Individual differences in learning behavior within social groups, whether in humans, other animals, or among robots, can have significant effects on collective task performance. This is because it can affect individuals' response to the environment and their interactions with each other. In recent years there has been rising interest in the question of how individual differences, whether in learning or other traits, affect collective outcomes: studied, for example, in social insect foraging behavior. Multi-robot, 'swarm' systems have a heritage of bioinspiration from such examples, and here we consider whether heterogeneity in a learning behavior called latent inhibition (LI) may be useful for a team of patrolling robots tasked with environmental monitoring and anomaly detection. Individuals with high LI can be seen as better at learning to be inattentive to irrelevant or unrewarding stimuli, while low LI individuals might be seen as 'distractible' and yet, more positively, more exploratory. We introduce a simple model of the effects of LI as the probability of re-searching a location for a reward (anomalous reading) where it has previously been found to be unrewarding (irrelevant). In simulated patrols, we find that a negatively skewed distribution of mostly high LI robots, and just a single low LI robot, is collectively most effective at monitoring dynamic environments. These results are an example of 'functional heterogeneity' in 'swarm engineering' and could inform predictions for ecological distributions of learning traits within social groups.

翻译：社会群体中个体学习行为的差异——无论是人类、其他动物还是机器人群体——都会对集体任务性能产生显著影响。这是因为这种差异会影响个体对环境的响应方式及其相互间的交互作用。近年来，关于个体差异（无论是学习能力还是其他特质）如何影响集体行为结果的研究日益受到关注，例如社会性昆虫的觅食行为研究。多机器人"蜂群"系统正是从这类生物例证中汲取灵感。本研究探讨一种称为潜在抑制（LI）的学习行为异质性，是否能应用于执行环境监测与异常检测任务的多机器人巡逻团队。高LI个体更擅长学习忽视无关或无奖赏刺激，而低LI个体则可能表现出"分心"特质，但更具探索性。我们建立了一个简化的LI效应模型，将其定义为机器人重新搜索先前发现无奖励（无关）位置以获得奖励（异常读数）的概率。通过仿真巡逻实验发现，当群体由大部分高LI机器人和单个低LI机器人组成时（呈现负偏态分布），其对动态环境的集体监测效率最为显著。这些结果可作为"蜂群工程"中"功能异质性"的典型案例，并为社会群体内学习特质的生态分布预测提供理论依据。