Abstract
Comprehensive and computational models of human performance have both scientific and practical importance to human–machine system design and human-centered computing. This article describes QN-ACES, a cognitive architecture that aims to integrate two complementary classes of cognitive architectures: Queueing network (QN) mathematical architecture and ACT–R, CAPS, EPIC, and Soar (ACES) symbolic architectures. QN-ACES represents the fourth major step along the QN architecture development for theoretical and methodological unification in cognitive and human–computer interaction modeling. The first three steps—QN architecture for response time, QN-RMD (Reflected Multidimensional Diffusions) for response time, response accuracy, and mental architecture, and QN-MHP (Model Human Processor) for mathematical analysis and real time simulation of procedural tasks—are summarized first, followed by a discussion of the rationale, importance and specific research issues of QN-ACES.
Published Version
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