Abstract argumentation for explainable satellite scheduling

Tools

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 (https://doi.org/10.1109/DSAA54385.2022.10032348)

[thumbnail of Powell-Riccardi-DSAA2022-Abstract-argumentation-for-explainable-satellite-scheduling]
Preview
 Text. Filename: Powell_Riccardi_DSAA2022_Abstract_argumentation_for_explainable_satellite_scheduling.pdf
Accepted Author Manuscript
License: Strathprints license 1.0
Download (1MB)| Preview

Abstract

Satellite schedules are derived from satellite mission objectives, which are mostly managed manually from the ground. This increases the need to develop autonomous on-board schedul- ing capabilities and reduce the requirement for manual manage- ment of satellite schedules. Additionally, this allows the unlocking of more capabilities on-board for decision-making, leading to an optimal campaign. However, there remain trust issues in decisions made by Artificial Intelligence (AI) systems, especially in risk-averse environments, such as satellite operations. Thus, an explanation layer is required to assist operators in understanding decisions made, or planned, autonomously on-board. To this aim, a satellite scheduling problem is formulated, utilizing real world data, where the total number of actions are maximised based on the environmental constraints that limit observation and down-link capabilities. The formulated optimisation problem is solved with a Constraint Programming (CP) method. Later, the mathematical derivation for an Abstract Argumentation Framework (AAF) for the test case is provided. This is proposed as the solution to provide an explanation layer to the autonomous decision-making system. The effectiveness of the defined AAF layer is proven on the daily schedule of an Earth Observation (EO) mission, monitoring land surfaces, demonstrating greater capabilities and flexibility, for a human operator to inspect the machine provided solution.

ORCID iDs

Powell, Cheyenne and Riccardi, Annalisa ORCID logoORCID: https://orcid.org/0000-0001-5305-9450; Huang, Joshua Zhexue, Pan, Yi, Hammer, Barbara, Khan, Muhammad Khurram, Xie, Xing, Cui, Laizhong and He, Yulin
CORE (COnnecting REpositories)