  Collective decision-making enables multi-robot systems to act autonomously in
real-world environments. Existing collective decision-making mechanisms suffer
from the so-called speed versus accuracy trade-off or rely on high complexity,
e.g., by including global communication. Recent work has shown that more
efficient collective decision-making mechanisms based on artificial neural
networks can be generated using methods from evolutionary computation. A major
drawback of these decision-making neural networks is their limited
interpretability. Analyzing evolved decision-making mechanisms can help us
improve the efficiency of hand-coded decision-making mechanisms while
maintaining a higher interpretability. In this paper, we analyze evolved
collective decision-making mechanisms in detail and hand-code two new
decision-making mechanisms based on the insights gained. In benchmark
experiments, we show that the newly implemented collective decision-making
mechanisms are more efficient than the state-of-the-art collective
decision-making mechanisms voter model and majority rule.
