Human-AI Composite Systems

Research Context & Potential Impact
How can we design AI systems that are able to seamlessly coordinate with human users?
This is the central question motivating the research proposed in this document. Better answering this question will facilitate the development of AI systems that can effectively collaborate with humans to complete tasks, boosting the productivity gains of working with an AI system and empowering human users.

Aims and Objectives
By aiming to improve the overall performance of human-AI composite systems, one clear
objective is to develop algorithms for training agents that are capable of ad-hoc coordination, ideally across a reasonable range of settings.
Another aim is to develop AI systems that more closely resemble, and better operate within,
collective human culture. An associated objective is to explore how cultural evolution can be
leveraged to create open-ended, continual learning systems that are capable of generational
improvement and online adaptation to distribution shifts induced by other agents.

Novelty of Research Methodology
Methodologically, this research will focus on the use of Multi-Agent Reinforcement Learning (MARL) and Large Language Models (LLMs) in addressing these problems. MARL
algorithms have been used to train agents capable of ad-hoc coordination, though in limited
settings, and that excel in certain cooperative games. Meanwhile, by the nature of their
training LLMs effectively model human culture. Moreover, their understanding of language
makes them expert communicators and provides a mechanism for simplifying coordination
problems. Finally, their in-context learning abilities, along with their capacity for planning and
acting as agents, could solve key challenges associated with the aforementioned objectives.
The flexibility afforded by a comprehensive use of both MARL and LLMs in developing agents
that meet our objectives provides a lot of room for novel research. Additionally, where notable limitations arise in the context of applying these methods to relevant problems, we will
conduct analysis to illuminate how and why the underlying methods face these limitations.

Alignment to ESPRC's Strategies and Research Areas
This research is aligned to ESPRC's Artificial Intelligence Technologies theme.

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.