Multi-Objective Learning

Since many years, our research group is working on research about multi-objective learning. Multi-Objective Learning is about learning tasks which involve multiple conflicting objecticves. For instance imagine a group of agents (a swarm) who is supposed to move in a small area. Each agent gets a positive reward, if its passes through a very narrow gate, but gets a negative reward after each collision with others or obstacles (walls etc). One way of dealing with this problem is to perform an on-board learning. In an early work in 2009, we worked on finite state machines and evolutionary robotics and let the agents to learn the task in a collective way: 

  • Lukas König, Sanaz Mostaghim, Hartmut Schmeck
  • Decentralized Evolution of Robotic Behavior Using Finite State Machines
  • International Journal of Intelligent Computing and Cybernetics, 2, (4), pages 695-723, December, 2009

In 2019, we repeated the same work, but this time we used hidden Markov models and more features:

  • Dominik Fischer, Larissa Albantakis and Sanaz Mostaghim
  • How cognitive and environmental constraints influence the reliability of simulated animats in groups
  • PLoS ONE 15(2): e0228879, February 2020. Open access --> Link

The interesting result from all these studies, is that the collective is able to learn to solve the multi-objective task in a very efficient way than if we would use a multi-agent path planning. They evolve the so called wall-following behavior: 






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