In this presentation, we discuss the use of Agent and Multi-agent techniques in Space systems. We first identify some AI research challenges related to satellite constellations, especially concerning Earth Observation applications. These challenges range from constellation design to on-board in-space decision, and raise opportunities for investigation efforts in Multi-agent based Simulation, to Distributed Problem Solving, by the way of Machine Learning and Game Theory. We then focus on case studies related to constellation resource allocation and scheduling. The first case study concerns the allocation of exclusive orbit slots to privileged constellation users. In this problem, the constellation operator aims at allocating the resources (orbit slots) as optimally and fairly as possible, prior to any scheduling, only using some simple requirements from clients. This problem is long-term, over horizons of several months. We explore here the use of utilitarian and Leximin-optimal techniques. The second case study investigates how distributed and coordinated decision techniques can be used as to schedule observation tasks over such exclusive orbit portions, so that exclusive users do not disclose their own agenda. This problem is short-term, over horizons of few hours. Here, we make use of distributed constraint optimization and sequential auctions to distribute decisions over the set of exclusive users.

Gauthier Picard received a Ph.D. in Computer Science from the University of Toulouse in 2004, and the Habilitation degree in Computer Science from the University of Saint-Etienne in 2014. He was Associate Professor and then Full professor in Computer Science at MINES Saint-Etienne, before reaching a Senior Researcher position at ONERA, The French Aerospace Lab. His research focuses on cooperation and adaptation in multi-agent systems and distributed optimization with applications to aircraft design, ambient intelligence, intelligent transport and space operations.