The ability to reason qualitatively about physical systems is important to understanding and interacting with the world for both humans and intelligent machines. Accordingly, this study has become an important subject of research in the artificial intelligence and cognitive science communities. The goal of "qualitative physics," as the field is sometimes known, is to capture both the commonsense knowledge of the person on the street and the tacit knowledge underlying the quantitative knowledge used by engineers and scientists. "Readings in Qualitative Reasoning About Physical Systems" is an introduction and source book for this dynamic area, presenting reprints of key papers chosen by the editors and a group of expert referees. The editors present introductions discussing the context and significance of each group of articles as well as providing pointers to the rest of the literature. In addition, the volume includes several original papers that are not available elsewhere.

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.

Readings in Qualitative Reasoning about Physical Systems describes the automated reasoning about the physical world using qualitative representations. This text is divided into nine chapters, each focusing on some aspect of qualitative physics. The first chapter deal with qualitative physics, which is concerned with representing and reasoning about the physical world. The goal of qualitative physics is to capture both the commonsense knowledge of the person on the street and the tacit knowledge underlying the quantitative knowledge used by engineers and scientists. The succeeding chapter discusses the qualitative calculus and its role in constructing an envisionment that includes behavior over both mythical time and elapsed time. These topics are followed by reviews of the mathematical aspects of qualitative reasoning, history-based simulation and temporal reasoning, as well as the intelligence in scientific computing. The final chapters are devoted to automated modeling for qualitative reasoning and causal explanations of behavior. These chapters also examine the qualitative kinematics of reasoning about shape and space. This book will prove useful to psychologists and psychiatrists.

This volume brings together current work on qualitative reasoning. Its publication reflects the maturity of qualitative reasoning as a research area and the growing interest in problems of reasoning about physical systems.The papers present knowledge bases for a number of very different domains, including heat flow, transistors, and digital computation. A common theme of all these papers is explaining how physical systems work. An important shared criterion is that the behavioral description must be compositional, that is the description of a system's behavior must be derivable from the structure of the system.This material should be of interest to anyone concerned with automated reasoning about the real (physical) world.

The book provides a survey about the field of Qualitative Reasoning, it contrasts and classifies its approaches and puts them into a common framework. Qualitative Reasoning represents an approach of Artificial Intelligence to model dynamic systems, about which little information is available, and to derive statements about the potential behavior of these systems, putting emphasis on a causal explanation of the behavior. Both variables and relationships between variables are described by means of qualitative terms such as small and large or positive and negative. Since this approach also takes into consideration the way how humans reason about physical systems, it can be stated that Qualitative Reasoning participates in the creation of a cognitive theory of non-numerical process descriptions which can be mapped onto a digital computer. This approach can be used for simulation, diagnosis, design, structure identification and interpretation. Areas of application are physics, medicine, the field of ecology, process control, etc. In addition to the classification of existing methods, the book presents a new approach based on fuzzy sets. And the work relates Qualitative Reasoning with such fields of Expert Systems, System Theory and Cognitive Science.

For a meaningful understanding of physics, it is necessary to realise that this corpus of knowledge operates in a register different from natural thought. This book aims at situating the main trends of common reasoning in physics with respect to some essential aspects of accepted theory. It analyses a great many research results based on studies of pupils and students at various academic levels, involving a range of physical situations. It shows the impressive generality of the trends of common thought, as well as their resistance to teaching. The book's main focus is to underline to what extent natural thought is organised. As a result of this mapping out of trends of reasoning, some suggestions for teaching are presented; these have already influenced recent curricula in France. This book is intended for teachers and teacher trainers principally, but students can also benefit from it to improve their understanding of physics and of their own ways of reasoning.

The book provides a survey about the field of Qualitative Reasoning, it contrasts and classifies its approaches and puts them into a common framework. Qualitative Reasoning represents an approach of Artificial Intelligence to model dynamic systems, about which little information is available, and to derive statements about the potential behavior of these systems, putting emphasis on a causal explanation of the behavior. Both variables and relationships between variables are described by means of qualitative terms such as small and large or positive and negative. Since this approach also takes into consideration the way how humans reason about physical systems, it can be stated that Qualitative Reasoning participates in the creation of a cognitive theory of non-numerical process descriptions which can be mapped onto a digital computer. This approach can be used for simulation, diagnosis, design, structure identification and interpretation. Areas of application are physics, medicine, the field of ecology, process control, etc. In addition to the classification of existing methods, the book presents a new approach based on fuzzy sets. And the work relates Qualitative Reasoning with such fields of Expert Systems, System Theory and Cognitive Science.

In this book, a methodology integrating qualitative reasoning and bond graphs is developed to construct intelligent supervisory control systems. Qualitative reasoning is a powerful model-based reasoning method while bond graphs are a formal modelling language for dynamic systems. Their integration and qualitative reasoning on bond graphs results in a problem-solving approach to artificial intelligence, in which qualitative reasoning is used as the general reasoning strategy and bond graphs are employed as the knowledge representation.A systematic modelling procedure based on qualitative bond graphs is presented. A controller design method is developed to derive control algorithms from qualitative bond graph models. An auto-tuning scheme is proposed to adjust the controllers in order to meet performance criteria and adapt to system changes. A fault diagnosis mechanism is built to localise system faults, and an additional measurement suggestion method is developed for the diagnosis result refinement. An automatic planner is proposed to generate the operation sequences for system start-up, shut-down, and emergency measures to help human operators operate systems safely. All of these applications are combined together via a management mechanism to construct a supervisory control system.

This book contains twenty-eight papers by participants in the NATO Advanced Study Institute (ASI) on "Cognitive and Linguistic Aspects of Geographic Space," held in Las Navas del Maxques, Spain, July 8-20, 1990. The NATO ASI marked a stage in a two-year research project at the U. S. National Center for Geographic Infonnation and Analysis (NCOIA). In 1987, the U. S. National Science Foundation issued a solicitation for proposals to establish the NCGIA-and one element of that solicitation was a call for research on a "fundamental theory of spatial relations". We felt that such a fundamental theory could be searched for in mathematics (geometry, topology) or in cognitive science, but that a simultaneous search in these two seemingly disparate research areas might produce novel results. Thus, as part of the NCGIA proposal from a consortium consisting of the University of California at Santa Barbara, the State University of New York at Buffalo, and the University of Maine, we proposed that the second major Research Initiative (two year, multidisciplinary research project) of the NCOIA would address these issues, and would be called "Languages of Spatial Relations" The grant to establish the NCOIA was awarded to our consortium late in 1988.