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Learning and Measuring Specialization in Collaborative Swarm Systems

机译：协同群系统中的学习与测量专业

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摘要

This paper addresses qualitative and quantitative diversity and specialization issues in the framework of self-organizing, distributed, artificial systems. Both diversity and specialization are obtained via distributed learning from initially homogeneous swarms. While measuring diversity essentially quantifies differences among the individuals, assessing the degree of specialization implies correlation between the swarm's heterogeneity with its overall performance. Starting from the stick-pulling experiment in collective robotics, a task that requires the collaboration of two robots, we abstract and generalize in simulation the task constraints to k robots collaborating sequentially or in parallel. We investigate quantitatively the influence of task constraints and types of reinforcement signals on performance, diversity, and specialization in these collaborative experiments. Results show that, though diversity is not explicitly rewarded in our learning algorithm, even in scenarios without explicit communication among agents the swarm becomes specialized after learning. The degrees of both diversity and specialization are affected strongly by environmental conditions and task constraints. While the specialization measure reveals characteristics related to performance and learning in a clearer way than diversity does, the latter measure appears to be less sensitive to different noise conditions and learning parameters.
机译：本文在自组织，分布式，人工系统的框架内解决定性和定量多样性以及专业化问题。多样性和专业性都是通过从最初同质的群体中进行分布式学习而获得的。虽然衡量多样性实质上是量化个体之间的差异，但是评估专业化程度意味着群体的异质性与其总体绩效之间的相关性。从集体机器人技术中的拉杆实验开始，这项需要两个机器人协作的任务，我们在仿真中抽象和概括了任务约束，以依次或并行协作的k个机器人。在这些协作实验中，我们定量研究了任务约束和增强信号类型对性能，多样性和专业化的影响。结果表明，尽管在我们的学习算法中多样性并没有得到明显的奖励，但是即使在代理之间没有明确沟通的情况下，群体在学习后也会变得专门化。多样性和专业化程度都受到环境条件和任务限制的强烈影响。尽管专业化措施以比多样性更清晰的方式揭示了与性能和学习有关的特征，但后一种措施似乎对不同的噪声条件和学习参数不那么敏感。

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