"Broadly speaking, qualitative-reasoning research aims to develop representation and reasoning techniques that will enable a program to reason about the behavior of physical systems, without the kind of precise quantitative information needed by conventional analysis techniques such as numerical simulators. ... Observing pouring rain and a river's steadily rising water level is sufficient to make a prudent person take measures against possible flooding - without knowing the exact water level, the rate of change, or the time the river might flood."
– Yumi Iwasaki. IEEE Expert: Intelligent Systems (May/June 1997).
The capability of making explainable inferences regarding physical processes has long been desired. One fundamental physical process is object motion. Inferring what causes the motion of a group of objects can even be a challenging task for experts, e.g., in forensics science. Most of the work in the literature relies on physics simulation to draw such infer- ences. The simulation requires a precise model of the under- lying domain to work well and is essentially a black-box from which one can hardly obtain any useful explanation. By contrast, qualitative reasoning methods have the advan- tage in making transparent inferences with ambiguous infor- mation, which makes it suitable for this task. However, there has been no suitable qualitative theory proposed for object motion in three-dimensional space. In this paper, we take this challenge and develop a qualitative theory for the motion of rigid objects. Based on this theory, we develop a reasoning method to solve a very interesting problem: Assuming there are several objects that were initially at rest and now have started to move. We want to infer what action causes the movement of these objects.
This paper describes a system that combines qualitative and quantitative reasoning to solve kinematics word problems that are expressed in a simplified form of English. Such an integrated approach is useful in identifying the equations required to solve the problem and to infer certain implicit details in the problem scenario. The system also generates self-explanatory solutions that can assist a student in mastering the concept involved. We created a dataset of 30 problems from this domain. Such word problems have not been addressed in recent times.
It was motivated by two observations regarding modeling in general and work in qualitative physics in particular. First, all modelbased reasoning is only as good as the model used (Davis and Hamscher 1988). Second, no single model is adequate or appropriate for a wide range of tasks (Weld 1989). A model of a real-world system is but an abstraction of some aspects of the system. To formulate a model of a physical system for a given task, we inevitably take certain perspectives of the system to capture proper scenarios by deciding what to describe and what to ignore (Hobbs 1985).
IAAI is the premier venue for learning about AI's impact through deployed applications and emerging AI technologies. Case studies of deployed applications with measurable benefits arising from the use of AI technology provide clear evidence of the impact and value of AI technology to today's world. The emerging applications track features technologies that are rapidly maturing to the point of application. The seven articles selected for this special issue are extended versions of the papers that appeared at the conference. Four of the articles describe deployed applications that are already in use in the field.
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 ; Collins ). 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.