Acquiring Problem-Solving Knowledge from End Users: Putting Interdependency Models to the Test

摘要

Developing tools that allow non-programmers to enter knowledge has been an ongoing challenge for AI. In recent years researchers have investigated a variety of promising ap-proaches to knowledge acquisition (KA), but they have often been driven by the needs of knowledge engineers rather than by end users. This paper reports on a series of experiments that we conducted in order to understand how far a particular KA tool that we are developing is from meeting the needs of end users, and to collect valuable feedback to motivate our future research. This DA tool, called EMeD, exploits Interdependency Models that relate individual components of the knowledge base in order to guide users in spectifying problem-solving knowledge. We describe how our experi-ments helped us address several questions and hypotheses re-garding the acquisition of problem-solving knowledge from end users and the benefits of Interdependency Models. and discuss what we learned in terms of improving not only our KA tools but also about KA research and experimental methodology.