Project ID: STAI-CDT-2023-KCL-30
Themes: Logic, Verification
Supervisor: Dr Hector Menendez Benito, Dr Karine Even Mendoza
The issue of machine learning trust is a pressing concern that has brought together multiple communities to tackle it. With the increasing use of tools such as ChatGPT and the identification of fairness issues, ensuring the...
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Project ID: STAI-CDT-2023-IC-7
Themes: Verification
Supervisor: Prof Alessio Lomuscio
Advances in machine learning have enabled the development of numerousapplications requiring the automation of tasks, such as computer vision, that were previously thought impossible to tackle. Although the success was...
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Project ID: STAI-CDT-2023-IC-6
Themes: Logic, Verification
Supervisor: Dr Francesco Belardinelli
In many games the outcome of the players’ actions is given stochastically rather than deterministically, e.g., in card games, board games with dice (Risk!), etc.However, the literature of logic-based languages for...
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Project ID: STAI-CDT-2023-IC-5
Themes: Logic, Verification
Supervisor: Dr Francesco Belardinelli
Deep Reinforcement Learning (DRL) has proved to be a powerful technique that allows autonomous agents to learn optimal behaviours (aka policies) in unknown and complex environments through models of rewards and...
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Project ID: STAI-CDT-2023-KCL-29
Themes: AI Planning, Verification
Supervisor: Dr Stefanos Leonardos, Dr. William Knottenbelt (Imperial College London)
Background: With recent technological advancements, multi-agent interactions have become increasingly complex, ranging from deep learning models and powerful neural networks to blockchain-based cryptoeconomies. However, as...
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Project ID: STAI-CDT-2023-KCL-26
Themes: Logic, Verification
Supervisor: David Watson
Causal reasoning is essential to decision-making in real-world problems. However, observational data is rarely sufficient to infer causal relationships or estimate treatment effects due to confounding signals. Pearl (2009)...
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Project ID: STAI-CDT-2023-KCL-28
Themes: Multi-agent systems, Verification
Supervisor: Nicola Paoletti
The field of neuro-symbolic systems is an exciting area of research that combines the power of machine learning with the rigour of symbolic reasoning. Neural systems have shown great promise in a wide range of applications,...
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Project ID: STAI-CDT-2023-KCL-19
Themes: Logic, Verification
Supervisor: Mohammad Abdulaziz
Matching is a fundamental problem in combinatorial optimisation with multiple applications in AI, like in belief propagation [10], multi-agent resource allocation algorithms [6], and constraint solving [16], and in...
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Project ID: STAI-CDT-2023-IC-2
Themes: Verification
Supervisor: Tolga Birdal
As part of the model-based approaches to safe and trusted AI, this project aims to shed light on the phenomenon of robust generalisation as a trade-off in geometric deep networks. Unfortunately, classical learning theory...
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Project ID: STAI-CDT-2023-KCL-24
Themes: Logic, Verification
Supervisor: Hana Chockler
We developed a framework for causal explanations of image classifiers based on the principled approach of actual causality [1] and responsibility [2], the latter pioneered by Dr Chockler. Our framework already resulted in a...
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Project ID: STAI-CDT-2023-KCL-23
Themes: Logic, Verification
Supervisor: Hana Chockler
We developed a framework for causal explanations of image classifiers based on the principled approach of actual causality [1] and responsibility [2], the latter pioneered by Dr Chockler. Our framework already resulted in a...
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Project ID: STAI-CDT-2023-KCL-20
Themes: AI Planning, Logic, Verification
Supervisor: David Watson
The current state of the art in generative modelling is dominated by neural networks. Despite their impressive performance on many benchmark tasks, these algorithms do not provide tractable inference for common and...
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