Integrating classification-based complied level reasoning with function-based deep level reasoning

Problem solving based on compiled associations between elements of the decision space and data is an efficient mode of reasoning for a large percentage of situations faced by an expert. But in some (usually small) percentage of cases, compiled associations are not enough by themselves to lead to correct results. Reasoning from “deeper” levels of understanding offers the advantage of producing correct results even in atypical cases, but at the cost of expanding more computational resources. Thus the trade-off between compiled level systems and deep level systems is between computational efficiency (at the compiled level) and problem-solving generality (at the deep level). We describe a hybrid system containing elements of both deep level reasoning and compiled level reasoning. More particularly, we propose a problem-solving architecture for category-based diagnostic problem solving which at the compiled level centers on classification problem solving and at the deep level uses a type of function-based reas...