Patterns of Effective Human-Agent Teams

Abstract

Intelligent systems are increasingly interacting with people, both in their daily lives and through their use in safety critical systems. Current research is focused on how to use intelligent systems in a collaborative way as a teammate, rather than a tool. This requires a better understanding of what behaviors enable effective human-agent teams. This paper reports an experiment where a human player collaborates with an agent to perform a maneuvering task while concurrently performing a memory task. The player must determine in which contexts the agent requires their input to achieve better combined game plus memory task accuracy scores. We hypothesized that high-performing teams would exhibit different patterns of control inputs when compared to low-performing teams and that these patterns of control would be made more evident with user interfaces that increased operator situation awareness. Preliminary results are inconclusive, but show different patterns of interaction between high- and low-performing teams.