Human-AI Composite Systems

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Research Context & Potential ImpactHow 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 ObjectivesBy aiming to improve the overall performance of human-AI composite systems, one clearobjective 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 beleveraged to create open-ended, continual learning systems that are capable of generationalimprovement and online adaptation to distribution shifts induced by other agents.Novelty of Research MethodologyMethodologically, this research will focus on the use of Multi-Agent Reinforcement Learning (MARL) and Large Language Models (LLMs) in addressing these problems. MARLalgorithms have been used to train agents capable of ad-hoc coordination, though in limitedsettings, and that excel in certain cooperative games. Meanwhile, by the nature of theirtraining LLMs effectively model human culture. Moreover, their understanding of languagemakes them expert communicators and provides a mechanism for simplifying coordinationproblems. Finally, their in-context learning abilities, along with their capacity for planning andacting 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 agentsthat 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 willconduct analysis to illuminate how and why the underlying methods face these limitations.Alignment to ESPRC's Strategies and Research AreasThis research is aligned to ESPRC's Artificial Intelligence Technologies theme.