Developing Concept Learning Capabilities in the COGNET/IGEN Integrative architecture and Associated Agent-based Modeling and Behavioral Representation (AMBR) Air Traffic Control
Abstract
This report describes all Systems' efforts during Phase III and IV of the model comparison process of the Agent-based Modeling and Behavioral Representation (AMBR) program conducted by the Air Force Research Laboratory. Phases III and IV focused on modeling and simulating concept learning within the context of a simulated Air Traffic Control (ATC) work environment. all Systems extended the COGNET/iGEN framework used in prior AMBR phases, implementing a general capability to learn the conditions under which each of a disjunctive set of goals or actions should be taken. Deeper extensions to the system enabled the representation of memory decay, rehearsal, and proactive interference needed to model human learning performance. The Phase III and IV COGNET/iGEN model execution results were compared with the results of three other models (ACT-R, D-Cog, EPIC- Soar) and results collected from human trials. On most measures (accuracy, response time, workload) iGEN model results were indistinguishable from human performance and, overall, iGEN results provided a better fit to the human data than the other models tested.
Document Details
- Document Type
- Technical Report
- Publication Date
- Sep 01, 2004
- Accession Number
- ADA439347
Entities
People
- Floyd Glenn
- James Stokes
- Jean-christophe Le Mentec
- Joan Ryder
- Thomas Santarelli
- Wayne Zachary
Organizations
- CHI Systems (United States)