The Seventh International Workshop on Qualitative Reasoning about Physical Systems was held on 16-19 May 1993. The bulk of the 50 attendees work in the AI area, but several engineers and cognitive psychologists also attended. The two topics attracting special attention were automated modeling and the design task. This article briefly describes some of the presentations and discussions held during the workshop. To promote deep and focused discussion, participation was limited to 50 researchers; the bulk of attendees work in the area of AI, but several engineers and cognitive psychologists enriched the atmosphere.

The automotive industry was the first to promote the development of applications of model-based systems technology on a broad scale and, as a result, has produced some of the most advanced prototypes and products. In this article, we illustrate the features and benefits of model-based systems and qualitative modeling by prototypes and application systems that were developed in the automotive industry to support on-board diagnosis, design for diagnosability, and failure modes and effects analysis. Car manufacturers and their suppliers face increasingly serious challenges particularly related to fault analysis and diagnosis during the life cycle of their products. On the one hand, the complexity and sophistication of vehicles is growing, so it is becoming harder to predict interactions between vehicle systems, especially when failures occur. On the other hand, legal regulations and the demand for safety impose strong requirements on the detection and identification of faults and the prevention of their effects on the environment or dangerous situations for passengers and other people.

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. Akman observes, correctly, that QSIM is a purely mathematical formalism for expressing qualitative differential equation models of the world, and not a physical modeling methodology. Our research group has also been concerned with this limitation, so we have developed modelbuilding methods which compile QDEs for QSIM to simulate, either from a component-connection description of a device (Franke and Dvorak 1989, 1990), or from a physical scenario description via qualitative views and processes (Crawford, Farquhar, and Kuipers 1990). These two model-building methods are important elements of the QSIM perspective on qualitative reasoning (Kuipers 1989).

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.

We argue that qualitative modeling provides a valuable way for students to learn. Learning to formulate, test, and revise models is a crucial aspect of understanding science and is critical to helping students become active, lifelong learners. Supporting students in articulating models of a domain and refining them through experience, reflection, and discussion with peers and teachers can lead to deeper, systematic understanding of science (for example, Reif and Larkin [1991]; Collins [1996]). However, modeling formalisms have traditionally been associated with creating mathematical models and deriving numeric results. Such approaches fail to capture many crucial aspects of models, such as the conditions under which a model is applicable, and are relatively inaccessible to younger children, such as middle school students.

In general, modeling is a complex and creative task, and building qualitative models is no exception. One way of automating this task is by means of machine learning. Observed behaviors of a modeled system are used as examples for a learning algorithm that constructs a model that is consistent with the data. In this article, we review approaches to learning qualitative models, either from numeric data or qualitative observations. However, an important practical question is how do we construct qualitative models in the first place.

Traditional approaches to ecological modeling, based on mathematical equations, are hampered by the qualitative nature of ecological knowledge. In this article, we demonstrate that qualitative reasoning provides alternative and productive ways for ecologists to develop, organize, and implement models. We present a qualitative theory of population dynamics and use this theory to capture and simulate commonsense theories about population and community ecology. Advantages of this approach include the possibility of deriving relevant conclusions about ecological systems without numeric data; a compositional approach that enables the reusability of models representing partial behavior; the use of a rich vocabulary describing objects, situations, relations, and mechanisms of change; and the capability to provide causal interpretations of system behavior. A number of textbooks published recently (for example, Haefner [1996]; Jørgensen and Bendoricchio [2001]) show that ecological modeling is almost synonymous with mathematical model building.

The Eleventh International Workshop on Qualitative Reasoning was held in Cortona, Italy, on 3 to 6 June 1997. Participants included scientists from both qualitative reasoning and quantitative mathematical modeling communities. This article summarizes the significant issues and discussion raised during the workshop. Given the organizational context, an additional goal in our minds in preparing the workshop was to establish a basis for interaction between the qualitative and quantitative communities. To this end, in addition to the presentation of full papers, posters, and short talks, in line with past workshop schedules, we planned invited talks, the focuses of which were problem domains for qualitative reasoning in real-world applications, and a tutorial on system identification.

We examine different formalisms for modeling qualitatively physical systems and their associated inferential processes that allow us to derive qualitative predictions from the models. We highlight the mathematical aspects of these processes along with their potential and limitations. However, the modeling process encounters difficulties from both ends: A model must adapt to the knowledge available and the task it is built for. The possible limitations of traditional numeric methods with respect to these problems mean qualitative models can be a good alternative: (1) qualitative models cope with uncertain and incomplete knowledge, (2) a qualitative model output equals an infinity of numeric runs that are obtained at once in compact form, (3) the qualitative predictions provide the relevant qualitative distinctions in the system's behavior, and (4) the modeling primitives allow for a more intuitive interpretation. A system's evolution can be tackled in discrete terms by defining states and events that trigger transitions between states.

Learning how to create, test, and revise models is a central skill in scientific reasoning. We argue that qualitative modeling provides an appropriate level of representation for helping middle-school students learn to become modelers. We describe Vmodel, a system we have created that uses visual representations and that enables middle-school students to create qualitative models. Software coaches use simple analyses of model structure plus qualitative simulation to provide feedback and explanations. This system has been used in several studies in Chicago public school classrooms, using curricula developed in collaboration with teachers.