Modeling and Debugging Engineering Decision Procedures with Machine Learning

This paper reports on the use of machine learning systems for modeling existing engineering decision procedures. In this activity, various models of an existing decision procedure are constructed by using diierent machine learning systems as well as by changing their operational parameters and input. Individual models serve to focus on diierent aspects of the decision procedure and their combined use thus improves the understanding of the decision procedure which, in turn, can assist in its evaluation and subsequent debugging and improvement. This important modeling role of machine learning systems is exempliied by modeling an existing decision procedure that is used by engineers in selecting among available techniques for modeling groundwater ow and contaminant transport in a process of environmental decision making. This decision procedure was corrected and improved in the course of this work. The example demonstrates the practical utility of the modeling role of machine learning for engineering applications.