The field of autonomous physical science - where machine learning guides and learns from experiments in a closed-loop - is rapidly growing in importance. Autonomous systems allow scientists to fail smarter, learn faster, and spend less resources in their studies. The field promises improved performance for various facilities such as labs, research and development pipelines, and warehouses. As autonomous systems grow in number, capability, and complexity, a new challenge arises - how will these systems work together across large facilities? We explore one solution to this question - a multi-agent framework. We demonstrate a framework with 1) a simulated facility with realistic resource limits such as equipment use limits, 2) machine learning agents with diverse learning capabilities and goals, control over lab instruments, and the ability to run research campaigns, and 3) a network over which these agents can share knowledge and work together to achieve individual or collective goals. The framework is dubbed the MULTI-agent auTonomous fAcilities - a Scalable frameworK aka MULTITASK. MULTITASK allows facility-wide simulations including agent-instrument and agent-agent interactions. Framework modularity allows real-world autonomous spaces to come on-line in phases, with simulated instruments gradually replaced by real-world instruments. Here we demonstrate the framework with a real-world materials science challenge of materials exploration and optimization in a simulated materials lab. We hope the framework opens new areas of research in agent-based facility control scenarios such as agent-to-agent markets and economies, management and decision-making structures, communication and data-sharing structures, and optimization strategies for agents and facilities including those based on game theory.
翻译:自主物理科学领域,即机器学习指南和从闭环实验中学习的机体科学领域,其重要性正在迅速增长;自主系统使科学家能够更聪明、更快地学习,在研究中花费较少的资源。这个领域有望改善实验室、研发管道和仓库等各种设施的业绩。随着自主系统的数量、能力和复杂性的提高,出现了新的挑战:这些系统如何在大型设施之间共同运作?我们探索了这一问题的一个解决办法——一个多试管框架。我们展示了一个框架:1)一个模拟共享设施,其资源限制现实,如设备使用限度;2)具有不同学习能力和目标的机体学习代理人,对实验室仪器的控制,以及开展研究运动的能力;3)一个这些代理人能够分享知识和共同努力实现个人或集体目标的网络。随着自主系统在数量、能力和复杂性方面不断增长,这个框架被称作:这些系统将如何在大型设施之间发挥作用?我们探索一个可升级的架式框架,一个多试管框架;一个多试管框架。我们展示了一个框架1)一个模拟的模拟共享设施模拟模型,包括代理研究和代理-代理机构互动互动关系;以及一个在现实世界中,一个虚拟工具工具,一个我们以模拟工具取代了这些空间空间空间,一个真正的工具,这些工具将展示了真正的工具。