Brian R. Gaines
The problem of transforming the knowledge bases of performance systems using induced rules or decision trees into comprehensible knowledge structures is addressed. A knowledge structure is developed that generalizes and subsumes production rules, decision trees, and rules with exceptions. It gives rise to a natural complexity measure that allows them to be understood, analyzed and compared on a uniform basis. This structure is a rooted directed acyclic graph with the semantics that nodes are concepts, some of which have attached conclusions, and the arcs are 'isa' inheritance links With disjunctive multiple inheritance. A detailed example is given of the generation of a range of such structures of equivalent performance for a simple problem, and the complexity measure of a particular structure is shown to relate to its perceived complexity. The simplest structures are generated by an algorithm that factors common concepts from the premises of rules. A more complex example of a chess dataset is used to show the value of this technique in generating comprehensible knowledge structures.