Decentralised learning and networked communication in large populations of AI decision makers

大量人工智能决策者的分散学习和网络通信

• 批准号: 2577365
• 金额: --
• 依托单位: University of Oxford
• 依托单位国家: 英国
• 项目类别: Studentship
• 财政年份: 2021
• 资助国家: 英国
• 起止时间: 2021 至 无数据
• 项目状态: 未结题
• 来源: https://gtr.ukri.org/projects?ref=studentship-2577365
• 关键词: Decentralised; learning; networked; communication; large

Brief description of the context of the research including potential impactSystems of numerous interacting autonomous decision makers ('agents') are likely to play an increasing role in many areas of our society, economy and infrastructure, with potential benefits for disaster response, financial markets, smart cities and energy grids, environmental monitoring and other cyber-physical systems. However, there is an explosion in computational complexity as the population size increases, making them difficult to scale for real-world usage. 'Mean-Field Games' (MFGs) are an area of game theory related to statistical physics, which can be combined with machine learning to address the scalability issue. Nevertheless, methods for solving MFGs have traditionally relied on idealised assumptions that are unrealistic in practice. Aims and ObjectivesI wish to bridge the gap between the abstract theory of MFGs and their practical usage in real-world problems. In particular, I am introducing inter-agent communication into the framework, to remove the reliance of theoretical techniques on the existence of a single controller that `puppeteers' all the agents. This can bring benefits in terms of robustness, flexibility and speed of convergence. Novelty of the research methodologyMFGs remain a relatively underexplored area, especially with regards to the desiderata we may have for complex systems to be trained and deployed in the real world, such as decentralised learning. My introduction of inter-agent communication to the MFG framework is a novel contribution. Alignment to EPSRC's strategies and research areas (which EPSRC research area the project relates to) Further information on the areas can be found on http://www.epsrc.ac.uk/research/ourportfolio/researchareas/Research into large multi-agent systems is at the heart of several EPSRC research areas, including 'AI technologies', 'control engineering' and 'verification and correctness', with potential application to the likes of 'ICT networks and distributed systems' and 'infrastructure and urban systems'. Any companies or collaborators involved None currently.

对研究背景的简要说明，包括潜在影响众多相互作用的自主决策者系统(“代理人”)可能在我们的社会、经济和基础设施的许多领域发挥越来越大的作用，对灾害应对、金融市场、智能城市和能源电网、环境监测和其他网络物理系统具有潜在的好处。然而，随着种群规模的增加，计算复杂性呈爆炸式增长，这使得它们难以扩展以适应现实世界的使用。Mean-field Games(MFGS)是与统计物理相关的博弈论领域，它可以与机器学习相结合来解决可伸缩性问题。然而，解决MFG的方法传统上依赖于在实践中不切实际的理想化假设。目的和目的我希望在MFGs的抽象理论和它们在现实世界问题中的实际应用之间架起一座桥梁。特别是，我正在将代理之间的通信引入该框架，以消除理论技术对存在的单个控制器的依赖，该控制器是所有代理的“傀儡操纵者”。这可以在稳健性、灵活性和收敛速度方面带来好处。研究方法的新颖性MFGs仍然是一个相对未被探索的领域，特别是关于我们可能对复杂系统进行培训并在现实世界中部署的愿望，例如分散学习。我将代理间通信引入MFG框架是一个新的贡献。与EPSRC的战略和研究领域(项目涉及的EPSRC研究领域)保持一致，有关这些领域的更多信息可以在http://www.epsrc.ac.uk/research/ourportfolio/researchareas/Research上找到，大型多代理系统是EPSRC几个研究领域的核心，包括“人工智能技术”、“控制工程”和“验证和正确性”，潜在地应用于“ICT网络和分布式系统”和“基础设施和城市系统”等领域。目前没有任何公司或合作者参与其中。