Abstract
Classical artificial intelligence techniques such as expert systems have often been found to be too brittle for large-scale applications. Model-based reasoning is a technique for making artificial intelligence software applicable to problems of realistic size.
In this working group, we have investigated some fundamental issues in model-based reasoning and various applications in diagnosis, control and design. This paper reports on the results in basic techniques as well as diagnosis applications obtained at EPFL, ETHZ, Landis & Gyr and Nestlé. The results of the part dealing with design are reported in another paper in this issue, pp. 65–73.
Get full access to this article
View all access options for this article.