Computer Generated Forces (CGF), to be effective training tools, must exhibit robust, challenging, and realistic behaviors. CGF tasks usually have both cognitive and reactive aspects to them. The reactivity has to co-exist with (higher-level) cognitive activities like planning and strategy assessment. The overall purpose of this research is to merge a machine-learning algorithm (SAMUEL, an evolutionary algorithm-based rule learning system) with a cognitive model (ACT-R) into a system where the learning algorithm handles the reactive aspects of the task and provides an adaptation mechanism, and where the behavior's realism is constrained by the cognitive model. Such a system would learn through experience and would be able to adapt to changes in adversaries' strategies and capabilities to present human opponents with more exciting, varied, yet realistic training situations. This preliminary work presents an initial examination of the effects of the changes in task reactivity on human and SAMUEL control abilities.