Abstract : Structural modeling requires the construction of an appropriate mathematical description to describe the behavior of a physical object. Because conflicting goals and uncertainty permeate the process of structural modeling, structural model derivation is a complicated process. Therefore, to effectively and efficiently model any structure, one must have a method for planning actions, for proceeding in the face of uncertain information, and for dealing with uncertainty. This paper presents a method for representing structural modeling as a strategic process involving decision-making in an environment where the results of any decision may not be known with complete certainty. Specifically, an existing task-level architecture developed to manage uncertainty in treating cardiac disease is adapted to embody the strategic knowledge of a structural engineer in formulating and solving structural problems. The MUMS system focuses on plates as the structure of interest in its pilot implementation. Since the system is concerned with structural modeling at the strategic level (as opposed to a detailed design, for instance), the ideas presented are applicable to modeling any structure. Keywords: Knowledge based systems.
[1]
C. L. Dym,et al.
A knowledge-based system for automated architectural code checking
,
1988
.
[2]
Edmund H. Durfee,et al.
Approximate Processing in Real-Time Problem Solving
,
1988,
AI Mag..
[3]
Paul R. Cohen,et al.
Design for Acquisition: Principles of Knowledge-System Design to Facilitate Knowledge Acquisition
,
1987,
Int. J. Man Mach. Stud..
[4]
Robert S. Engelmore,et al.
SACON: A Knowledge-Based Consultant for Structural Analysis
,
1979,
IJCAI.
[5]
Edward H. Shortliffe,et al.
A model of inexact reasoning in medicine
,
1990
.
[6]
Thomas R. Gruber,et al.
The Acquisition of Strategic Knowledge
,
1989
.
[7]
Steven J. Fenves,et al.
Knowledge-based analysis of structural systems
,
1987
.
[8]
Paul R. Cohen,et al.
MU: A Development Environment for Prospective Reasoning Systems
,
1987,
AAAI.
[9]
Clive L. Dym,et al.
Knowledge Acquisition from Multiple Experts
,
1984,
AI Mag..