Utilizing Concept Maps to Improve Human-Agent Collaboration Within a Recognition-Primed Decision Model

In our ever expanding network-centric, service- oriented environments, human-agent collaboration is becoming increasingly more important to the success of future automated decision support models. Effective information retrieval, information analysis, information management and presentation will rely on stronger collaboration between the user (human) and their automated decision assistants (agents). This paper explores the utility of concept maps as an effective medium for improving human-agent collaboration within the scope of a naturalistic decision making (NDM) model; specifically the agent-based R-CAST system derived from Klein´s recognition-primed decisions (RPD) model.