Learning from Evolution: Improving Collective Decision-Making Mechanisms using Insights from Evolutionary Robotics

• URL: http://arxiv.org/abs/2405.02133v1
• Date: Fri, 3 May 2024 14:37:17 GMT
• Title: Learning from Evolution: Improving Collective Decision-Making Mechanisms using Insights from Evolutionary Robotics
• Authors: Tanja Katharina Kaiser,
• Abstract summary: Collective decision-making enables multi-robot systems to act autonomously in real-world environments. Recent work has shown that more efficient collective decision-making mechanisms can be generated using methods from evolutionary computation. We analyze evolved collective decision-making mechanisms in detail and hand-code two new decision-making mechanisms based on the insights gained.
• Score: 2.1756081703276
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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