2018, Volume 20, Issue 4
Strategic Study of CAE >> 2018, Volume 20, Issue 4 doi: 10.15302/J-SSCAE-2018.04.017
Theory of Collective Intelligence Evolution and Its Applications in Intelligent Robots
1. Beijing Deep Singularity Technology Co., Ltd., Beijing 100086, China;
2. Intelligent Robot Research Institute, Fudan University, Shanghai 200433, China
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Abstract
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.
Keywords
collective intelligence ; emergence ; evolution ; positive feedback ; ant colony optimization ; Monte-Carlo tree search ; distributed AI cloud-terminal platform ; intelligent robot
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