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

Lead Research Organisation: University of Oxford


Brief description of the context of the research including potential impact

Systems 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 Objectives

I 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 methodology

MFGs 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

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.

Planned Impact

AIMS's impact will be felt across domains of acute need within the UK. We expect AIMS to benefit: UK economic performance, through start-up creation; existing UK firms, both through research and addressing skills needs; UK health, by contributing to cancer research, and quality of life, through the delivery of autonomous vehicles; UK public understanding of and policy related to the transformational societal change engendered by autonomous systems.

Autonomous systems are acknowledged by essentially all stakeholders as important to the future UK economy. PwC claim that there is a £232 billion opportunity offered by AI to the UK economy by 2030 (10% of GDP). AIMS has an excellent track record of leadership in spinout creation, and will continue to foster the commercial projects of its students, through the provision of training in IP, licensing and entrepreneurship. With the help of Oxford Science Innovation (investment fund) and Oxford University Innovation (technology transfer office), student projects will be evaluated for commercial potential.

AIMS will also concretely contribute to UK economic competitiveness by meeting the UK's needs for experts in autonomous systems. To meet this need, AIMS will train cohorts with advanced skills that span the breadth of AI, machine learning, robotics, verification and sensor systems. The relevance of the training to the needs of industry will be ensured by the industrial partnerships at the heart of AIMS. These partnerships will also ensure that AIMS will produce research that directly targets UK industrial needs. Our partners span a wide range of UK sectors, including energy, transport, infrastructure, factory automation, finance, health, space and other extreme environments.

The autonomous systems that AIMS will enable also offer the prospect of epochal change in the UK's quality of life and health. As put by former Digital Secretary Matt Hancock, "whether it's improving travel, making banking easier or helping people live longer, AI is already revolutionising our economy and our society." AIMS will help to realise this potential through its delivery of trained experts and targeted research. In particular, two of the four Grand Challenge missions in the UK Industrial Strategy highlight the positive societal impact underpinned by autonomous systems. The "Artificial Intelligence and data" challenge has as its mission to "Use data, Artificial Intelligence and innovation to transform the prevention, early diagnosis and treatment of chronic diseases by 2030". To this mission, AIMS will contribute the outputs of its research pillar on cancer research. The "Future of mobility" challenge highlights the importance the autonomous vehicles will have in making transport "safer, cleaner and better connected." To this challenge, AIMS offers the world-leading research of its robotic systems research pillar.

AIMS will further promote the positive realisation of autonomous technologies through direct influence on policy. The world-leading academics amongst AIMS's supervisory pool are well-connected to policy formation e.g. Prof Osborne serving as a Commissioner on the Independent Commission on the Future of Work. Further, Dr Dan Mawson, Head of the Economy Unit; Economy and Strategic Analysis Team at BEIS will serve as an advisor to AIMS, ensuring bidirectional influence between policy objectives and AIMS research and training.

Broad understanding of autonomous systems is crucial in making a society robust to the transformations they will engender. AIMS will foster such understanding through its provision of opportunities for AIMS students to directly engage with the public. Given the broad societal importance of getting autonomous systems right, AIMS will deliver core training on the ethical, governance, economic and societal implications of autonomous systems.


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Studentship Projects

Project Reference Relationship Related To Start End Student Name
EP/S024050/1 30/09/2019 30/03/2028
2577365 Studentship EP/S024050/1 30/09/2021 29/09/2025 Patrick Benjamin