A Comparison of Dynamical Perceptual-Motor Primitives and Deep Reinforcement Learning for Human-Artificial Agent Training Systems

Abstract

Effective team performance often requires that individuals engage in team training exercises. However, organizing team-training scenarios presents economic and logistical challenges and can be prone to trainer bias and fatigue. Accordingly, a growing body of research is investigating the effectiveness of employing artificial agents (AAs) as synthetic teammates in team training simulations, and, relatedly, how to best develop AAs capable of robust, human-like behavioral interaction. Motivated by these challenges, the current study examined whether task dynamical models of expert human herding behavior could be embedded in the control architecture of AAs to train novice actors to perform a complex multiagent herding task. Training outcomes were compared to human-expert trainers, novice baseline performance, and AAs developed using deep reinforcement learning (DRL). Participants’ subjective preferences for the AAs developed using DRL or dynamical models of human performance were also investigated. The results revealed that AAs controlled by dynamical models of human expert performance could train novice actors at levels equivalent to expert human trainers and were also preferred over AAs developed using DRL. The implications for the development of AAs for robust human-AA interaction and training are discussed, including the potential benefits of employing hybrid Dynamical-DRL techniques for AA development.

Conversation dynamics in a multiplayer video game with knowledge asymmetry

“…The overall aim of the larger study is to model the actions and communicative behaviors of teams engaged in complex and dynamically evolving task contexts for the development of human-inspired artificial agents which can be embedded within such teams. A potential application of this work is to reduce the demands associated with team training exercises by reducing the number of personnel needed ( Rigoli et al, 2022 ). In order to achieve this aim, we first must understand the contextual factors, behavioural strategies and communication dynamics which impact team performance and explains expertise.…”

Special Issue on Human-AI Teaming and Special Issue on AI in Healthcare

“…Although many human-AI teaming concepts are formed around the ability of the two to work together on tasks, Rigoli et al (2022) explore the use of AI agents as trainers. They showed that AI agents guided by models of expert performance could train novices as well as human trainers.…”