Modeling and Simulation with Incomplete Knowledge
A body of methods that have been developed for building and simulating qualitative models of physical systems where knowledge of that system is incomplete.
This book presents, within a conceptually unified theoretical framework, a body of methods that have been developed 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.