We claim that most of the existing learning and optimization problems are multi-objective. In our research, we work on various such problems and develop multi-objective algorithms. One of the applications concerns multi-agent path finding: many robots moving in an environment (imagine autonomous driving cars) and there are many objectives which have to considered such as collision avoidance (no accidents), but efficiency in terms of traveling cost and time for all the involved agents. In one of our recent works, we study the problem:
- Sebastian Mai and Sanaz Mostaghim
- Modelling Pathfinding for Swarm Robotics
- In: Dorigo M. et al. (eds) Swarm Intelligence. ANTS 2020. Lecture Notes in Computer Science, vol 12421. Springer, Cham. 2020. https://doi.org/10.1007/978-3-030-60376-2_15
The very good accept about multi-objective learning is that we have several optimal solutions. The picture below shows two behaviors for each setting (various number of agents and various environments): best behavior in terms of length (bottom) and best behavior in terms of collisions (top).