A framework for intervention based team support in time critical tasks

In this paper we describe the intervention framework of ATLAS, an artificial socially intelligent agent that advises teams. The framework treats interventions as atomic components, and manages the lifecycle of each intervention through presentation, as well as followups to interventions. The key benefit of this framework is that it allows for rapid development of scenario-specific Interventions that leverage scenario-agnostic team models. The implementation of this framework is reported for three player teams in a Search and Rescue task simulated in Minecraft. Low competence teams advised by ATLAS improved more between first and second trials than those with a human advisor while the reverse was found for high competence. Four times as many interventions were proposed as were presented. 15 % of advice was withheld to avoid repetitive advice, excessive rate of advice, and needlessly advising high performing teams, while a Theory of Mind model and delay for confirmation mechanism filtered out other unnecessary advice.