Empirical and Model-based Reasoning in Expert Systems

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, "rules 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 (Koton, 1983) and GENEX II (Koton, 1985) solve problems about the behavior of bacterial operons, a subfield of molecular biology. Both pro-* These concepts are not strictly logically equivalent, but in current practice systems based on empirical or heuristic associations typically do express their knowledge in large-grained, compiled form. **The term operon refers to a genetic control system in which the activity of a set of structural genes, coding for metabolically related proteins required by the cell, is governed by the interaction of a regulatory protein with an adjacent region of DNA (called the operator). In order for the operon proteins to be made, the enzyme RNA polymerase must be able to bind to the DNA at a specific site, known as the promoter. It can only do this if the operator, which overlaps the promoter, is not blocked by the presence of the bound regulatory protein. Whether or not the regulatory protein binds at the operator depends on whether the cell needs to make more of the operon proteins. grams take as input a description of an operon (real or imaginary) and information on whether or not the genes of that operon are expressed, then attempt to deduce the biological control mechanism causing the observed behavior of the operon. GENEX uses large-grained empirical knowledge to solve problems, and GENEX II uses model-based reasoning. GENEX II can solve a greater variety of problems and more difficult problems than GENEX. The expert knowledge in the GENEX program consists of 62 pieces of knowledge relating observed phenomena to possible causes. …