Automating knowledge acquisition as extending, updating, and improving a knowledge base

A method for the automation of knowledge acquisition that is viewed as a process of incremental extension, updating, and improvement of an incomplete and possibly partially incorrect knowledge base of an expert system is presented. The knowledge base is an approximate representation of objects and inference processes in the expertise domain. Its gradual development is guided by the general goal of improving this representation to consistently integrate new input information received from the human expert. The knowledge acquisition method is presented as part of a methodology for the automation of the entire process of building expert systems, and is implemented in the system NeoDISCIPLE. The method promotes several general ideas for the automation of knowledge acquisition, such as understanding-based knowledge extension, knowledge acquisition through multistrategy learning, consistency-driven concept formation and refinement, closed-loop learning, and synergistic cooperation between a human expert and a learning system