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

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 approachesto knowledgeacquisition (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 conductedin order to understandhow 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 KA tool, called EMeD, exploits Interdependency Models that relate individual components of the knowledge base in order to guide users in specifying problem-solving knowledge. We describe how our experiments helped us addressseveral questionsand hypothesesregarding 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.

Authors

• Jihie Kim and Yolanda Gil University of Southern California

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