Empirical and Model-based Reasoning in Expert Systems

Abstract

Many expert systems are now being written which rely on highly-compiled, empirical knowledge for their reasoning power. Model-based reasoning has significant theoretical advantages. I constructed two expert systems for the same domain , one using large-grained compiled knowledge, the second using model-based reasoning. The use of model-based reasoning resulted in improved knowledge accessibility and flexibility, and expanded problem-solving ability. Many current expert systems rely on highly-compiled, large-grained knowledge (heuristics, empirical associations, "r ules of thumb") for their reasoning power.* An alternative approach, model-based reasoning, uses a detailed model of the objects in the domain and the operations that act on those objects. The relative problem solving abilities of these two different types of reasoning have not previously been experimentally compared. I constructed two programs that solve problems in the same domain, with the same objectives, but each using a different reasoning method. GENEX (I{oton , 1983) and GENEX II (Koton, 1985) solve problems about the behavior of bacterial operons, ** a subfield of molecular biology.