Human-Agent Collaboration for Time-Stressed Multicontext Decision Making

Multi-context team decision making under time stress is an extremely challenging issue faced by various real world application domains. In this study we employ an experience-based cognitive agent architecture (R-CAST) to address the informational challenges associated with military command and control (C) decision making teams, the performance of which can be significantly affected by dynamic context switching and tasking complexities. Using context switching frequency and task complexity as two factors, we conducted an experiment to evaluate whether the use of R-CAST agents as teammates and decision aids can benefit C decision making teams. Members from a US Army ROTC (Reserve Officer Training Corps) organization were randomly recruited as human participants. They were grouped into ten Human-Human teams each composed of two participants and ten Human-Agent teams each composed of one participant and two R-CAST agents as teammates and decision aids. Statistical inference of the experiment results indicates that R-CAST agents can significantly improve the performance of C teams in multi-context decision making under varying time-stressed situations.