Varol Akman, in his letter (AI Magazine, Spring 1990) criticizing QSIM, quotes both me and Janowski, accurately I believe, describing various limitations of QSIM. At the risk of being scolded again for "employing universal truths and unarguable facts" in support of my position, I must point out that it is the responsibility of a scientist or engineer to document clearly the known limitations of any method he develops and publishes. In addition to truth in packaging, a clear and unblinking examination of the limitations of one's own work is an invaluable guide to further research.
Qualitative reasoning can, and should, be decomposed into a model-buibding task, which creates a qualitative differential equation (&DE) as a model of a physical situation, and a qualitative simuhtion task, which starts with a QDE, and predicts the possible behaviors following from the model. In support of this claim, we present & PC, a model builder that takes the general approach of Qualitative Process Theory [Forbus, 19841, describing a scenario in terms of views, processes, and influences. However, & PC builds & DE s f or simulation by QSIM, which gives it access to a variety of mathematical advances in qualitative simulation incorporated in QSIM. We present QPC and its approach to Qualitative Process Theory, provide an example of building and simulating a model of a nontrivial mechanism, and compare the representation and implementation decisions underlying & PC with those of QPE [Falkenhainer and Forbus, 1988; Forbus, 19901.
Jim Saveland Research Forester Associate Editor, AI Application in Natural Resource Management United States Department of Agriculture Forest Service Southern Forest Fire Laboratory Route 1, Box 182A Dry Branch, GA 31020 Editor: Mr. Saveland's letter focuses our attention on the important distinction between accuracy and realism. We believed the Phoenix fire simulator to be accurate (with the provisos noted in our article). Mr. Saveland believes otherwise, and he is certainly better qualified than us to judge! We can allay some doubts (e.g., firefighting objects actually do move at variable rates, depending on ground cover, as Mr. Saveland notes they should), but basically we agree with Mr. Saveland that the Phoenix fire simulator is not accurate. But we do claim it is realistic.
Predicting the behavior of physical systems is essential to both common sense and engineering tasks. It is made especially challenging by the lack of complete precise knowledge of the phenomena in the domain and the system being modelled. We present an implemented approach to automatically building and simulating qualitative models of physical systems. Imprecise knowledge of phenomenais expressed by qualitative representations of monotonic functions and variable values. Incomplete knowledge about the system is either inferred or alternative complete descriptions that will affect behavior are explored. The architecture and algorithms used support both effective implementation and formal analysis. The expressiveness of the modelling language and strength of the resulting predictions are demonstrated by substantial applications to complex systems.
Semiquantitative models combine both qualitative and quantitative knowledge within a single semiquantitative qualitative differential equation (S&DE) representation. With current simulation methods, the quantitative knowledge is not exploited as fully as possible. This paper describes dynamic envelopes - a method to exploit quantitative knowledge more fully by deriving and numerically simulating an extremad system whose solution is guaranteed to bound all solutions of the SQDE. It is shown that such systems can be determined automatically given the SQDE and an initial condition. As model precision increases, the dynamic envelope bounds become more precise than those derived by other semiquantitative inference methods. We demonstrate the utility of our method by showing how it improves the dynamic monitoring and diagnosis of a vacuum pumpdown system.