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.

Geographic Information Systems are an essential tool for analyzing and representing quantitative spatial data. Qualitative GIS explains the recent integration of qualitative research with Geographical Information Systems With a detailed contextualising introduction, the text is organised in three sections: Representation: examines how researchers are using GIS to create new types of representations; working with spatial data, maps, and othervisualizations to incorporate multiple meanings and to provide texture and context. Analysis: discusses the new techniques of analysis that are emerging at the margins between qualitative research and GIS, this in the wider context of a critical review of mixed-methods in geographical research Theory: questions how knowledge is produced, showing how ideas of ′science′ and ′truth′ inform research, and demonstrates how qualitative GIS can be used to interrogate discussions of power, community, and social action Making reference to representation, analysis, and theory throughout, the text shows how to frame questions, collect data, analyze results, and represent findings in a truly integrated way. An important addition to the mixed methods literature, Qualitative GIS will be the standard reference for upper-level students and researchers using qualitative methods and Geographic Information Systems.

Reflecting on the practice of qualitative literacy research, this book presents 14 essays that address the most pressing questions faced by qualitative researchers today: how to represent others and themselves in research narratives; how to address ethical dilemmas in research-participant relations; and how to deal with various rhetorical, institutional, and historical constraints on research. After a foreword ("Considering Research Methods in Composition and Rhetoric" by Andrea A. Lunsford and others) and an introduction ("Reflections on Methodology in Literacy Studies" by the editors), essays in the book are (1) "Seduction and Betrayal in Qualitative Research" (Thomas Newkirk); (2) "Still-Life: Representations and Silences in the Participant-Observer Role" (Brenda Jo Brueggemann); (3) "Dealing with the Data: Ethical Issues in Case Study Research" (Cheri L. Williams); (4) "'Everything's Negotiable': Collaboration and Conflict in Composition Research" (Russel K. Durst and Sherry Cook Stanforth); (5) "Dilemmas of Fidelity: Qualitative Research in the Classroom" (Helen Dale); (6) "Ethnography and the Problem of the 'Other'" (Patricia A. Sullivan); (7) "Turning in upon Ourselves: Positionality, Subjectivity, and Reflexivity in Case Study and Ethnographic Research" (Elizabeth Chiseri-Strater); (8) "Constructing Voices in Writing Research: Developing Participatory Approaches to Situated Inquiry" (Ann M. Blakeslee and others); (9) "A Text for Many Voices: Representing Diversity in Reports of Naturalistic Research" (Lucille Parkinson McCarthy and Stephen M. Fishman); (10) "Culture on the Page: Experience, Rhetoric, and Aesthetics in Ethnographic Writing" (Bonnie S. Sunstein); (11) "Engendering Ethnography: Insights from the Feminist Critique of Postmodern Anthropology" (Roxanne D. Mountford); (12) "Writing, Rap, and Representation: Problematic Links between Texts and Experience" (Jabari Mahiri); (13) "Social and Institutional Power Relationships in Studies of Workplace Writing" (Jennie Dautermann); and (14) "Ethics, Institutional Review Boards, and the Involvement of Human Participants in Composition Research" (Paul V. Anderson). An afterword ("Ethics and Representation in Teacher Research" by Ruth E. Ray) is attached. Each chapter contains references. (RS)

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.

SAGE has been a major force shaping the field of qualitative methods, not just in its specialist methods journals like Qualitative Inquiry but in the ′empirical′ journals such as Social Studies of Science. Delving into SAGE′s deep backlist of qualitative research methods journals, Paul Atkinson and Sara Delmont, editors of Qualitative Research, have selected over 70 articles to represent SAGE′s distinctive contribution to methods publishing in general and qualitative research in particular. This collection includes research from the past four decades and addresses key issues or controversies, such as: explanations and defences of qualitative methods; ethics; research questions and foreshadowed problems; access; first days in the field; field roles and rapport; practicalities of data collection and recording; data analysis; writing and (re) presentation; the rise of auto-ethnography; life history, narrative and autobiography; CA and DA; and alternatives to the logocentric (such as visual methods).

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.