TY - GEN
T1 - Acquiring Problem-Solving Knowledge from End Users
T2 - 17th National Conference on Artificial Intelligence, AAA1 2000
AU - Kim, Jihie
AU - Gil, Yolanda
N1 - Publisher Copyright:
Copyright © 2000, American Association for Artificial Intelligence (www.aaai.org). All rights reserved.
PY - 2000
Y1 - 2000
N2 - 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 approaches 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 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 address several questions and hypotheses regarding 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.
AB - 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 approaches 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 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 address several questions and hypotheses regarding 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.
UR - http://www.scopus.com/inward/record.url?scp=0012998831&partnerID=8YFLogxK
M3 - Conference contribution
AN - SCOPUS:0012998831
T3 - Proceedings of the 17th National Conference on Artificial Intelligence and 12th Conference on Innovative Applications of Artificial Intelligence, AAAI 2000
SP - 223
EP - 229
BT - Proceedings of the 17th National Conference on Artificial Intelligence and 12fth Conference on Innovative Applications ofArtificial Intelligence, AAAI 2000
PB - AAAI Press
Y2 - 30 July 2000 through 3 August 2000
ER -