群智进化理论及其在智能机器人中的应用

Theory of Collective Intelligence Evolution and Its Applications in Intelligent Robots

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。

Collective intelligence(CI)is widely studied in the past few decades.The most well-known CI algorithm is the ant colony optimization(ACO).ACO is used to solve complex path searching problems through CI emergence.Recently,DeepMind announced the AlphaZero program which has achieved superhuman performance in the game of Go,Chess,and Shogi,by tabula rasa reinforcement learning from games of self-play.By experimenting and implementing the AlphaZero series program in the game of Gomoku,along with analyzing and comparing the Monte-Carlo tree search(MCTS)and ACO algorithms,it is realized that the success of AlphaZero is not only due to the deep neural network and reinforcement learning,but also due to the MCTS algorithm,which is discovered to be a CI emergence algorithm.Thus we propose a CI evolution theory,as a general framework towards artificial general intelligence(AGI).Combining the strengths of deep learning,reinforcement learning,and CI algorithm,CI evolution theory enables individual intelligence to evolve with high efficiency and low cost through CI emergence.This CI evolution theory has natural applications in intelligent robots.A cloud-terminal platform is developed to help intelligent robots evolve their intelligent models.As a proof of this idea,a welding robot’s welding parameter optimization intelligent model is implemented on the platform.