Transparency's Influence on Human-Collective Interactions

TL;DR

This study investigates how different models and visualizations affect transparency and human-collective interactions in robotic systems, providing insights for designing more effective collective systems for various applications.

Contribution

It compares two models and visualizations to determine their impact on transparency and interaction quality, offering design guidance for remote supervision of robotic collectives.

Findings

Models and visualizations influence interactions differently

No single model-visualization combo offers optimal transparency

Design choices significantly affect human-collective collaboration

Abstract

Collective robotic systems are biologically inspired and advantageous due to their apparent global intelligence and emergent behaviors. Many applications can benefit from the incorporation of collectives, including environmental monitoring, disaster response missions, and infrastructure support. Transparency research has primarily focused on how the design of the models, visualizations, and control mechanisms influence human-collective interactions. Traditionally most evaluations have focused only on one particular system design element, evaluating its respective transparency. This manuscript analyzed two models and visualizations to understand how the system design elements impacted human-collective interactions, to quantify which model and visualization combination provided the best transparency, and provide design guidance, based on remote supervision of collectives. The consensus decision-making and baseline models, as well as an individual agent and abstract visualizations, were analyzed for sequential best-of-n decision-making tasks involving four collectives, composed of 200 entities each. Both models and visualizations provided transparency and influenced human-collective interactions differently. No single combination provided the best transparency.