Abstract
One of the challenging issues in homeland security area is the early detection and successful processing of potential terrorist threats, which demands effective team collaboration. In this research, we investigate the way of incorporating naturalistic decision making models for supporting distributed team decision making. By extending Klein's Recognition-Primed Decision model, we propose a Collaborative RPD model (C 2RPD), which encourages proactive information seeking, linking, and sharing in distributed teamwork settings. This model establishes a basis for developing agent architectures that support both agent-agent and agent-human collaborations in developing shared situation awareness and in making decisions based on progressively refined recognitions.
Original language | English (US) |
---|---|
Title of host publication | AI Technologies for Homeland Security - Papers from the 2005 AAAI Spring Symposium, Technical Report |
Pages | 17-24 |
Number of pages | 8 |
Volume | SS-05-01 |
State | Published - 2005 |
Event | 2005 AAAI Spring Symposium - Stanford, CA, United States Duration: Mar 21 2005 → Mar 23 2005 |
Other
Other | 2005 AAAI Spring Symposium |
---|---|
Country/Territory | United States |
City | Stanford, CA |
Period | 3/21/05 → 3/23/05 |
All Science Journal Classification (ASJC) codes
- Engineering(all)