  In collective robotic systems, the automatic generation of controllers for
complex tasks is still a challenging problem. Open-ended evolution of complex
robot behaviors can be a possible solution whereby an intrinsic driver for
pattern formation and self-organization may prove to be important. We implement
such a driver in collective robot systems by evolving prediction networks as
world models in pair with action-selection networks. Fitness is given for good
predictions which causes a bias towards easily predictable environments and
behaviors in the form of emergent patterns, that is, environments of minimal
surprise. There is no task-dependent bias or any other explicit
predetermination for the different qualities of the emerging patterns. A
careful configuration of actions, sensor models, and the environment is
required to stimulate the emergence of complex behaviors. We study
self-assembly to increase the scenario's complexity for our minimal surprise
approach and, at the same time, limit the complexity of our simulations to a
grid world to manage the feasibility of this approach. We investigate the
impact of different swarm densities and the shape of the environment on the
emergent patterns. Furthermore, we study how evolution can be biased towards
the emergence of desired patterns. We analyze the resilience of the resulting
self-assembly behaviors by causing damages to the assembled pattern and observe
the self-organized reassembly of the structure. In summary, we evolved swarm
behaviors for resilient self-assembly and successfully engineered
self-organization in simulation. In future work, we plan to transfer our
approach to a swarm of real robots.
