We present SpaceQA, to the best of our knowledge the first open-domain QA system in Space mission design. SpaceQA is part of an initiative by the European Space Agency (ESA) to facilitate the access, sharing and reuse of information about Space mission design within the agency and with the public. We adopt a state-of-the-art architecture consisting of a dense retriever and a neural reader and opt for an approach based on transfer learning rather than fine-tuning due to the lack of domain-specific annotated data. Our evaluation on a test set produced by ESA is largely consistent with the results originally reported by the evaluated retrievers and confirms the need of fine tuning for reading comprehension. As of writing this paper, ESA is piloting SpaceQA internally.

In the last decade, Dynamic Optimization Problems (DOP) have received increasing attention. Changes in the problem structure pose a great challenge for the optimization techniques. The Ant Colony Optimization (ACO) metaheuristic has a number of potentials in this field due to its adaptability and flexibility. However their design and analysis are still critical issues. This is where research on formal methods can increase the reliability of these systems and improve the understanding of their dynamics in complex problems such as DOPs. This paper presents a novel ACO algorithm based on an analytical model describing the long-terms behaviours of the ACO systems in problems represented as binary chains, a type of DOP. These behaviours are described using modelling techniques already developed for studying dynamical systems. The algorithm developed takes advantage of new insights offered by this model to regulate the tradeoff of exploration/exploitation resulting in a ACO system able to adapt its long-term behaviours to the problem changes and to improve its performance due to the experiences learnt from the previous explorations. An empirical evaluation is used to validate the algorithm capabilities of adaptability and optimization.

This paper describes the algorithms used in a planning and scheduling software tool developed for the European Space Agency in the framework of the Mars Express mission. The planning and scheduling algorithm computes a feasible schedule for the transmission of telecommands (TCs) from the ground segment to the space segment, complying with a number of technical constraints. Owing to the distance between Mars and Earth, it is important that the robustness of the schedule is taken into account because repair operations may be very time consuming or even impossible. For this reason, besides the maximization of the number of TCs transmitted from Earth to Mars, the scheduler is also designed to maximize the number of full confirmations and secondary time windows, which are two special characteristics of the Mars Express schedule explicitly designed for the sake of robustness. Besides the maximization of robustness, the scheduling algorithm that can run with different settings can be used to optimize some secondary figures of merit, such as the average saturation of the memory devices of the space segment and the usage of the time windows available for communication. Computational results on real instances are presented.

This paper describes RAXEM, an AI-based system developed to support human mission planners in the daily task to plan uplink commands for an interplanetary spacecraft. The intelligent environment of RAXEM has been designed to support the users in analyzing the problem and taking planning decisions as a result of an interactive process. The system combines different ingredients like integrating flexible automated algorithms, promoting user active participation during problem solving, and guaranteeing continuity of work practice. The paper touches upon all these aspects and comments on how a key factor for success has been the integration of intelligent technology to continuously support mission plan management.