Two Invariants of Human Swarm Interaction

Abstract

The search for invariants is a fundamental aim of scientific endeavors. These invariants, such as Newton's laws of motion, allow us to model and predict the behavior of systems across many different problems. In the nascent field of Human-Swarm Interaction (HSI), a systematic identification of fundamental invariants is still lacking. Discovering and formalizing these invariants will provide a foundation for developing, and better understanding, effective methods for HSI. We propose two invariants underlying HSI for geometric-based swarms: (1) collective state is the fundamental percept associated with a bio-inspired swarm, and (2) a human's ability to influence and understand the collective state of a swarm is determined by the balance between the span and persistence. We provide evidence of these invariants by synthesizing much of our previous work in the area of HSI with several new results, including a novel user study where users manage multiple swarms simultaneously. We also discuss how these invariants can be applied to enable more efficient and successful teaming between humans and bio-inspired collectives and identify several promising directions for future research into the invariants of HSI.

Swarm Transparency

“…Transparency of swarms is complicated in that there is no single point on which humans can focus in order to identify the swarm's state [3,15], which aligns with the biological perspective [10]. Thus, swarm transparency requires understanding the collective swarm state.…”

“…High information quality and quantity requires reliable and highbandwidth communication networks, which are generally infeasible with swarms. Thus, human-swarm interaction research has focused on "influencing" elements of the swarm [3,11]. Unreliable communications leads to two issues.…”

“…Models and predictive capabilities can remedy some transparency issues [3], but also have limitations. However, the emergent behaviors often improve the swarm's robustness in response to the environment or other disturbances or influences.…”

“…Recent research has focused on how humans can influence robotic swarms in order to achieve the human's desired outcomes [3,11]. Simultaneously, efforts exist to provide transparency into the capabilities and actions of artificial systems that also increase the human's trust and communication with the system [4].…”

Regulating Highly Automated Robot Ecologies

“…One particularly relevant application of shared control [6] that has parallels to regulating HARE is human-swarm interaction (HSI) [16,3]. In HSI, an operator commands or influences a set of robots that have been programmed to mimic biological swarms.…”