Robotics are already being used in warehouses, factories, super markets, homes, hazardous sites and other applications. While many issues of stereotypes, disparate impact, and harmful impact of AI have been brought to the surface recently, it is not clear yet how...
Constructing a world-model is a fundamental part of model-based AI, e.g. planning. Usually, such a model is constructed by a human modeller and it should capture the modeller’s intuitive understanding of the world dynamics and how an intelligent agent should behave in...
The main objective of this project is to develop AI techniques to analyse the behaviour recorded in the past Stop and Search (S&S) operations. The AI system will be used to inform future operations, avoid unnecessary escalations that jeopardise the safety of the...
The rise of fake news and misinformation is a threat to our societies. Even though we are not always able to quantify the effect of misinformation, it is clear that it is polarising the society and often leads to violence and promotes racism.[1] Much of the fake news...
Deep learning has shown huge potential in terms of delivering AI with real-world impact. Most current projects are built in either PyTorch, Tensorflow, or similar platforms. These tend to be written in languages where the correct description of neural networks relies...
Trusted autonomous systems (TAS) rely on AI components that perform critical tasks for stakeholders that have to rely on the services provided by the system, e.g., self-driving cars or intelligent robotic systems. Two techniques that help the designers automatically...
The goal of this project is to develop a causality-based framework for the analysis of decentralised finance (DeFi), based on the principled approach of actual causality [1] and responsibility [2], the latter pioneered by Dr Chockler. The framework will assist in...
Reinforcement Learning, and its extension Deep Reinforcement Learning (DRL), are Machine Learning (ML) techniques that allow autonomous agents to learn optimal behaviours (called policies) in unknown and complex environments through models of rewards and...
Reinforcement Learning (RL) is a technique widely used to allow agents to learn behaviours based on a reward/punishment mechanism [1]. In combination with methods from deep learning, RL is currently applied in a number of different scenarios that have a significant...
Today, most computer applications are developed as ensembles of concurrent multi-agents (or components), that communicate via message passing across some network. Modern programming languages and toolkits provide developer-friendly concurrency abstractions for...
Knowledge graphs and knowledge bases are forms of symbolic knowledge representations used across AI applications. Both refer to a set of technologies that organise data for easier access, capture information about people, places, events, and other entities of...
The growing societal impact of AI-based systems has brought with it a set of risks and concerns [1, 2].Indeed, unintended and harmful behaviours may emerge from the application of machine learning (ML) algorithms, including negative side effects, reward hacking,...
