Available Projects

Projects on Agentic System Implementation (theme)

Supervisors: Christian Cabrera Jojoa, Neil D. Lawrence

Prerequisite: L172 Information, Energy and Intelligence (IEI), or equivalent preparation in information theory, maximum entropy, and information geometry.

The research group is interested in how we engineer multi-agent systems for reliability and transparancy. We have developed the DOAgent framework for delivering such multi-agent systems. This list of Part III / MPhil projects apply information-topographic ideas to engineered multi-agent and organisational systems using DOAgent. The projects listed serve as ideas, we will run a maximum of two or three projects in this space.

Projects on Data-Oriented Multi-Agent Systems (theme)

Supervisors: Christian Cabrera Jojoa, Neil D. Lawrence

Data Oriented Architecture (DOA) is a software architecture pattern that creates data-driven, loosely coupled, decentralised, and open systems. DOA achieves these goals by exposing systems’ data as a first-class citizen to distributed, stateless, and asynchronous components. These design decisions enable DOA-based systems to achieve data availability, reusability, and monitoring, as well as adaptability, scalability, and autonomy. DOAgent is a Python library for building multi-agent systems where shared data is the primary interface between decentralised agents that cooperate in open environments. It follows three principles: data as a first-class citizen, decentralisation, and openness. These Part II, Part III, and MPhil projects use that library. Related group programme: Data-Oriented Architectures for AI-based Systems.

Projects on Information Topography (theme)

Supervisor: Neil D. Lawrence

Prerequisite: L172 Information, Energy and Intelligence (IEI), or equivalent preparation in information theory, maximum entropy, and information geometry.

Information topography is the geometry of how information flows through a complex system. These Part III / MPhil projects develop the mathematical foundations of that geometry — from Fisher conductance and open inaccessible games to geometries of agency and good-regulator conditions — with L172 IEI as the shared prerequisite. Related group programme: Information Topography.

Homeostatic Regulators in Multi-Agent Environments

Supervisors: Joery de Vries, Neil D. Lawrence

Prerequisite: L172 Information, Energy and Intelligence (IEI), or equivalent preparation in information theory, maximum entropy, and information geometry.

Conant and Ashby’s good regulator theorem concerns a regulator that holds an outcome steady against a disturbance. Its success criterion is the entropy of the outcome, which is different from the classical notion of reward in reinforcement learning: it is a concave objective over the occupancy polytope, so an optimal single regulator is deterministic. This project asks what happens when the disturbance is another regulator. Several agents share an environment and each minimises the entropy of its own outcome under its own reference measure. From any agent’s viewpoint the other agents are structured, adaptive disturbances. Refinements of the theorem, notably Wentworth’s, say the regulator must carry a posterior over its disturbance, thus the notion of “model” that Conant and Ashby’s theorem implies is a posterior over the other agents’ policies. We will try to answer whether this posterior is necessary, and whether the joint problem is Nash. The working hypothesis is that competing regulators partition the state space into per-agent stable niches, which remains to be verified experimentally.

Interpretable Machine Learning for Intensive Care Decision Support

Supervisors: Christian Cabrera Jojoa, Neil D. Lawrence

Intensive care units (ICUs) generate vast volumes of patient data, yet clinicians often lack tools to translate this data into timely, trustworthy decisions. The aICU project, which aims to support safe, interpretable, and clinically meaningful decision-making by establishing a standardised pipeline for developing, evaluating, and deploying AI in critical care. This project proposes to reproduce existing machine learning models that address specific ICU problems (e.g., mortality prediction, sepsis detection, ventilator weaning, or length-of-stay estimation) and then investigate interpretability methods to make the model predictions understandable to clinicians.

Multi-objective optimisation of cloud infrastructure

Supervisors: Andrei Paleyes, Neil D. Lawrence

Machine learning (ML) and optimisation techniques are increasingly used to help solve decision-making problems that would be difficult or time-consuming to address manually. One such problem is the configuration of cloud infrastructure, where many deployment parameters can affect several competing objectives at the same time. This project investigates the use of multi-objective optimisation to automatically explore different cloud infrastructure configurations defined through Infrastructure-as-Code templates. Our aim will be to build a fully automated system that identifies a range of Pareto-optimal configurations that represent different trade-offs between the objectives being considered. Such a system can help reduce the time and cost required to create efficient cloud deployments while providing a better understanding of the available configuration choices.

What Does a Good Regulator Need to Know?

Supervisors: Joery de Vries, Neil D. Lawrence

Prerequisite: L172 Information, Energy and Intelligence (IEI), or equivalent preparation in information theory, maximum entropy, and information geometry.

Conant and Ashby’s (1970) good regulator theorem says a successful regulator must be a model of its system. Which model depends on what the regulator observes: Wentworth’s (2021) “gooder regulator” for instance requires the belief state. Since a good regulator objective minimises the entropy of a regulated outcome this adds a secondary dependence during learning due to concavity of the optimization problem. Similar to convex RL, it can be solved by a sequence of linear rewards built from the occupancy of the outcome features. Although the agent converges to a single deterministic policy, during learning its representation must support every reward in the sequence. Therefore, reusing what it learned under earlier rewards while staying focused on what the objective makes relevant is crucial. For instance, the successor features of the outcome suffice for this. Despite much work on state abstraction, self-predictive representations and sensorimotor world models, it is unclear what a good regulator needs to represent while it learns. This project investigates what acting and learning require for good regulators in the language of state abstractions of Li, Walsh and Littman (2006) and of Ni et al. (2024), and what combination of latent world-model loss delivers all aspects.

Projects on Data-Oriented Multi-Agent Systems (theme)

Supervisors: Christian Cabrera Jojoa, Neil D. Lawrence

Data Oriented Architecture (DOA) is a software architecture pattern that creates data-driven, loosely coupled, decentralised, and open systems. DOA achieves these goals by exposing systems’ data as a first-class citizen to distributed, stateless, and asynchronous components. These design decisions enable DOA-based systems to achieve data availability, reusability, and monitoring, as well as adaptability, scalability, and autonomy. DOAgent is a Python library for building multi-agent systems where shared data is the primary interface between decentralised agents that cooperate in open environments. It follows three principles: data as a first-class citizen, decentralisation, and openness. These Part II, Part III, and MPhil projects use that library. Related group programme: Data-Oriented Architectures for AI-based Systems.

5asideCHESS Engine and Tablebase

Supervisor: Radzim Sendyka

The idea of this project is to build an Engine and Tablebase for a Cambridge-based smaller variant of the classic game. This project would be carried out in contact with Ross Smith from 5asideCHESS, an organisation focused on improving social connections. Offered to motivated students passionate about machine learning and chess.

Interpretable Machine Learning for Intensive Care Decision Support

Supervisors: Christian Cabrera Jojoa, Neil D. Lawrence

Intensive care units (ICUs) generate vast volumes of patient data, yet clinicians often lack tools to translate this data into timely, trustworthy decisions. The aICU project, which aims to support safe, interpretable, and clinically meaningful decision-making by establishing a standardised pipeline for developing, evaluating, and deploying AI in critical care. This project proposes to reproduce existing machine learning models that address specific ICU problems (e.g., mortality prediction, sepsis detection, ventilator weaning, or length-of-stay estimation) and then investigate interpretability methods to make the model predictions understandable to clinicians.