Trust-based Performance Optimization for Human-Swarm Collaboration

Abstract

This work develops a trust inference model to address scenarios where agents in a swarm collaborate to achieve the coverage control task. To gather empirical data from human subjects for the probabilistic model development, we build various simulation tools and user interfaces. Using our visual training tool, we train a single-agent model and then extend that to create our multi-agent model. These models utilize a dynamic Bayesian network and produce stochastic predictions. We then apply these models to our Voronoi-based area coverage problem in real time, where agents adjust their behavior to maximize the team performance and hence human trust. As a result of this research, multi-agent teams will be able to increase their individual trust levels thereby enhancing team performance and efficiency.