Multi-Agent Systems model how different autonomous Agents, with limited knowledge, interact with each other in a shared Environment. A usual use case is to use Agents as a Team and give them a goal, which can only be achieved by multiple agents.
There are is a large number of Parameters that the operator of those teams needs to set in order for the team to interact successfully and reach its goal. Therefore, Methods, from the machine-learning domain, are used to automatically explore all possible parameters and find the most optimal ones.
This Survey will concentrate on Research where the Team Size is greater than two or three. At first, I present the history and background of Multi-Agent-Systems. After that, I give an overview of the of the Problems, which arise when large teams and machine-learning are used together. After that, I show existing algorithms, which can cope with those problems. Finally, I discuss what further research can be done in the area.