Cis Schut

An Overvieuw of Approaches to Qualitative Model Construction

In qualitative reasoning research, much effort has been spent on developing representation and reasoning formalisms. Only recently, the process of constructing models in terms of these formalisms has been recognised as an important research topic of its own. Approaches addressing this topic are examined in this review. For this purpose a general model of the task of constructing qualitative models is developed that serves as a frame of reference in considering these approaches. Two categories of approaches are identified: model composition and model induction approaches. The former compose a model from predefined partial models and the latter infer a model from behavioural data. Similarities and differences between the approaches are discussed using the general task model as a reference. It appears that the majority of approaches focus on automating model construction entirely. Assessing and debugging a model in cooperation with a modeller is identified as an important topic for future research

Modeling the Influence of Non-Changing Quantities

In qualitative modelling, information is lost by abstracting from quantitative formulae. We show that when the behaviour of two similar systems is compared, non-changing quantities from these formulae can have a significant influence on the qualitative prediction. We propose the addition of a new ontological primitive for representing these influences in qualitative models, and provide a calculus for exploiting this primitive in the reasoning process. Augmentation with the new primitive enhances the power of the qualitative simulator, resulting in a more appropriate prediction of behaviour, and also improves the explanation capacities of the model. The latter feature is of major importance for tutoring systems using qualitative reasoning.

Building Qualitative Models for Reasoning about Device Behavior

This article describes the general principles of qualitative reasoning, a special form of reasoning with deep knowledge. Three prevailing approaches to qualitative reasoning are integrated into one generic framework with which it is possible to describe qualitative deep knowledge and the reasoning process that uses this knowledge to predict the behavior of physical systems. In addition, the process of modeling a real-world system into the qualitative vocabulary is analyzed, and two examples of techniques to automate this modeling activity are discussed.