Bridger: Learning Bridge Design Knowledge

Bridger is an experimental learning system developed to explore the possibility of using machine learning techniques for the acquisition and improvement of design knowledge. Bridger is a multistrategy learning system consisting of two main learning systems: ECOBWEB and EPROTOS. Both systems employ multistrategy learning and both are case-based reasoning systems. Thus Bridger is a multistrategy learning system at the micro and macro sense. As shown bellow, Bridger's architecture also integrates finite element analysis for checking a newly synthesized bridge according to AASHTO requirements.

Bridger's architecture

A Sample Publications Include:

  • Reich, Y. (1991), Design Knowledge Acquisition: Task Analysis and a Partial Implementation, Knowledge Acquisition, 3(3):237-254.
    Contrary to views presented before 1990, this paper shows that by breaking up design tasks into smaller subtasks, computational support can be designed to aid the acquisiton of knowledge for a complete design task. (Postscript file, 273K, one figure missing)
  • Reich, Y. and Fenves, S. J., (1992), Inductive Learning of Synthesis Knowledge, International Journal of Expert Systems: Research and Applications, 5(4):275-297.
    This paper presents ECOBWEB, a system that extends the conceptual clustering system COBWEB. Part of the paper shows how ECOBWEB traces a change in a domain as reflected by bridges constructed in Pittsburgh. (Postscript file, 451K)
  • Reich. Y. (1993), A Model of Aesthetic Judgment in Design, Artificial Intelligence in Engineering, 8(2):141-153.
    This paper presents a model of how aesthetic judgment in design may be performed. It illustrates the ideas by using examples of bridge shaped synthesized by Bridger. (Postscript file, 810K)
  • Reich, Y. (1993), The Development of Bridger: A Methodological Study of Research on Machine Learning in Design, Artificial Intelligence in Engineering, 8(3):217-231.
    This paper presents a reflection on the course of the development of Bridger. The aim is to give a more accurate description of the steps that were carried out and their underlying reasons than is usually given in a paper. (Postscript file, 359K)
  • Reich, Y. and Fenves, S. J. (July, 1995), A system that Learns to Design Cable-Stayed Bridges, Journal of Structural Engineering, ASCE, 121(7):1090-1100.
    This paper presents the most complete description of Bridger. (Note however that due to ASCE's limit on the number of words per paper [10,000] there are still details that were omitted.) (Postscript file, 408K; Zipped PDF file, 855K)
  • Reich, Y. (1995), The Value of Knowledge, International Journal of Human-Computer Studies, 42(1):3-30.
    The issue of knowledge evaluation is raised often in relation to building intelligent systems, knowledge acquisition, or machine learning. There are many proposals, both quantitative and qualitative that float around. This paper organizes these by using the ideas of measurement theory. The paper illustrates the ideas through the evaluation of the design knowledge acquired by Bridger, an experimental system for learning knowledge on cable-stayed bridge design. (Postscript file, 136K compressed; PDF, 185K zipped)

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Copyright 1995-2005 Yoram Reich
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Last modified: 4/18/2005 9:10:00 PM