多智能体机器人系统自发产生集体僵局，揭示了智能体间新交互机制。
Emergent interactions lead to collective frustration in robotic matter
- 用深度神经网络模拟粒子，让其根据环境自主预测行为
- 系统出现长期学习态转换、粒子物种分化及集体僵局现象
- 适合研究群体智能与自组织系统的学者参考
当前人工智能系统在孤立部署时已接近人类水平。当少量智能体协同工作时，可完成复杂任务。这引出一个问题：当大量学习型智能体构成机器人物质时，是否会涌现出集体行为？若存在，会呈现何种现象？本文研究了一个典型的机器人物质模型：一个一维随机多粒子系统，每个粒子配备深度神经网络，基于环境预测自身状态转移。结果发现，该系统表现出复杂涌现现象，包括长期学习态之间的转换、粒子物种的出现以及集体性僵局。此外，还观察到密度依赖的相变，具有临界性特征。通过活性物质理论分析表明，该相变源于由涌现的粒子间相互作用所驱动的自组织过程。该简单模型捕捉了更复杂机器人系统的关键特性。
原文摘要 · Abstract (English)
Current artificial intelligence systems show near-human-level capabilities when deployed in isolation. Systems of a few collaborating intelligent agents are being engineered to perform tasks collectively. This raises the question of whether robotic matter, where many learning and intelligent agents interact, shows emergence of collective behaviour. And if so, which kind of phenomena would such systems exhibit? Here, we study a paradigmatic model for robotic matter: a stochastic many-particle system in which each particle is endowed with a deep neural network that predicts its transitions based on the particles' environments. For a one-dimensional model, we show that robotic matter exhibits complex emergent phenomena, including transitions between long-lived learning regimes, the emergence of particle species, and frustration. We also find a density-dependent phase transition with signatures of criticality. Using active matter theory, we show that this phase transition is a consequence of self-organisation mediated by emergent inter-particle interactions. Our simple model captures key features of more complex forms of robotic systems.
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