QN-ACES: Integrating Queueing Network and ACT-R, CAPS, EPIC, and Soar Architectures for Multitask Cognitive Modeling

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 stepsQN 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 tasksare summarized first, followed by a discussion of the rationale, importance and specific research issues of QN-ACES.