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 special issue arose out of a symposium on metaphor and artificial intelligence in which the main orientation was computational models and psychological processing models of metaphorical understanding. The papers in this issue discuss: *implemented computational systems for handling different aspects of metaphor understanding; *how metaphor can be accommodated in accepted logical representational frameworks; *psychological processes involved in metaphor understanding; and *the cross-linguistic cognitive reality of conceptual metaphors.

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.

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.

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.

Natural language understanding systems require a knowledge base provided with formal representations reflecting the structure of human beings' cognitive system. Although surface semantics can be sufficient in some other systems, the construction of a robust knowledge base guarantees its use in most natural language processing applications, thus consolidating the concept of resource reuse. This conference deals with meaning and knowledge representation in the context of natural language understanding from the perspective of theoretical linguistics, computational linguistics, cognitive science, knowledge engineering, artificial intelligence, natural language processing, text analytics or linked data and semantic web technologies.