Practical Applications of Multiagent Shepherding for Human-Machine Interaction

Abstract

The shepherding problem is interesting for multiagent systems research as it requires multiple actors (e.g., dogs, humans) to exert indirect control over autonomous agents (e.g., sheep, cattle) for containment or transportation. Accordingly, plenty of research has focused on designing algorithms for robotic agents to solve such tasks. Almost no research, however, has utilized this task to investigate human-human or human-machine interactions, even though the shepherding problem encapsulates desirable qualities for an experimental paradigm to investigate the dynamics of human group and mixed-group coordination in complex tasks. This paper summarizes our recent research that has employed the shepherding problem to study complex multiagent human-human and human-machine interaction. The paper concludes with a discussion of practical applications for using the shepherding problem for the design of assistive agents that can be incorporated into human groups or enhance training and human learning.