Project ID: STAI-CDT-2022-KCL-10
Themes: AI Planning, Logic, Verification
Supervisor: Mohammad Abdulaziz
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...
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Project ID: STAI-CDT-2022-KCL-9
Themes: Reasoning, Verification
Supervisor: Mohammad Mousavi, Rita Borgo
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...
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Project ID: STAI-CDT-2022-IC-5
Themes: Verification
Supervisor: Nicolas Wu, Matthew Williams
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...
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Project ID: STAI-CDT-2022-KCL-6
Themes: Verification
Supervisor: Jan Oliver Ringert, Mohammad Mousavi
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...
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Project ID: STAI-CDT-2022-KCL-3
Themes: Logic, Norms, Verification
Supervisor: Hana Chockler
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...
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Project ID: STAI-CDT-2022-IC-3
Themes: Logic, Verification
Supervisor: Francesco Belardinelli
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...
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Project ID: STAI-CDT-2022-IC-4
Themes: Logic, Verification
Supervisor: Francesco Belardinelli
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...
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Project ID: STAI-CDT-2022-ICL-1
Themes: Logic, Verification
Supervisor: Prof Nobuko Yoshida
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...
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Project ID: STAI-CDT-2022-KCL-1
Themes: AI Provenance, Argumentation, Verification
Supervisor: Prof Elena Simperl
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,...
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Project ID: STAI-CDT-2021-IC-24
Themes: AI Planning, Logic, Verification
Supervisor: Francesco Belardinelli
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...
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Project ID: STAI-CDT-2021-IC-23
Themes: AI Planning, Logic, Verification
Supervisor: Francesco Belardinelli
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...
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Project ID: STAI-CDT-2021-IC-22
Themes: AI Planning, Logic, Verification
Supervisor: Francesco Belardinelli
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...
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