Shaping Multi-Robot Patrol Performance with Heterogeneity in Individual Learning Behavior
<|reference_start|>Shaping Multi-Robot Patrol Performance with Heterogeneity in Individual Learning Behavior: 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.<|reference_end|>
@article{york2024shaping,
    title={Shaping Multi-Robot Patrol Performance with Heterogeneity in Individual
  Learning Behavior},
    author={Connor York, Zachary R Madin, Paul O'Dowd, Edmund R Hunt},
    journal={2024 IEEE International Conference on Development and Learning
  (ICDL)},
    year={2024},
    doi={10.1109/ICDL61372.2024.10645013},
    archivePrefix={arXiv},
    eprint={2403.01181},
    primaryClass={cs.RO}
}