## 1 Introduction  
Driven by the need of more accurate, reliable, and frequent data to study earth science related issues, space engineering industry [1; 2; 3; 4] is facing an architectural change of paradigm. Distributed Satellite Systems (DSS) encompassing several interacting spacecraft are leading the way in applications where monolithic satellites have become obsolete in terms of risk, cost, or even performance [5]. One typical instance is distributed earth observation satellites (DEOS), which involves fleet of satellites to detect the Earth's surface and lower atmosphere to obtain information [6]. Though with more satisfied revisit times, coverage of larger areas and higher resolution, DEOS constellations are facing new challenges, the core of which is how to coordinate the mission plans for numbers of satellites that are generally heterogeneous [7]. During the past decades, researchers have carried out extensive research on centralized methodologies. Satellites mission planning are usually designed to be executed on the ground with the premise that all the observation requests and resource information are updated in a centralized way. Researchers use methods such as ant colony algorithm [8; 9; 10], simulated annealing algorithm [11; 12], genetic algorithm [13; 14; 15], tabu search algorithm [16; 17; 18], etc. to handle the scheduling problem. Although optimal solutions can be obtained under certain circumstances, it is not practical to apply the methods above directly in distributed satellite mission planning due to the following drawbacks. Firstly, computation and memory cost could be extremely expensive as the number of satellites and observation requests increase. Time consumption in one planning period is so high that it cannot satisfy the need for rapid response. Secondly, there is always a time delay between satellites and ground stations; when emergencies (resource malfunctioning, observation failure, etc.) occur onboard, it will take time for mission planning center (MPC) to be informed, which leads to missed observation opportunities. Moreover, these methods always have difficulties in handling situations where observation requests are generated online. Whenever changes occur, the planning process should be resumed completely, which may create conflict with the executed missions in progress and lead to waste of time and resources. Finally, this kind of architecture does not fit the trend of decentralized way of system management and control, and malfunction in the central point may lead to the whole system's failure. Consequently, new methods using a self-organized mechanism to make DSS work in a more resilient, flexible, and adaptable way are in urgent need, by which a system-level optimal can be reached when agents in the system cooperate with each other based on local and exchange information. To address the issue above, modeling DSS as multi-agent systems (MAS) is a common trend in many experimental and academic works. The European Space Agency [19] (ESA) has implemented multi-agent system (MAS) technologies in Earth Observation (EO) constellations. Unlike traditional heuristic methods that are well-suited and designed for a fixed number of missions and satellites, multi-agent methods allow for an adaptability and scalability that is not comparable [20]. Other similar work [21; 22; 23] shows how MAS-related applications can improve system performance and provide negotiation-based self-organizing mechanisms as means to enhance the autonomy of a system. We can find that, after implementing the MAS framework, the definitions of coordination rules or mechanisms can directly determine the quality of DSS. Luckily, game theory [24; 25; 26], which focuses on cooperation and conflict between rational players, can be perfectly utilized in this kind of problem, and we can see considerable applications of game theory, including unmanned aerial vehicle (UAV), sensor web, electricity management, job scheduling, etc.