Abstract
The success of case-based design aids depends both on the case-based reasoning processes they apply and on effectively integrating those processes into the larger task context: on making the case-based reasoning component present case information at the right time and in the right way, on exploiting additional information resources as needed to supplement the case library and to guide case application, on capturing useful information from current reasoning and providing it to up- and down-stream designers, and on unobtrusively learning new cases during the design process. This article presents a set of principles and techniques for integrated case-based design support systems and illustrates their application through a case study of the Stamping Advisor, a system to support feasibility analysis for sheet metal automotive parts.
| Original language | English (US) |
|---|---|
| Pages (from-to) | 705-716 |
| Number of pages | 12 |
| Journal | Engineering Applications of Artificial Intelligence |
| Volume | 12 |
| Issue number | 6 |
| DOIs | |
| State | Published - Dec 1999 |
Funding
This research was supported by the Ford Motor Company under award No. 0970-355-A200. It was conducted while David Leake was a Visiting Professor at Northwestern University, on sabbatical leave from Indiana University, and he thanks the Intelligent Information Laboratory and the Northwestern Computer Science Department for their generous support. His research is also supported in part by NASA under award No. NCC 2-1035. This paper is extended and revised from ( Leake et al., 1999a ). David Leake is an Associate Professor of Computer Science and member of the Cognitive Science faculty at Indiana University. He received his Ph.D. in Computer Science from Yale University in 1990. His primary research interests are case-based reasoning, goal-driven learning, and strategic information search. He Co-Chaired the Second International Conference on Case-Based Reasoning (ICCBR-97) and edited the book Case-Based Reasoning: Experiences, Lessons, and Future Directions (AAAI Press, 1996). He is the Editor of AI Magazine . Larry Birnbaum is Chair of the Computer Science Department and Associate Professor of Computer Science and the School of Education and Social Policy at Northwestern University. He received his Ph.D. in Computer Science from Yale University in 1986. His research interests include interface design and intelligent interfaces; computers and education; natural language understanding; computer vision; and machine learning. Kristian Hammond is a Professor of Computer Science at Northwestern University. He received his Ph.D. in Computer Science from Yale University in 1986. Through 1998 he was the Director of the Artificial Intelligence Laboratory at The University of Chicago, where he led projects investigating intelligent agents in a wide range of domains. He recently founded the Intelligent Information Laboratory to focus on issues of information access and management arising out of the high-speed computer connectivity of the modern world. The Info Lab charter is to invent and creatively exploit new information technologies that respond to and support human goals within complex computer environments. Cameron Marlow is a doctoral student at the MIT Media Laboratory. He received a B.S. degree at The University of Chicago in 1999. His primary research interests involve methods for using context to better satisfy users’ information needs, intelligent indexing, and integrating case-based reasoning with information retrieval. Hao (Howie) Yang is a process development engineer at the Vehicle Operations division of the Ford Motor Company. He received a Ph.D. in Mechanical Engineering from the University of Missouri-Rolla in 1994. His research interests are in the areas of CAD/CAM, feature-based design, intelligent design and manufacturing systems, case-based reasoning, and knowledge management.
ASJC Scopus subject areas
- Control and Systems Engineering
- Artificial Intelligence
- Electrical and Electronic Engineering