A Machine Learning Decision Support System for Collaborative Design

by Nenad Ivezic, Carnegie Mellon Univ, Pittsburgh, United States,
James H. Garrett, Jr., Carnegie Mellon Univ, Pittsburgh, United States,



Document Type: Proceeding Paper

Part of: Computing in Civil Engineering

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 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.

Subject Headings: Structural design | Decision support systems | Equipment and machinery | Artificial intelligence | Building design | Computer languages | Engineering education | Motivation

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