Synopses & Reviews
Complex machines are used, understood and repaired by people with essentially no formal training in physics and engineering-although the design and manufacture of such machines requires a deep knowledge of these subjects, and advanced mathematical reasoning. How is this possible? The way people understand these devices, the conceptual frameworks or "mental models" which they use, must be different from the differential equation based descriptions of the world taught in school; people's models are acquired informally, are largely qualitative, use causal reasoning, and relate directly to the language we speak every day. They are crude, but successful.This collection of articles, which constituted a special issue of the Journal of Artificial Intelligence, presents the most recent work on qualitative reasoning about the real (physical) world. A common theme of all the contributions is explaining how physical systems work-from heat flow to transistors to digital computation. The explanations are so detailed and exact that they can be used by computer programs to reason about physical work in the same kinds of ways that people do.This rapidly developing area of cognitive science, variously called Qualitative or Naive Physics, is of direct psychological interest and has strong connections to theories of linguistic semantics. But there are also immediate technological applications, with much of this work funded by industry in the expectation of building computer systems which can communicate sensibly with people about physical mechanisms.Daniel G. Bobrow is a Research Fellow in the Intelligent Systems Laboratory, Xerox Palo Alto Research Center, editor-in-chief of the Journal of Artificial Intelligence, and Chair of the Governing Board of the Cognitive Science Society.This book inaugurates the series Computational Models of Cognition and Perception.A Bradford Book.
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 systems - 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 better 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 is important in problem solving for diagnosis, design, monitoring, explanation, and other applications of artificial intelligence.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. Qualitative 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.Artificial Intelligence series