人工认知与人工智能对抗下一代自主机器人代理
Giulio Sandini1, Alessandra Sciutti1, Pietro Morasso1
1Italian Institute of Technology, Cognitive Architecture for Collaborative Technologies (CONTACT) and Robotics, Brain and Cognitive Sciences (RBCS) Research Units, Genoa, Italy.
Frontiers in computational neuroscience
|April 8, 2024
概括
自主机器人的未来需要一种由大脑启发的,体现的方法,称为人工认知 (ACo),整合身体和认知,以获得更好的人机交互和信任.
科学领域:
- 机器人和人工智能 机器人和人工智能
- 认知神经科学 认知神经科学
- 人与机器人的交互
背景情况:
- 目前的工业/服务机器人趋势集中在人机合作上,这对于第四和第五次工业革命 (4IR,5IR) 至关重要.
- 人工智能 (AI) 经常被认为是这些系统的智能来源,但这种说法缺乏强有力的科学证据,并依赖于无体的,笛卡尔式的心身二元论.
- 现有的AI机器人通常采用无形的方法,反映了传统计算架构中的硬件-软件区别.
研究的目的:
- 提出一种新的方法,即人工认知 (ACo),用于开发具有先进认知能力的下一代自主机器人代理.
- 倡导一个由大脑启发的,体现的认知框架,它整合了"Bodyware"和"Cogniware",超越了心身二元论.
- 通过积极的知识获取和双向交互来增强概括性,可解释性和人机互信.
主要方法:
- 采用完全由大脑启发的,体现的认知方法,基于认知神经科学.
- 专注于通过双向人机交互来主动获取知识.
- 在一个统一的框架内整合开发机器人的原则,与前的行动表示和社会互动.
主要成果:
- 拟议的人工认知 (ACo) 方法旨在克服目前机器人领域无形人工智能的局限性.
- ACo促进了主动的知识获取,提高了机器人代理的概括性和可解释性.
- 它培养了一个以大脑为灵感的网络,用于人机合作,建立信任和相互问责,这是目前AI缺乏的.
结论:
- 下一代自主机器人代理人需要从无形的人工智能转向有形,大脑启发的人工认知 (ACo) 方法.
- 通过整合身体和认知,ACO提供了一条通往更具普遍性,可解释性和可信赖的人机交互的道路.
- 进一步的研究整合发展机器人,行动代表和社会互动对于推进ACO至关重要.
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