Qualitative reasoning is one of the most vigorous areas in artificial intelligence. This book presents, within a conceptually unified theoretical framework, a body of methods that have been developed over the past fifteen years for building and simulating qualitative models of physical systems (bathtubs, tea kettles, automobiles, the physiology of the body, chemical processing plants, control systems, electrical circuits, and the like) where knowledge of that system is incomplete. The primary tool for this work is the author's QSIM algorithm which is discussed in detail.
Qualitative models are more able than traditional models to express states of incomplete knowledge about continuous mechanisms. Qualitative simulation guarantees to find all possible behaviors consistent with the knowledge in the model. This expressive power and coverage are important in problem-solving for diagnosis, design, monitoring, and explanation.
The framework is built around the QSIM algorithm for qualitative simulation, and the QSIM representation for qualitative differential equations, both of which are carefully grounded in continuous mathematics. Qualititative simulation draws on a wide range of mathematical methods to keep a complete set of predictions tractable, including the use of partial quantitative information. Compositional modeling and component-connection methods for building qualitative models are also discussed in detail.
Qualitative Reasoning is primarily intended for advanced students and researchers in AI or its applications. Scientists and engineers who have had a solid introduction to AI, however, will be able to use this book for self instruction in qualitative modeling and simulation methods.
This book is a major contribution to the field. For the student, it is the best introduction to qualitative simulation that has been written. For the researcher, it summarizes more than 10 years of work on a very successful and productive research project.
The book is a sparkling discourse that will engage the reader. The many recent references offer a solid basis for anyone beginning an adventure in modeling and simulation with incomplete knowledge.
Kuipers sets himself the twin goals of describing his research and writing a textbook. He succeeds brilliantly at the first goal. The book is well organized, well reasoned, well written, and well illustrated. The ideas are clearly stated, the formalization is concise and precise, and the algorithms are explained in detail. The numerous examples and figures illustrate every aspect of the research. ... The clear and detailed exposition makes it a fine course text when supplemented on a few topics.
This book may become a very important book for Geographic Systems and for geography in general, despite the fact, that it does not discuss anything geographical or spatial in its 400 pages. ... It must be hoped that many geographers use the methods to model geographical processes and to explore the dynamic behavior of systems in physical and human geography. Many M.Sc. or Ph.D. thesis could benefit from the rigor of the method and the application of the software. Modeling of dynamic systems - a la Forrester's Urban Dynamics - become feasible, even in the absence of detailed quantitative knowledge.
Overall the book is extremely well written. The book assumes, and requires, a fairly strong knowledge of AI; one beyond that provided by a typical introductory undergraduate course. For those with the relevant background, or strong motivation, the book will be an excellent resource. It has been written as a textbook with thought-provoking practice and research problems at the end of each chapter. The organization of chapters is excellent, starting with a gentle introduction and then increasing in detail and complexity, while building on the material of earlier chapters. Given the significant advances and successes in the field of qualitative reasoning this book is a very timely one and I would recommend it to anyone with an interest in AI, in general, and modeling and simulation, in particular.