Transparent Shepherding: A Rule-Based Learning Shepherd for Human Swarm Teaming

This chapter aims to demonstrate how rule-based Artificial Intelligence algorithms can address a few human swarm teaming challenges. We will start from the challenges identified by the cognitive engineering community for building human autonomy teaming and how they scale to human swarm teaming. The discussion will follow with a description of rule-based machine learning with a focus on learning classifier systems as a representative for these algorithms and their benefits for human swarm teaming. Shepherding affords a human to manage a swarm by teaming with an autonomous single shepherd. A learning classifier system is designed to learn behaviour needed to be exhibited by the shepherd. Results demonstrate the effectiveness of the rule-based XCS model to capture shepherding behaviour, where the XCS model achieves comparable performance to the standard Strömbom’s shepherding method as measured by the number of steps needed by a sheep-dog to guide group of sheep to target destination. These results are promising and demonstrate that learning classifier systems could design autonomous shepherds for new type of shepherding tasks and scenarios that we may not have rules for today.