络架构的补充.
- 概述了实施化学神经网络和原细胞剂的技术路线图.
主要成果:
- WNAI将重点从无体计算转移到用于认知的网状化学自我组织.
- 它将人工体现认知的设计空间扩展到完全合成的领域.
- 通过与神经网络的交换,WNAI促进了基于网络的认知的跨基质原则.
结论:
- WNAI为人工认知提供了一个超越和生物混合系统的新范式.
- 潜在的应用包括需要最小,适应性和基板敏感智能的机器人系统.
- 湿器神经形态工程为扩大AI和机器人的能力提供了一个框架.
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