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

Essam Debie, Raul Fernandes Rojas, Justin Fidock, Michael Barlow, Kathryn Kasmarik, Sreenatha Anavatti, Matthew Garratt, Hussein A. Abbass

Research output: A Conference proceeding or a Chapter in BookChapterpeer-review


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.

Original languageEnglish
Title of host publicationShepherding UxVs for Human-Swarm Teaming
Subtitle of host publicationAn Artificial Intelligence Approach to Unmanned X Vehicles
EditorsHussein A Abbass, Robert A Hunjet
Place of PublicationNetherlands
Number of pages26
ISBN (Electronic)9783030608989
ISBN (Print)9783030608972
Publication statusPublished - 2021

Publication series

NameUnmanned System Technologies
ISSN (Print)2523-3734
ISSN (Electronic)2523-3742


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