Mental care systems require patient assessment and diagnosis. Thus, creating a reliable artificial intelligence that provides a mental state examination (MSE) requires proper verification that guarantees accuracy and robustness. Both aims are only reachable when the...
Magnetic Resonance (MR)-guided online adaptive radiotherapy has the potential to revolutionise cancer treatment. It exploits soft-tissue contrast of MR images obtained right before patient’s radiation treatment to personalise radiotherapy treatment plans....
Large language models have become the main backbone of most state-of-the-art NLP systems. By pre-training on very large datasets with unsupervised objectives, these models are able to learn good representations for language composition, then transfer these over to a...
Large language models have become the main backbone of most state-of-the-art NLP systems. By pre-training on very large datasets with unsupervised objectives, these models are able to learn good representations for language composition, then transfer these over to a...
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 reliability of machine learning is paramount...
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 driven mainly by neural networks, applications...
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 strategic reasoning has so far fallen short of...
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 penalizations [1].By extending DRL with formal...
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 these systems continue to grow and evolve,...
Task Planning (also known as Symbolic Planning or AI Planning) has proved to be a very useful technique to tackle the decision-making problem in robotics. Given a set of task goals, the planner can come up with a set of actions that will reach those goals once...
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) proposes a sound and complete formal system...
Reinforcement learning resembles human learning with intelligence accumulated through experiment. To attain expert human-level performance on tasks such as Atari video games or chess, deep RL systems have required many orders of magnitude more training data than human...
