Diversity in multi-agent systems for successful real-world deployments

多代理系统的多样性可实现成功的实际部署

• 批准号: 561116-2020
• 负责人: Taylor, Matthew
• 金额: $ 3.64万
• 依托单位: University of Alberta
• 依托单位国家: 加拿大
• 项目类别: Alliance Grants
• 财政年份: 2020
• 资助国家: 加拿大
• 起止时间: 2020-01-01 至 2021-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=707353
• 关键词: Diversity; multi; agent; systems; successful

Machine learning, a data-driven type of artificial intelligence, has revolutionized multiple industries. Not only does this allow for higher performance but it can also enable previously unrealizable opportunities. Unfortunately, learning can be impractically slow, particularly when data is expensive or slow to gather. For example, rather than letting a robot learn for days in the real world, which costs both time and money, why not first learn in simulation and then transfer to the real world? Unfortunately, because simulations are never perfect, some type of adaptation and simulation change strategies are needed, typically called Sim2Real. While common in single-agent settings, Multi-agent Sim2Real (MAS2R), has not been studied, even though it is a critical component to many real-world potential breakthroughs. This project will extend and test MAS2R, considering not only when situations change but also when different types of actors are in the environment. MAS2R will be applied to high-fidelity simulations in: Autonomous Driving Smart Grid Smart Buildings Commercialization options will be explored, with one or more paths forward towards commercialization decided on for next steps to help Alberta companies, post-award.

机器学习是一种数据驱动的人工智能，已经彻底改变了多个行业。这不仅可以实现更高的性能，而且还可以实现以前无法实现的机会。不幸的是，学习可能会慢得不切实际，特别是当数据昂贵或收集缓慢时。例如，与其让机器人在真实的世界中学习好几天，这既费时又费钱，为什么不先在模拟中学习，然后再转移到真实的世界呢？不幸的是，由于仿真永远不会完美，因此需要某种类型的适应和仿真更改策略，通常称为Sim2Real。虽然在单智能体环境中很常见，但多智能体Sim2Real（MAS2R）尚未被研究，尽管它是许多现实世界潜在突破的关键组成部分。该项目将扩展和测试MAS2R，不仅考虑情况变化，还考虑环境中不同类型的参与者。 MAS2R将应用于以下领域的高保真模拟： 自动驾驶 智能电网 智能建筑 将探讨商业化的选择，并决定下一步的商业化道路，以帮助阿尔伯塔公司，后奖。