Report Number: CS-TR-95-1540
Institution: Stanford University, Department of Computer Science
Title: Model-Matching and Individuation for Model-Based Diagnosis
Author: Murdock, Janet L.
Date: January 1995
Abstract: In model-based systems that reason about the physical world, models are attached to portions of the physical system. To make model-based systems more extensible and re-usable, this thesis explores automating model-matching. Models address particular individuals, portions of the physical world identified as separate entities. If the set of models is not fixed, one cannot carve the physical system into a fixed set of individuals. Our goals are to develop methods for matching and individuating and identify characteristics of physical equipment and models required by those methods. Our approach is to identify a set of characteristics, build a system which used them, and test re-usability and extensibility. If the system correctly defines individuals and matches models, even when models calls for individuals not previously defined, then we can conclude that we have identified some subset of the characteristics required. The system matches models to a series of equipment descriptions, simulating re-use. We also add a number of models, extending the system, having it match the new models. Our investigation shows characteristics required are the 3-dimensional space and how the space is filled by functional components, phases, materials, and parameters.