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《中国工程科学》 >> 2018年 第20卷 第4期 doi: 10.15302/J-SSCAE-2018.04.017

群智进化理论及其在智能机器人中的应用

1. 北京深度奇点科技有限公司,北京100086;

2. 复旦大学智能机器人研究院,上海 200433

资助项目 :中国工程院咨询项目“新一代人工智能引领下的智能制造研究”(2017-ZD-08-03) 收稿日期: 2018-08-10 修回日期: 2018-08-15

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摘要

群体智能(CI)已经在过去的几十年里被广泛研究。最知名的CI算法就是蚁群算法(ACO),它被用来通过CI涌现解决复杂的路径搜索问题。最近,DeepMind发布的AlphaZero程序,通过从零开始的自我对弈强化学习,在围棋、国际象棋、将棋上都取得了超越人类的成绩。通过在五子棋上试验并实现AlphaZero系列程序,以及对蒙特卡洛树搜索(MCTS)和ACO两种算法的分析和比较,AlphaZero的成功原因被揭示,它不仅是因为深度神经网络和强化学习,而且是因为MCTS算法,该算法实质上是一种CI涌现算法。在上述研究基础上,本文提出了一个CI进化理论,并将其作为走向人工通用智能(AGI)的通用框架。该算法融合了深度学习、强化学习和CI算法的优势,使得单个智能体能够通过CI涌现进行高效且低成本的进化。此CI进化理论在智能机器人中有天然的应用。一个云端平台被开发出来帮助智能机器人进化其智能模型。作为这个概念的验证,一个焊接机器人的焊接参数优化智能模型已经在云端平台上实现。

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