Outputs (4)

Event-driven Temporal Models for Explanations - ETeMoX: Explaining Reinforcement Learning (2021)
Journal Article
Parra-Ullauri, J. M., Garcoa-Dominguez, A., Bencomo, N., Zheng, C., Zhen, C., Boubeta-Puig, J., …Yang, S. (2022). Event-driven Temporal Models for Explanations - ETeMoX: Explaining Reinforcement Learning. Software and Systems Modeling, 21, 1091-1113. https://doi.org/10.1007/s10270-021-00952-4

Modern software systems are increasingly expected to show higher degrees of autonomy and self-management to cope with uncertain and diverse situations. As a consequence, autonomous systems can exhibit unexpected and surprising behaviours. This is... Read More about Event-driven Temporal Models for Explanations - ETeMoX: Explaining Reinforcement Learning.

A Machine Learning Based Quantitative Data Analysis for Screening Skin Diseases Based on Optical Coherence Tomography Angiography (OCTA) (2021)
Conference Proceeding
Ji, Y., Yang, S., Zhou, K., Li, C., & Huang, Z. (2021). A Machine Learning Based Quantitative Data Analysis for Screening Skin Diseases Based on Optical Coherence Tomography Angiography (OCTA). In 2021 IEEE International Ultrasonics Symposium (IUS). https://doi.org/10.1109/IUS52206.2021.9593642

Lack of accurate and standard quantitative evaluations limit the progress of applying the OCTA technique into skin clinical trials. More systematic research is required to investigate the possibility of using quantitative OCTA techniques for screenin... Read More about A Machine Learning Based Quantitative Data Analysis for Screening Skin Diseases Based on Optical Coherence Tomography Angiography (OCTA).

Reward-Reinforced Generative Adversarial Networks for Multi-agent Systems (2021)
Journal Article
Zheng, C., Yang, S., Parra-Ullauri, J., Garcia-Dominguez, A., & Bencomo, N. (2022). Reward-Reinforced Generative Adversarial Networks for Multi-agent Systems. IEEE Transactions on Emerging Topics in Computational Intelligence, 6(3), 479 - 488. https://doi.org/10.1109/TETCI.2021.3082204

Multi-agent systems deliver highly resilient and adaptable solutions for common problems in telecommunications, aerospace, and industrial robotics. However, achieving an optimal global goal remains a persistent obstacle for collaborative multi-agent... Read More about Reward-Reinforced Generative Adversarial Networks for Multi-agent Systems.

The use of artificial intelligence and robotics in regional anaesthesia (2021)
Journal Article
McKendrick, M., Yang, S., & McLeod, G. A. (2021). The use of artificial intelligence and robotics in regional anaesthesia. Anaesthesia, 76(S1), 171-181. https://doi.org/10.1111/anae.15274

The current fourth industrial revolution is a distinct technological era characterised by the blurring of physics, computing and biology. The driver of change is data, powered by artificial intelligence. The UK National Health Service Topol Report em... Read More about The use of artificial intelligence and robotics in regional anaesthesia.