Currently, literature regarding Multiagent Path Finding (MAPF) does not give a broad enough overview of all the different approaches. Many papers are hard to read and require proper knowledge of MAPF. The goal of this report is to give a global overview of MAPF. To achieve this goal, we provide a detailed explanation of what MAPF problems look like, as well as giving a clear overview of the strength and weaknesses of different solutions. Besides this theoretical analysis, we also analyse and critique benchmarking performed by other researchers. Following all this, we conclude that the field of MAPF lacks agreement on terminology. Furthermore, performance analysis is limited to researchers choice, skewing research in their own favour.

Most courses in the Computer Science Bachelor at the Delft University of Technology make use of lab sessions. During these lab sessions students can ask questions about course material and get feedback on their assignment. Moreover, their knowledge about assignments can be orally tested. In order to properly help the students, teaching assistants, or TAs, are selected to assist the lecturer during the lab sessions. With the number of students in the Bachelor quickly growing, the process of manually recruiting students to become a TA and assigning the TAs to lab sessions is becoming very time consuming and almost impossible. During a Bachelor End Project in 2018 four students (van Deursen et al., 2018) created the Teach- ing Assistant Management (TAM) platform. This project aimed to ease the process of recruiting and scheduling TAs. All parties involved in the process of appointing TAs can use TAM to provide their input. Lecturers can register their courses on TAM and students are able to indicate their interest and availability to help with different courses. However, the first version of TAM missed a number of important features. For example, student avail- ability data had to be extracted manually and teachers still had to email their TA selection to the coordinator. This project aims to continue and improve TAM with these missing features. In order to achieve this goal TAM 2.0 has been developed. TAM 2.0 consists of three components: a MySQL database, a back end written using Spring and Java containing the business logic, and a front end website created using Vue to provide an interface to its users. TAM 2.0 also integrated LabraCORE. LabraCORE provides user and course information to several platforms and stores it conveniently in one central place.