Automated knowledge acquisition by reasoning failures

摘要

Automated knowledge acquisition is viewed as a problem in modeling a knowledge engineer's introspective capabilities. We formulate a computational model of automated knowledge acquisition by modeling such introspective debugging actions. We propose that an automated knowledge acquisition system should be provided with an explicit model of performance-failure explanation mechanisms, and show that linking the expectations of the knowledge-based system to the model enables the knowledge acquisition program to determine what parts of the domain knowledge base are responsible for observed performance failures. The knowledge acquisition process is failure driven and is guided by the explanations of failures. Generating explanations of bugs in the knowledge base is perceived as the abductive as well as model-based process. We use an explanation apprentice that analyzes the faulty behavior of the knowledge-based system and answers a broad range of questions of the knowledge acquisition system. A training-and-test experiment using the knowledge acquisition system increases the performance of the knowledge-based system of the training and the testing set by 31% and 51%, respectively.