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 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.

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.

An engaging introduction to data science that emphasizes critical thinking over statistical techniques An introduction to data science or statistics shouldn’t involve proving complex theorems or memorizing obscure terms and formulas, but that is exactly what most introductory quantitative textbooks emphasize. In contrast, Thinking Clearly with Data focuses, first and foremost, on critical thinking and conceptual understanding in order to teach students how to be better consumers and analysts of the kinds of quantitative information and arguments that they will encounter throughout their lives. Among much else, the book teaches how to assess whether an observed relationship in data reflects a genuine relationship in the world and, if so, whether it is causal; how to make the most informative comparisons for answering questions; what questions to ask others who are making arguments using quantitative evidence; which statistics are particularly informative or misleading; how quantitative evidence should and shouldn’t influence decision-making; and how to make better decisions by using moral values as well as data. Filled with real-world examples, the book shows how its thinking tools apply to problems in a wide variety of subjects, including elections, civil conflict, crime, terrorism, financial crises, health care, sports, music, and space travel. Above all else, Thinking Clearly with Data demonstrates why, despite the many benefits of our data-driven age, data can never be a substitute for thinking. An ideal textbook for introductory quantitative methods courses in data science, statistics, political science, economics, psychology, sociology, public policy, and other fields Introduces the basic toolkit of data analysis—including sampling, hypothesis testing, Bayesian inference, regression, experiments, instrumental variables, differences in differences, and regression discontinuity Uses real-world examples and data from a wide variety of subjects Includes practice questions and data exercises

To endow computers with common sense is one of the major long-term goals of Artificial Intelligence research. One approach to this problem is to formalize commonsense reasoning using mathematical logic. Commonsense Reasoning is a detailed, high-level reference on logic-based commonsense reasoning. It uses the event calculus, a highly powerful and usable tool for commonsense reasoning, which Erik T. Mueller demonstrates as the most effective tool for the broadest range of applications. He provides an up-to-date work promoting the use of the event calculus for commonsense reasoning, and bringing into one place information scattered across many books and papers. Mueller shares the knowledge gained in using the event calculus and extends the literature with detailed event calculus solutions to problems that span many areas of the commonsense world. - Covers key areas of commonsense reasoning including action, change, defaults, space, and mental states. - The first full book on commonsense reasoning to use the event calculus. - Contextualizes the event calculus within the framework of commonsense reasoning, introducing the event calculus as the best method overall. - Focuses on how to use the event calculus formalism to perform commonsense reasoning, while existing papers and books examine the formalisms themselves. - Includes fully worked out proofs and circumscriptions for every example.

This book is a collection of some of the papers that were presented during a NATO Advanced Research Workshop (ARW) on "Intelligent Systems: Safety, Reliability and Maintainability Issues" that was held in Kusadasi, Turkey during August 24- 28, 1992. Attendance at this workshop was mainly by invitation only, drawing people internationally representing industry, government and the academic community. Many of the participants were internationally recognized leaders in the topic of the workshop. The purpose of the ARW was to bring together a highly distinguished group of people with the express purpose of debating where the issues of safety, reliability and maintainability place direct and tangible constraints on the development of intelligent systems. As a consequence, one of the major debating points in the ARW was the definition of intelligence, intelligent behaviour and their relation to complex dynamic systems. Two major conclusions evolved from the ARW are: 1. A continued need exists to develop formal, theoretical frameworks for the architecture of such systems, together with a reflection on the concept of intelligence. 2. There is a need to focus greater attention to the role that the human play in controlling intelligent systems. The workshop began by considering the typical features of an intelligent system. The complexity associated with multi-resolutional architectures was then discussed, leading to the identification of a necessity for the use of a combinatorial synthesis/approach. This was followed by a session on human interface issues.

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.

Making Sense of Numbers teaches students the skills they need to be both consumers and producers of quantitative research: able to read about, collect, calculate, and communicate numeric information for both everyday tasks and school or work assignments. The text teaches how to avoid making common errors of reasoning, calculation, or interpretation by introducing a systematic approach to working with numbers, showing students how to figure out what a particular number means. The text also demonstrates why it is important to apply a healthy dose of skepticism to the numbers we all encounter, so that we can understand how those numbers can (and cannot) be interpreted in their real-world context. Jane E. Miller uses annotated examples on a wide variety of topics to illustrate how to use new terms, concepts, and approaches to working with numbers. End-of-chapter engagement activities designed based on Miller’s three decades of teaching experience can be used in class or as homework assignments, with some for students to do individually and others intended for group discussion. The book is ideally suited for a range of courses, including quantitative reasoning, research methods, basic statistics, data analysis, and communicating quantitative information. An instructor website for the book includes a test bank, editable PowerPoint slides, and tables and figures from the book.

Formal ways of representing uncertainty and various logics for reasoning about it; updated with new material on weighted probability measures, complexity-theoretic considerations, and other topics. In order to deal with uncertainty intelligently, we need to be able to represent it and reason about it. In this book, Joseph Halpern examines formal ways of representing uncertainty and considers various logics for reasoning about it. While the ideas presented are formalized in terms of definitions and theorems, the emphasis is on the philosophy of representing and reasoning about uncertainty. Halpern surveys possible formal systems for representing uncertainty, including probability measures, possibility measures, and plausibility measures; considers the updating of beliefs based on changing information and the relation to Bayes' theorem; and discusses qualitative, quantitative, and plausibilistic Bayesian networks. This second edition has been updated to reflect Halpern's recent research. New material includes a consideration of weighted probability measures and how they can be used in decision making; analyses of the Doomsday argument and the Sleeping Beauty problem; modeling games with imperfect recall using the runs-and-systems approach; a discussion of complexity-theoretic considerations; the application of first-order conditional logic to security. Reasoning about Uncertainty is accessible and relevant to researchers and students in many fields, including computer science, artificial intelligence, economics (particularly game theory), mathematics, philosophy, and statistics.

Designing Social Inquiry focuses on improving qualitative research, where numerical measurement is either impossible or undesirable. What are the right questions to ask? How should you define and make inferences about causal effects? How can you avoid bias? How many cases do you need, and how should they be selected? What are the consequences of unavoidable problems in qualitative research, such as measurement error, incomplete information, or omitted variables? What are proper ways to estimate and report the uncertainty of your conclusions?