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

• Published in: Proceedings of the 2007 IEEE/WIC/ACM International Conference on Intelligent Agent Technology pp. 116 - 120
• Main Authors: Hanratty, Timothy, Hammell II, Robert J., Yen, John, Fan, Xiaocong
• Format: Conference Proceeding
• Language: English
• Published: Washington, DC, USA IEEE Computer Society 02.11.2007
• Series: ACM Conferences
• Subjects: Applied computing > Operations research > Decision analysis; Computing methodologies > Artificial intelligence > Distributed artificial intelligence > Cooperation and coordination; Computing methodologies > Artificial intelligence > Distributed artificial intelligence > Intelligent agents; Computing methodologies > Modeling and simulation > Model development and analysis; Computing methodologies > Modeling and simulation > Model development and analysis > Model verification and validation; Human-centered computing > Human computer interaction (HCI); Information systems > Information systems applications > Decision support systems
• ISBN: 0769530273; 9780769530277
• DOI: 10.1109/IAT.2007.106

In our ever expanding network-centric, serviceoriented 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.