This series will include monographs and collections of studies devoted to the investigation and exploration of knowledge, information, and data processing systems of all kinds, no matter whether human, (other) animal, or machine. Its scope is intended to span the full range of interests from classical problems in the philosophy of mind and philosophical psychol ogy through issues in cognitive psychology and sociobiology (concerning the mental capabilities of other species) to ideas related to artificial intelligence and computer science. While primary emphasis will be placed upon theoretical, conceptual, and epistemological aspects of these problems and domains, empirical, experimental, and methodological studies will also appear from time to time. The problems posed by metaphor and analogy are among the most challenging that confront the field of knowledge representation. In this study, Eileen Way has drawn upon the combined resources of philosophy, psychology, and computer science in developing a systematic and illuminating theoretical framework for understanding metaphors and analogies. While her work provides solutions to difficult problems of knowledge representation, it goes much further by investigating some of the most important philosophical assumptions that prevail within artificial intelligence today. By exposing the limitations inherent in the assumption that languages are both literal and truth-functional, she has advanced our grasp of the nature of language itself. J.R.F.

This is an analysis of the philosophical assumptions and implications of current artificial intelligence (AI) representation schemes, particularly those dealing with the underlying cognitive processes of language. The work attacks the traditional, logic-based view of language and knowledge representation and argues that cognitive mechanisms provide a better model for structuring knowledge that that of first-order logic. The author explains her dynamic type hierarchy theory, a new approach to metaphor, language and knowledge representation.

Knowledge representation is fundamental to the study of mind. All theories of psychological processing are rooted in assumptions about how information is stored. These assumptions, in turn, influence the explanatory power of theories. This book fills a gap in the existing literature by providing an overview of types of knowledge representation techniques and their use in cognitive models. Organized around types of representations, this book begins with a discussion of the foundations of knowledge representation, then presents discussions of different ways that knowledge representation has been used. Both symbolic and connectionist approaches to representation are discussed and a set of recommendations about the way representations should be used is presented. This work can be used as the basis for a course on knowledge representation or can be read independently. It will be useful to students of psychology as well as people in related disciplines--computer science, philosophy, anthropology, and linguistics--who want an introduction to techniques for knowledge representation.

Proceedings held May 1989. Topics include temporal logic, hierarchical knowledge bases, default theories, nonmonotonic and analogical reasoning, formal theories of belief revision, and metareasoning. Annotation copyright Book News, Inc. Portland, Or.

This volume brings together the work of researchers from various disciplines where aspects of descriptive, mathematical, computational or design knowledge concerning metaphor and analogy, especially in the context of agents, have emerged. The book originates from an international workshop on Computation for Metaphors, Analogy, and Agents (CMAA), held in Aizu, Japan in April 1998. The 19 carefully reviewed and revised papers presented together with an introduction by the volume editor are organized into sections on Metaphor and Blending, Embodiment, Interaction, Imitation, Situated Mapping in Space and Time, Algebraic Engineering: Respecting Structure, and a Sea-Change in Viewpoints.

Mind, Machine, and Metaphor is a rich, original, and wide-ranging view of legal theory in the context of artificial intelligence (AI) research. It is essential reading for legal theorists and for legal scholars and students of AI with an interest in each other's fields.

Metaphor lies at the heart of the contemporary debate in aesthetics, semantics and the philosophy of science. It is generally recognised now that metaphor is not an obfuscation of the truth (as so many philosophers since Plato have argued); on the contrary, it is essential that we consider metaphor if we strive for an optimal understanding of how truth is gained both in science and in our everyday dealings with reality. Hence, metaphor is not of interest only for the literary theorists, but for all those who wish to understand science and how to grasp the structure of our social world. This volume presents eleven essays on the role of metaphor in philosophy, poetry, semiotics, art, literary criticism, economics, medical science and in political theory. Through the use of metaphor, the contributors provide a unique and exciting picture of these disciplines.