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Powell, Cheyenne and Riccardi, Annalisa (2024) Explaining AI decisions in autonomous satellite scheduling via computational argumentation. In: SPAICE, 2024-09-17 - 2024-10-18, European Centre for Space Applications and Telecommunications (ECSAT), UK.
Powell, Cheyenne and Riccardi, Annalisa (2023) Question answering over knowledge graphs for explainable satellite scheduling. In: International Astronautical Congress, 2023-10-02 - 2023-10-06, Heydar Aliyev Center.
Powell, Cheyenne and Berquand, Audrey and Riccardi, Annalisa (2023) Natural language processing for explainable satellite scheduling. In: SPACEOPS 2023, 2023-03-06 - 2023-03-10.
Powell, Cheyenne and Riccardi, Annalisa; Huang, Joshua Zhexue and Pan, Yi and Hammer, Barbara and Khan, Muhammad Khurram and Xie, Xing and Cui, Laizhong and He, Yulin, eds. (2023) Abstract argumentation for explainable satellite scheduling. In: 2022 IEEE 9th International Conference on Data Science and Advanced Analytics (DSAA). IEEE, CHN, pp. 1-10. ISBN 9781665473309
Hall, Iain and Powell, Cheyenne and Riccardi, Annalisa (2022) Constraint programming for scheduling the operations of STRATHcube : a nanosatellite for detecting space debris. In: 73rd International Astronautical Congress (IAC), 2022-09-18 - 2022-10-22, Paris Convention Centre.
Powell, Cheyenne and Riccardi, Annalisa (2022) On-board re-planning of an earth observation satellite for maximisation of observation campaign goals. In: 73rd International Astronautical Congress (IAC), 2022-09-18 - 2022-10-22, Paris Convention Centre.
Powell, Cheyenne and Riccardi, Annalisa (2021) Towards explainability of on-board satellite scheduling for end user interactions. In: 72nd International Astronautical Congress, 2021-10-25 - 2021-10-29, Dubai World Trade Centre.
Marchetti, Francesco and Wilson, Callum and Powell, Cheyenne and Minisci, Edmondo and Riccardi, Annalisa; Romero, Juan and Martins, Tiago and RodrÃguez-Fernández, Nereida, eds. (2021) Convolutional generative adversarial network, via transfer learning, for traditional Scottish music generation. In: Artificial Intelligence in Music, Sound, Art and Design. Lecture Notes in Computer Science . Springer, ESP, pp. 187-202. ISBN 9783030729141