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An argument that qualitative representations—symbolic representations that carve continuous phenomena into meaningful units—are central to human cognition. In this book, Kenneth Forbus proposes that qualitative representations hold the key to one of the deepest mysteries of cognitive science: how we reason and learn about the continuous phenomena surrounding us. Forbus argues that qualitative representations—symbolic representations that carve continuous phenomena into meaningful units—are central to human cognition. Qualitative representations provide a basis for commonsense reasoning, because they enable practical reasoning with very little data; this makes qualitative representations a useful component of natural language semantics. Qualitative representations also provide a foundation for expert reasoning in science and engineering by making explicit the broad categories of things that might happen and enabling causal models that help guide the application of more quantitative knowledge as needed. Qualitative representations are important for creating more human-like artificial intelligence systems with capabilities for spatial reasoning, vision, question answering, and understanding natural language. Forbus discusses, among other topics, basic ideas of knowledge representation and reasoning; qualitative process theory; qualitative simulation and reasoning about change; compositional modeling; qualitative spatial reasoning; and learning and conceptual change. His argument is notable both for presenting an approach to qualitative reasoning in which analogical reasoning and learning play crucial roles and for marshaling a wide variety of evidence, including the performance of AI systems. Cognitive scientists will find Forbus's account of qualitative representations illuminating; AI scientists will value Forbus's new approach to qualitative representations and the overview he offers.
This book develops, for the first time, a qualitative model for the representation of spatial knowledge based only on locative relations between the objects involved. The core of this book is devoted to the study of qualitative inference methods that take into account the rich structure of space. These methods can be applied to quite a number of areas characterized by uncertain or incomplete knowledge, as for example geographic information systems, robot control, computer-aided architectural design, and natural language information systems.
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.
This book constitutes the refereed proceedings of the Second International Workshop on Biologically Motivated Computer Vision, BMCV 2002, held in Tübingen, Germany, in November 2002. The 22 revised full papers and 37 revised short papers presented together with 6 invited papers were carefully reviewed and selected from 97 submissions. The papers are organized in topical sections on neurons and features, motion, mid-level vision, recognition - from scenes to neurons, attention, robotics, and cognitive vision.
Handbook of Knowledge Representation describes the essential foundations of Knowledge Representation, which lies at the core of Artificial Intelligence (AI). The book provides an up-to-date review of twenty-five key topics in knowledge representation, written by the leaders of each field. It includes a tutorial background and cutting-edge developments, as well as applications of Knowledge Representation in a variety of AI systems. This handbook is organized into three parts. Part I deals with general methods in Knowledge Representation and reasoning and covers such topics as classical logic in Knowledge Representation; satisfiability solvers; description logics; constraint programming; conceptual graphs; nonmonotonic reasoning; model-based problem solving; and Bayesian networks. Part II focuses on classes of knowledge and specialized representations, with chapters on temporal representation and reasoning; spatial and physical reasoning; reasoning about knowledge and belief; temporal action logics; and nonmonotonic causal logic. Part III discusses Knowledge Representation in applications such as question answering; the semantic web; automated planning; cognitive robotics; multi-agent systems; and knowledge engineering. This book is an essential resource for graduate students, researchers, and practitioners in knowledge representation and AI. * Make your computer smarter* Handle qualitative and uncertain information* Improve computational tractability to solve your problems easily
Rather than being a how-to book, this volume examines the ideas and practices of qualitative research in terms of their applicability for an understanding and explanation of the place of qualitative research in the social sciences.
Digital culture and digital technologies have rapidly become unavoidable and essential forms of social experience and communication in our emerging globalised society. If we want to attempt to analyse and understand our technology-saturated society, and all its new media, then we must also develop research methods and forms of analysis that can accommodate and exploit digital culture and digital technologies. This important new methods text sets out to equip qualitative researchers with the tools necessary to conduct ethnography in the age of email and the internet. It will investigate how digital technologies potentially transform the ways in which we do research. This text also introduces the reader to new emerging methods that utilise new technologies and explains how to conduct data collection, analysis and representation using new technologies and `hypermedia′. Essential reading for any student or researcher interested in qualitative research in an age of hypermedia, this text: - explains how digital technology impacts on social research; - investigates how digital technology has reshaped the field of social research; - consider the implications of bringing multimedia into the forefront of qualitative research; - suggests new ways of observing and documenting a `technologised′ and design-rich society; - enables the reader to use new technologies to handle and represent qualitative data; - unpacks the theoretical implications of writing and researching for the electronic screen
formation. The basic ideas underlying knowledge visualization and information vi- alization are outlined. In a short preview of the contributions of this volume, the idea behind each approach and its contribution to the goals of the book are outlined. 2 The Basic Concepts of the Book Three basic concepts are the focus of this book: "data", "information", and "kno- edge". There have been numerous attempts to define the terms "data", "information", and "knowledge", among them, the OTEC Homepage "Data, Information, Kno- edge, and Wisdom" (Bellinger, Castro, & Mills, see http://www.syste- thinking.org/dikw/dikw.htm): Data are raw. They are symbols or isolated and non-interpreted facts. Data rep- sent a fact or statement of event without any relation to other data. Data simply exists and has no significance beyond its existence (in and of itself). It can exist in any form, usable or not. It does not have meaning of itself.
Knowledge about distances---along with knowledge about spatial direction---is one of the most important fundamentals for a cognitive agent's orientation, navigation, and route planning. However, only some distances can be perceived directly. Therefore, knowledge about distances must often be inferred from other sources of information. In cognitive science research on spatial cognition, this is investigated in different ways, using empirical studies, computer simulations, and knowledge representation approaches. This book presents a comprehensive interdisciplinary study of human distance cognition. It discusses results on knowledge about distances from artificial intelligence research and cognitive psychology, proposing an integrating formal framework. Focusing on knowledge about route distances, it then presents a computational model of the way in which humans infer knowledge about distances in environmental spaces like cities or buildings.
Problem solving is implicit in the very nature of all science, and virtually all scientists are hired, retained, and rewarded for solving problems. Although the need for skilled problem solvers has never been greater, there is a growing disconnect between the need for problem solvers and the educational capacity to prepare them. Learning to Solve Complex Scientific Problems is an immensely useful read offering the insights of cognitive scientists, engineers and science educators who explain methods for helping students solve the complexities of everyday, scientific problems. Important features of this volume include discussions on: *how problems are represented by the problem solvers and how perception, attention, memory, and various forms of reasoning impact the management of information and the search for solutions; *how academics have applied lessons from cognitive science to better prepare students to solve complex scientific problems; *gender issues in science and engineering classrooms; and *questions to guide future problem-solving research. The innovative methods explored in this practical volume will be of significant value to science and engineering educators and researchers, as well as to instructional designers.