ABSTRACT
The goal of the Agent-based Modeling and Behavior Representation (AMBR) Model Comparison Project is to advance the state of the art in cognitive modeling. Cognitive models representing different modeling architectures are created, run on the task, and then compared to the collected data. The task to be modeled for the phase of AMBR is the combination of the same Air Traffic Control (ATC) task with a new concept acquisition task. The learning mechanism developed enables learning of the conditions under which a task or goal should or should not be pursued, in addition to the mechanism of evaluating a predefined boolean expression against contents of declarative memory. The chapter presents a model of category learning implemented in the ACT-R cognitive architecture. ACT-R is a hybrid architecture that combines a symbolic production system with a subsymbolic activation calculus. The chapter describes the feature-based concept learning infrastructure of DCOG and discusses its performance on the ATC task.
