On the Cost of Evolving Task Specialization in Multi-Robot Systems
This addresses efficiency optimization for multi-robot swarms, but it is incremental as it builds on prior work by focusing on cost analysis.
The study tackled the problem of whether task specialization improves efficiency in multi-obot systems by analyzing costs in a foraging scenario, finding that evolved generalist behaviors outperformed task-specialist controllers, resulting in worse performance for specialists.
Task specialization can lead to simpler robot behaviors and higher efficiency in multi-robot systems. Previous works have shown the emergence of task specialization during evolutionary optimization, focusing on feasibility rather than costs. In this study, we take first steps toward a cost-benefit analysis of task specialization in robot swarms using a foraging scenario. We evolve artificial neural networks as generalist behaviors for the entire task and as task-specialist behaviors for subtasks within a limited evaluation budget. We show that generalist behaviors can be successfully optimized while the evolved task-specialist controllers fail to cooperate efficiently, resulting in worse performance than the generalists. Consequently, task specialization does not necessarily improve efficiency when optimization budget is limited.