<title>Traditional and automated approaches for acquiring expert knowledge</title>

Abstract

The acquisition of expert knowledge is a laborious task that could benefit from techniques which simplify and shorten the process without degrading the results. The expert domain under examination is a pilot's threat response, which includes skilled motor responses as well as expert knowledge. Standard knowledge acquisition techniques can be accelerated in several ways, including improvements in the expert interviewing and knowledge encoding processes, and in using cases of expert performance in addition to the experts' interpretation of the cases. Either approach to improving the acquisition process has costs associated with it. To maximize the usefulness of an interview knowledge engineers should exploit the time available before and after the interview to carefully prepare the questions and delicately analyze the responses. To extract rules or response patterns from cases, requires a learning algorithm (i.e. a Machine Learning or Neural Network (NN) system) or a person who can generalize from the observed behaviors. In order to obtain cases with as much recorded information as possible, we are using responses made by pilots "flying" in a full mission simulator. These responses can then be used to train NNs. A benefit of this second approach, in the acquisition of a pilot's tactical responses, is that it eliminates the difficult process of trying to recreate long forgotten reasons for making sensory classifications and selecting sensory guided behavior. There are also potential costs associated with this second approach. The computational cost of inferring rules from cases is generally high, and a large number of examples may be required to accurately deduce even a simple rule-base. One advantage to learning responses with a NN is that the response time of the NN system can be very fast. We are simultaneously pursuing both approaches for improving the acquisition of pilot responses. These approaches may be complementary to each other, they may be used to verify each other (to measure each one’s effectiveness), or they may be totally separate from one another, each providing valued knowledge gleaned from domain experts. Over the next year we will be comparing the cost and performance of Expert and Learning Systems.