We hypothesize that they are acquired incrementally from design experiences in familiar domains by generalization over patterns of regularity. Three important issues in generalization from experiences are what to generalize from an experience, how far to generalize, and what methods to use. In particular, we describe the model-based learning method with a scenario of learning of feedback mechanism. Learning Problem-Solving Concepts by Reflecting on Problem Solving . Different models of problem solving, however, recognize different knowledge needs, and, as a result, set up different learning tasks.
Some recent models analyze problem solving in terms of generic tasks, methods, and subtasks.
These models require the learning of problem-solving concepts such as new tasks and new task decompositions. We view reflection as a core process for learning these problem-solving concepts. In this paper, we identify the learning issues raised by the task-structure framework of problem solving. We describe how this model enables refection, and how model-based reflection enables the reasoner to adapt its task structure to produce solutions of better quality. The Autognostic system illustrates this reflection process.