Problem Solving and Sustained Learning from Experience: Analyzing Methods with Respect to Domain Characteristics

Integration of learning and problem solving may start out from different goals, and be viewed from different perspectives. One example is "concept formation" as a goal, and the formation and utilization of operationalization criteria related to the problem solving solving task, as the perspective. Another example is "improved performance" as a goal, and the improvement of total problem solving speed for computer and human together as the perspective. A third example is "sustained learning", i.e. continuous learning through problem solving experience, as a goal, and the impact of the application problem task on the learning method as a perspective. Many more examples may be given, and for each of them a particular area of overlap, an "intersection space" between machine learning (ML) and problem solving (PS) methods can be identified. Within this space, interdependencies between ML and PS methods may be described and analyzed in a systematic way, provided we have a suitable means to structure the space.