|
Abstract : |
The principal goal of Qualitative Reasoning (QR) is to represent not only our everyday commonsense knowledge about the physical world, but also the underlying abstractions used by engineers and scientists when they create quantitative models. Endowed with such knowledge, and appropriate reasoning methods, a computer could make predictions, diagnoses and explain the behaviour of physical systems in a qualitative manner, even when a precise quantitative description is not available or is computationally intractable. The key to a qualitative representation is not simply that it is symbolic, and utilises discrete quantity spaces, but that the distinctions made in these discretisations are relevant to the behaviour being modelled. QR has now become a mature subfield of AI as evidenced by its 10th annual international workshop, several books (e.g. [7]) and a wealth of conference and journal publications. Although the field has broadened to become more than just Qualitative Physics (as it was first known), the bulk of the work has dealt with reasoning about scalar quantities, whether they denote the level of a liquid in a tank, the operating region of a transistor or the, |
