Learning and revising task-specific rules in ACT-R

This paper discusses the problem of how to learn and revise rules for a new task. It will use the framework of the ACT-R theory, which uses analogy to learn new productions. As an example, two general rules to learn new productions will be discussed that can learn two different tasks, a beam-task and a card-classification task.