Learning problem-oriented decision structures from decision rules: The AQDT-2 system

A decision structure is an acyclic graph that specifies an order of tests to be applied to an object (or a situation) to arrive at a decision about that object. and serves as a simple and powerful tool for organizing a decision process. This paper proposes a methodology for learning decision structures that are oriented toward specific decision making situations. The methodology consists of two phases: 1—determining and storing declarative rules describing the decision process, 2—deriving on-line a decision structure from the rules. The first step is performed by an expert or by an AQ-based inductive learning program that learns decision rules from examples of decisions (AQ15 or AQ17). The second step transforms the decision rules to a decision structure that is most suitable for the given decision making situation. The system, AQDT-2, implementing the second step, has been applied to a problem in construction engineering. In the experiments, AQDT-2 outperformed all other programs applied to the same problem in terms of the accuracy and the simplicity of the generated decision structures.