在体现的神经形态代理人中,积极推断学习和发展
Sarah Hamburg1, Alejandro Jimenez Rodriguez1, Aung Htet1
1Department of Computing, Sheffield Hallam University, Sheffield S1 1WB, UK.
Entropy (Basel, Switzerland)
|July 26, 2024
概括
人类启发的体内人工智能需要更好的框架. 我们建议基于自由能量原理,对神经形态代理进行主动推断,以实现类似人类的计算和学习.
科学领域:
- 人工智能的人工智能
- 神经科学是一个神经科学.
- 机器人技术 机器人技术 机器人技术
背景情况:
- 人类的灵感加速了现实世界的应用体现的AI.
- 目前的发展神经机器人缺乏类似人类计算和学习的框架.
- 神经形态系统和发育性学习是现有的以人类为灵感的方法.
研究的目的:
- 提出一个统一的框架,以实现人体化人工智能的人类式计算和学习.
- 利用主动推断和自由能量原理 (FEP) 在神经形态代理人中实现自主精神发展.
- 解决在发育神经机器人学中的强有力的理论和实证支持的需要.
主要方法:
- 实体化神经形态药物的活性推理的理论和经验验证.
- 专注于嵌入式计算,神经形态接口和多时间尺度学习.
- 概述目前用于发育神经机器人的实施方法和工具箱.
主要成果:
- 展示主动推理对自主精神发展的潜力.
- 提供支持拟议框架的理论和经验证据.
- 确定现有的实施策略和现场挑战.
结论:
- 积极推断为体现的人工智能提供了一个统一的框架,其灵感来源于人类认知.
- 自由能量原理 (FEP) 对于开发具有人类学习能力的神经形态代理来说至关重要.
- 需要进一步的研究和开发,以催化发育神经机器人领域.
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