Carnegie Mellon University
file.pdf (146.28 kB)

A machine learning decision support system for collaborative design

Download (146.28 kB)
journal contribution
posted on 1994-01-01, 00:00 authored by Nenad Ivezic, James Henry. Garrett, Carnegie Mellon University.Engineering Design Research Center.
Abstract: "The research described in this paper is motivated by the complexity surrounding the development of decision support systems (DSSs) for collaborative design processes. If one realizes that each design agent engaged in a collaborative design process may have a unique theory of product behavior, a distinct language of communication, and a specific model of decision making, the complexity of building a DSS for such a design process is obvious. In this paper, we propose that machine learning is probably the only feasible approach to build a DSS for certain classes of collaborative design problems. We discuss high-level requirements for such a DSS and then propose a conceptual solution to build such a DSS based on a machine learning approach."


Publisher Statement

All Rights Reserved



Usage metrics