Acquiring control knowledge from examples using ripple-down rules and machine learning

Abstract

The inability of experts to articulate the knowledge required to solve a problem is, arguably, the greatest challenge to building an expert system. The problem is made worse in situations where the response of the expert must be so rapid that there is not even a chance of a plausible post hocreconstruction of the decision processes involved. For this reason, construction of the knowledge base by example is the only approach available. Examples can be used in two ways. They may be used as input to an induction program whose task is to find an abstraction of a control strategy from the data. Examples may also be used to induce the expert to discern differences between cases, thereby allowing the knowledge acquisition system to construct rules semi-automatically. The work presented in this paper demonstrates the feasibility of both approaches. In particular, it shows that the RDR methodology can be extended to domains where the expertise involved is necessarily subcognitive. This is demonstrated by the application of a combination of ripple-down rules and machine learning to the task of acquiring piloting skills for an aircraft in a flight simulator.