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This book contains an introduction to symbolic logic and a thorough discussion of mechanical theorem proving and its applications. The book consists of three major parts. Chapters 2 and 3 constitute an introduction to symbolic logic. Chapters 4-9 introduce several techniques in mechanical theorem proving, and Chapters 10 an 11 show how theorem proving can be applied to various areas such as question answering, problem solving, program analysis, and program synthesis.

Artificial Intelligence provides information pertinent to the fundamental aspects of artificial intelligence. This book presents the basic mathematical and computational approaches to problems in the artificial intelligence field. Organized into four parts encompassing 16 chapters, this book begins with an overview of the various fields of artificial intelligence. This text then attempts to connect artificial intelligence problems to some of the notions of computability and abstract computing devices. Other chapters consider the general notion of computability, with focus on the interaction between computability theory and artificial intelligence. This book discusses as well the concepts of pattern recognition, problem solving, and machine comprehension. The final chapter deals with the study of machine comprehension and reviews the fundamental mathematical and computing techniques underlying artificial intelligence research. This book is a valuable resource for seniors and graduate students in any of the computer-related sciences, or in experimental psychology. Psychologists, general systems theorists, and scientists will also find this book useful.

Fundamental Studies in Computer Science, 3: Conceptual Information Processing discusses a theory of natural language and implementation of that theory on a computer, focusing basically on an Artificial Intelligence approach to linguistics. This book aims to write computer programs that could understand and generate sentences, which is intended as a first step towards the long range goal of a computer that can communicate with people in natural language. The topics covered include computational linguistics, conceptual dependency theory, history of the analyzer, representation in memory, and structure of BABEL. This publication is a good reference for researchers and specialists working in the field of computer science.

First Published in 1977. In the summer of 1971, there was a workshop in an ill-defined field at the intersection of psychology, artificial intelligence, and linguistics. The fifteen participants were in various ways interested in the representation of large systems of knowledge (or beliefs) based upon an understanding process operating upon information expressed in natural language. This book reflects a convergence of interests at the intersection of psychology and artificial intelligence. What is the nature of knowledge and how is this knowledge used? These questions lie at the core of both psychology and artificial intelligence.

Associative Networks: Representation and Use of Knowledge by Computers is a collection of papers that deals with knowledge base of programs exhibiting some operational aspects of understanding. One paper reviews network formalism that utilizes unobstructed semantics, independent of the domain to which it is applied, that is also capable of handling significant epistemological relationships of concept structuring, attribute/value inheritance, multiple descriptions. Another paper explains network notations that encode taxonomic information; general statements involving quantification; information about processes and procedures; the delineation of local contexts, as well as the relationships between syntactic units and their interpretations. One paper shows that networks can be designed to be intuitively and formally interpretable. Network formalisms are computer-oriented logics which become distinctly significant when access paths from concepts to propositions are built into them. One feature of a topical network organization is its potential for learning. If one topic is too large, it could be broken down where groupings of propositions under the split topics are then based on'co-usage'statistics. As an example, one paper cites the University of Maryland artificial intelligence (AI) group which investigates the control and interaction of a meaning-based parser. The group also analyzes the inferences and predictions from a number of levels based on mundane inferences of actions and causes that can be used in AI. The collection can be useful for computer engineers, computer programmers, mathematicians, and researchers who are working on artificial intelligence.

The Handbook of Artificial Intelligence, Volume I focuses on the progress in artificial intelligence (AI) and its increasing applications, including parsing, grammars, and search methods. The book first elaborates on AI, AI handbook and literature, problem representation, search methods, and sample search programs. The text then ponders on representation of knowledge, including survey of representation techniques and representation schemes. The manuscript explores understanding natural languages, as well as machine translation, grammars, parsing, test generation, and natural language processing systems. The book also takes a look at understanding spoken language, including systems architecture and the ARPA SUR projects. The text is a valuable source of information for computer science experts and researchers interested in pursuing further research in artificial intelligence.

First published in 1981. This book has been written for those who want to comprehend how a large natural language-understanding program works. Thirty-five professionals in Cognitive Science, mostly psychologists by training, in a summer school were taught to grapple with the details of programming in Artificial Intelligence. As a part of the curriculum designed for this project the authors created what they called micro-programs. These micro-programs were an attempt to give students the flavor of using a large AI program without all the difficulty normally associated with learning a complex system written by another person. Using the authors'parser, ELI, or story understanding program, SAM, they also gave students the micro versions of these programs, which were very simple versions that operated in roughly the same way as their larger versions, but without all the frills. Students were asked to add pieces to the programs and otherwise modify them in order to learn how they worked.

The Handbook of Artificial Intelligence, Volume II focuses on the improvements in artificial intelligence (AI) and its increasing applications, including programming languages, intelligent CAI systems, and the employment of AI in medicine, science, and education. The book first elaborates on programming languages for AI research and applications-oriented AI research. Discussions cover scientific applications, teiresias, applications in chemistry, dependencies and assumptions, AI programming-language features, and LISP. The manuscript then examines applications-oriented AI research in medicine and education, including ICAI systems design, intelligent CAI systems, medical systems, and other applications of AI to education. The manuscript explores automatic programming, as well as the methods of program specification, basic approaches, and automatic programming systems. The book is a valuable source of data for computer science experts and researchers interested in conducting further research in artificial intelligence.

The Handbook of Artificial Intelligence, Volume I focuses on the progress in artificial intelligence (AI) and its increasing applications, including parsing, grammars, and search methods. The book first elaborates on AI, AI handbook and literature, problem representation, search methods, and sample search programs. The text then ponders on representation of knowledge, including survey of representation techniques and representation schemes. The manuscript explores understanding natural languages, as well as machine translation, grammars, parsing, test generation, and natural language processing systems. The book also takes a look at understanding spoken language, including systems architecture and the ARPA SUR projects. The text is a valuable source of information for computer science experts and researchers interested in pursuing further research in artificial intelligence

Neural Models of Language Processes offers an interdisciplinary approach to understanding the nature of human language and the means whereby we use it. The book is organized into five parts. Part I provides an opening framework that addresses three tasks: to place neurolinguistics in current perspective; to provide two case studies of aphasia; and to discuss the''rules of the game''of the various disciplines that contribute to this volume. Part II on artificial intelligence (AI) and processing models discusses the contribution of AI to neurolinguistics. The chapters in this section introduce three AI systems for language perception: the HWIM and HEARSAY systems that proceed from an acoustic input to a semantic interpretation of the utterance it represents, and Marcus9 system for parsing sentences presented in text. Studying these systems demonstrates the virtues of implemented or implementable models. Part III on linguistic and psycholinguistic perspectives includes studies such as nonaphasic language behavior and the linguistics and psycholinguistics of sign language. Part IV examines neurological perspectives such as the neuropathological basis of Broca's aphasia and the simulation of speech production without a computer. Part V on neuroscience and brain theory includes studies such as the histology, architectonics, and asymmetry of language areas; hierarchy and evolution in neurolinguistics; and perceptual-motor processes and the neural basis of language.

As the contributions to this book make clear, a fundamental change is taking place in the study of computational linguistics analogous to that which has taken place in the study of computer vision over the past few years and indicative of trends that are likely to affect future work in artificial intelligence generally. The first wave of efforts on machine translation and the formal mathematical study of parsing yielded little real insight into how natural language could be understood by computers or how computers could lead to an understanding of natural language. The current wave of research seeks both to include a wider and more realistic range of features found in human languages and to limit the dimensions of program goals. Some of the new programs embody for the first time constraints on human parsing which Chomsky has uncovered, for example. The isolation of constraints and the representations for their expression, rather than the design of mechanisms and ideas about process organization, is central to the work reported in this volume. And if present goals are somewhat less ambitious, they are also more realistic and more realizable. ContentsComputational Aspects of Discourse, Robert Berwick • Recognizing Intentions from Natural Language Utterances, James Allen • Cooperative Responses from a Portable Natural Language Data Base Query System, Jerrold Kaplan • Natural Language Generation as a Computational Problem: An Introduction, David McDonald • Focusing in the Comprehension of Definite Anaphor, Candace Sidner • So What Can We Talk About Now? Bonnie Webber • A Preface by David Israel relates these chapters to the general considerations of philosophers and psycholinguists

The ability to learn is one of the most fundamental attributes of intelligent behavior. Consequently, progress in the theory and computer modeling of learn­ ing processes is of great significance to fields concerned with understanding in­ telligence. Such fields include cognitive science, artificial intelligence, infor­ mation science, pattern recognition, psychology, education, epistemology, philosophy, and related disciplines. The recent observance of the silver anniversary of artificial intelligence has been heralded by a surge of interest in machine learning-both in building models of human learning and in understanding how machines might be endowed with the ability to learn. This renewed interest has spawned many new research projects and resulted in an increase in related scientific activities. In the summer of 1980, the First Machine Learning Workshop was held at Carnegie-Mellon University in Pittsburgh. In the same year, three consecutive issues of the Inter­ national Journal of Policy Analysis and Information Systems were specially devoted to machine learning (No. 2, 3 and 4, 1980). In the spring of 1981, a special issue of the SIGART Newsletter No. 76 reviewed current research projects in the field.. This book contains tutorial overviews and research papers representative of contemporary trends in the area of machine learning as viewed from an artificial intelligence perspective. As the first available text on this subject, it is intended to fulfill several needs.

Machine Learning: An Artificial Intelligence Approach contains tutorial overviews and research papers representative of trends in the area of machine learning as viewed from an artificial intelligence perspective. The book is organized into six parts. Part I provides an overview of machine learning and explains why machines should learn. Part II covers important issues affecting the design of learning programs—particularly programs that learn from examples. It also describes inductive learning systems. Part III deals with learning by analogy, by experimentation, and from experience. Parts IV and V discuss learning from observation and discovery, and learning from instruction, respectively. Part VI presents two studies on applied learning systems—one on the recovery of valuable information via inductive inference; the other on inducing models of simple algebraic skills from observed student performance in the context of the Leeds Modeling System (LMS). This book is intended for researchers in artificial intelligence, computer science, and cognitive psychology; students in artificial intelligence and related disciplines; and a diverse range of readers, including computer scientists, robotics experts, knowledge engineers, educators, philosophers, data analysts, psychologists, and electronic engineers.

The question,'What is Cognitive Science?'is often asked but seldom answered to anyone's satisfaction. Until now, most of the answers have come from the new breed of philosophers of mind. This book, however, is written by a distinguished psychologist and computer scientist who is well-known for his work on the conceptual foundations of cognitive science, and especially for his research on mental imagery, representation, and perception.In Computation and Cognition, Pylyshyn argues that computation must not be viewed as just a convenient metaphor for mental activity, but as a literal empirical hypothesis. Such a view must face a number of serious challenges. For example, it must address the question of'strong equivalents'of processes, and must empirically distinguish between phenomena which reveal what knowledge the organism has, phenomena which reveal properties of the biologically determined'functional architecture'of the mind. The principles and ideas Pylyshyn develops are applied to a number of contentious areas of cognitive science, including theories of vision and mental imagery. In illuminating such timely theoretical problems, he draws on insights from psychology, theoretical computer science, artificial intelligence, and psychology of mind.A Bradford Book

This volume brings together current work on qualitative reasoning. Its publication reflects the maturity of qualitative reasoning as a research area and the growing interest in problems of reasoning about physical systems.The papers present knowledge bases for a number of very different domains, including heat flow, transistors, and digital computation. A common theme of all these papers is explaining how physical systems work. An important shared criterion is that the behavioral description must be compositional, that is the description of a system's behavior must be derivable from the structure of the system.This material should be of interest to anyone concerned with automated reasoning about the real (physical) world.

This landmark work in computational linguistics is of great importance both theoretically and practically because it shows that much of English grammar can be learned by a simple program. The Acquisition of Syntactic Knowledge investigates the central questions of human and machine cognition: How do people learn language? How can we get a machine to learn language? It first presents an explicit computational model of language acquisition which can actually learn rules of English syntax given a sequence of grammatical, but otherwise unprepared, sentences. It shows that natural languages are designed to be easily learned and easily processed-an exciting breakthrough from the point of view of artificial intelligence and the design of expert systems because it shows how extensive knowledge might be acquired automatically, without outside intervention. Computationally, the book demonstrates how constraints that may be reasonably assumed to aid sentence processing also aid language acquisition. Chapters in the book's second part apply computational methods to the general problem of developmental growth, particularly the thorny problem of the interaction between innate genetic endowment and environmental input, with the intent of uncovering the constraints on the acquisition of syntactic knowledge. A number of'mini-theories'of learning are incorporated in this study of syntax with results that should appeal to a wide range of scholarly interests. These include how lexical categories, phonological rule systems, and phrase structure rules are learned; the role of semantic-syntactic interaction in language acquisition; how a'parameter setting'model may be formalized as a learning procedure; how multiple constraints (from syntax, thematic knowledge, or phrase structure) interact to aid acquisition; how transformational-type rules may be learned; and, the role of lexical ambiguity in language acquisition.The Acquisition of Syntactic Knowledge is sixteenth in the Artificial Intelligence Series, edited by Patrick Winston and Michael Brady.

Includes index.

This volume critically evaluates the present state of research in the domain of inferences in text processing and indicates new areas of research.The book is structured around the following theoretical aspects: - The representational aspect is concerned with the cognitive structure produced by the processed text, e.g. the social, spatial, and motor characteristics of world knowledge. - The procedural aspect investigates the time relationships on forming inferences, e.g. the point of time at which referential relations are constructed. - The contextual aspect reflects the dependence of inferences on the communicative embedding of text processing, e.g. on factors of modality and instruction.

The first full-scale history of cognitive science, this work addresses a central issue: What is the nature of knowledge?

Readings in Artificial Intelligence focuses on the principles, methodologies, advancements, and approaches involved in artificial intelligence. The selection first elaborates on representations of problems of reasoning about actions, a problem similarity approach to devising heuristics, and optimal search strategies for speech understanding control. Discussions focus on comparison with existing speech understanding systems, empirical comparisons of the different strategies, analysis of distance function approximation, problem similarity, problems of reasoning about action, search for solution in the reduction system, and relationship between the initial search space and the higher level search space. The book then examines consistency in networks of relations, non-resolution theorem proving, using rewriting rules for connection graphs to prove theorems, and closed world data bases. The manuscript tackles a truth maintenance system, elements of a plan-based theory of speech acts, and reasoning about knowledge and action. Topics include problems in reasoning about knowledge, integration knowledge and action, models of plans, compositional adequacy, truth maintenance mechanisms, dialectical arguments, and assumptions and the problem of control. The selection is a valuable reference for researchers wanting to explore the field of artificial intelligence.

Cognitive Sciences: Basic Problems, New Perspectives, and Implications for Artificial Intelligence presents models and theories that describe and analyze some selected topics in the cognitive sciences and their implications for artificial intelligence (AI). These topics range from problems of observability and its restrictions or distortions of the subjective perception of time, to visual perception, memory, and communication. Extensive use is made of fuzzy set theory. Comprised of six chapters, this volume begins with an introduction to the distortion of time perception and the relationship between objective and subjective time. An explanatory concept used here is that of a pre-event (being a candidate for an event to be stored in memory) and the concept of a dynamic event-representation of an object (events on events) generated by the perceiver in the process of perceptual work. The discussion then turns to the notion of an event that underlies the theory of time; the semantics of multimedial languages of verbal and non-verbal communication; and problems of the mechanisms underlying the formation of judgments, as well as the problems of expression of these judgments in forms ranging from simple answers to binary questions and the generation of texts or discourses. The book also considers memory and perception before concluding with a description of stochastic models of expertise formation, opinion change, and learning. This monograph will appeal to specialists in the fields of cognitive sciences and AI.

First published in 1986. The chapters in this collection are based on presentations made at the First Annual Workshop on Theoretical Issues in Conceptual Information Processing (TICIP) grew out of that. It was held in Atlanta, Georgia in March 1984 and included 50 people with roughly the same world view. In particular, the contributors were interested in content-based theories of conceptual information processing. Each chapter addresses some issue associated with the relationships between memory, experience and reasoning.

First Published in 1986. In the age of the computer, conjecture about things mechanical has naturally led to the question of whether machines can think. As the emphasis on Artificial Intelligence (AI) has grown rapidly, questions about machine intelligence have begun to have a certain urgency. The question we are concerned with in this book is: If we can find a set of processes that machines can slavishly follow, and if by so doing, these machines can come up with creative thoughts, what would that tell us about human beings? If the machine's procedure was adapted from a human procedure, that is, if all the machine was doing was what we know people are doing, would we abandon our inherent skepticism about the abilities of machines, or would we demystify our inherent admiration for things human? In a sense, these are the issues dealt with in this book. The author says in a sense because this book is no way a philosophical treatise. Rather it is an exercise in Artificial Intelligence and in Cognitive Science, it is an attempt to come to understand one of the most complex problems of mind by examining some of the mechanisms of mind: to define the apparatus that underlies our ability to think.

The volume applies to the study of the motor system the computational approach developed by David Marr for the visual system. Accordingly, understanding movement is viewed as an information processing problem, centred on the representation of appropriate computational structures. In particular, the book deals with the representation of objects, concurrent parallel processes, trajectory formation patterns and patterns of interaction with the environment.A number of modeling techniques are discussed, ranging from computational geometry to artificial intelligence, integrating very different aspects of movement, especially those which are not directly motoric.

Two psychologists, a computer scientist, and a philosopher have collaborated to present a framework for understanding processes of inductive reasoning and learning in organisms and machines. Theirs is the first major effort to bring the ideas of several disciplines to bear on a subject that has been a topic of investigation since the time of Socrates. The result is an integrated account that treats problem solving and induction in terms of rule­based mental models.Induction is included in the Computational Models of Cognition and Perception Series.A Bradford Book.

First Published in 1986. This book marks a watershed in cognitive science activity at Yale University. Over the past decade, the cognitive science orientation has become more and more integrated into the mainstream of cognitive psychology, and artificial intelligence workers now feel comfortable thinking about psychological experimentation. This book collects in one place the research work which concentrates on covering topics in the representation, processing, and recall of meaningful verbal.materials. Several of the chapters are first reports of research; others are specially prepared reviews and elaborations of research reported previously. Here it is all together: Studies of scripts, plans, and higher-level knowledge structures; analyses of knowledge structure activation, of autobiographical memory, of the phenomenon of reminding, of the summarization of text, of explanations for events, and more.

One of the currently most active research areas within Artificial Intelligence is the field of Machine Learning. which involves the study and development of computational models of learning processes. A major goal of research in this field is to build computers capable of improving their performance with practice and of acquiring knowledge on their own. The intent of this book is to provide a snapshot of this field through a broad. representative set of easily assimilated short papers. As such. this book is intended to complement the two volumes of Machine Learning: An Artificial Intelligence Approach (Morgan-Kaufman Publishers). which provide a smaller number of in-depth research papers. Each of the 77 papers in the present book summarizes a current research effort. and provides references to longer expositions appearing elsewhere. These papers cover a broad range of topics. including research on analogy. conceptual clustering. explanation-based generalization. incremental learning. inductive inference. learning apprentice systems. machine discovery. theoretical models of learning. and applications of machine learning methods. A subject index IS provided to assist in locating research related to specific topics. The majority of these papers were collected from the participants at the Third International Machine Learning Workshop. held June 24-26. 1985 at Skytop Lodge. Skytop. Pennsylvania. While the list of research projects covered is not exhaustive. we believe that it provides a representative sampling of the best ongoing work in the field. and a unique perspective on where the field is and where it is headed.

Machinery of Mind is a full-scale, indispensable examination of the history, content, politics, and philosophy of Artificial Intelligence. In detail, consecutively but with a keen awareness of the interdisciplinary nature of the field, Johnson traces the history of the burgeoning science from its earliest practitioners to corporation-funded engineers and experts: Roger Schank, Terry Winograd, Doug Lenat and others who are struggling to create machines that can actually think independently, going beyond man-made programs and'games.'Johnson presents the counterarguments of some theorists that intelligence is less than soul and that the'new science'moves arrogantly into dangerous territory. Necessarily inconclusive, this fascinating study is clear, comprehensive, richly detailed, and endlessly provocative.

A classic introduction to artificial intelligence intended to bridge the gap between theory and practice, Principles of Artificial Intelligence describes fundamental AI ideas that underlie applications such as natural language processing, automatic programming, robotics, machine vision, automatic theorem proving, and intelligent data retrieval. Rather than focusing on the subject matter of the applications, the book is organized around general computational concepts involving the kinds of data structures used, the types of operations performed on the data structures, and the properties of the control strategies used. Principles of Artificial Intelligenceevolved from the author's courses and seminars at Stanford University and University of Massachusetts, Amherst, and is suitable for text use in a senior or graduate AI course, or for individual study.

Documents realistic applications of approximate reasoning techniques, with emphasis placed on operational systems. The papers presented explore new areas of practical decision-making and control systems by considering important aspects of fuzzy logic theory and the latest developments in the field of expert systems. Specific fields of application covered include modelling and control, management, planning, diagnostics, finance and software. Contains 12 papers.

Artificial Intelligence: State of the Art Report is a two-part report consisting of the invited papers and the analysis. The editor first gives an introduction to the invited papers before presenting each paper and the analysis, and then concludes with the list of references related to the study. The invited papers explore the various aspects of artificial intelligence. The analysis part assesses the major advances in artificial intelligence and provides a balanced analysis of the state of the art in this field. The Bibliography compiles the most important published material on the subject of artificial intelligence and includes all the materials cited in the invited paper and analysis references.

Discovering Causal Structure: Artificial Intelligence, Philosophy of Science, and Statistical Modeling provides information pertinent to the fundamental aspects of a computer program called TETRAD. This book discusses the version of the TETRAD program, which is designed to assist in the search for causal explanations of statistical data. or alternative models. This text then examines the notion of applying artificial intelligence methods to problems of statistical model specification. Other chapters consider how the TETRAD program can help to find god alternative models where they exist, and how it can help detect the existence of important neglected variables. This book discusses as well the procedures for specifying a model or models to account for non-experimental or quasi-experimental data. The final chapter presents a description of the format of input files and a description of each command. This book is a valuable resource for social scientists and researchers.

Intended both as a text for advanced undergraduates and graduate students, and as a key reference work for AI researchers and developers, Logical Foundations of Artificial Intelligence is a lucid, rigorous, and comprehensive account of the fundamentals of artificial intelligence from the standpoint of logic. The first section of the book introduces the logicist approach to AI--discussing the representation of declarative knowledge and featuring an introduction to the process of conceptualization, the syntax and semantics of predicate calculus, and the basics of other declarative representations such as frames and semantic nets. This section also provides a simple but powerful inference procedure, resolution, and shows how it can be used in a reasoning system. The next several chapters discuss nonmonotonic reasoning, induction, and reasoning under uncertainty, broadening the logical approach to deal with the inadequacies of strict logical deduction. The third section introduces modal operators that facilitate representing and reasoning about knowledge. This section also develops the process of writing predicate calculus sentences to the metalevel--to permit sentences about sentences and about reasoning processes. The final three chapters discuss the representation of knowledge about states and actions, planning, and intelligent system architecture. End-of-chapter bibliographic and historical comments provide background and point to other works of interest and research. Each chapter also contains numerous student exercises (with solutions provided in an appendix) to reinforce concepts and challenge the learner. A bibliography and index complete this comprehensive work.

A Many-Sorted Calculus Based on Resolution and Paramodulation emphasizes the utilization of advantages and concepts of many-sorted logic for resolution and paramodulation based automated theorem proving. This book considers some first-order calculus that defines how theorems from given hypotheses by pure syntactic reasoning are obtained, shifting all the semantic and implicit argumentation to the syntactic and explicit level of formal first-order reasoning. This text discusses the efficiency of many-sorted reasoning, formal preliminaries for the RP- and?RP-calculus, and many-sorted term rewriting and unification. The completeness and soundness of the?RP-calculus, sort theorem, and automated theorem prover for the?RP-calculus are also elaborated. This publication is a good source for students and researchers interested in many-sorted calculus.

By applying research in artificial intelligence to problems in the philosophy of science, Paul Thagard develops an exciting new approach to the study of scientific reasoning. This approach uses computational ideas to shed light on how scientific theories are discovered, evaluated, and used in explanations. Thagard describes a detailed computational model of problem solving and discovery that provides a conceptually rich yet rigorous alternative to accounts of scientific knowledge based on formal logic, and he uses it to illuminate such topics as the nature of concepts, hypothesis formation, analogy, and theory justification.

This review volume represents the first attempt to provide a comprehensive overview of this exciting and rapidly evolving development. The book comprises specially commissioned articles by leading researchers in the areas of neural networks and connectionist systems, classifier systems, adaptive network systems, genetic algorithm, cellular automata, artificial immune systems, evolutionary genetics, cognitive science, optical computing, combinatorial optimization, and cybernetics.

Interpretation of Visual Motion: A Computational Study provides an information processing point of view to the phenomenon of visual motion. This book discusses the computational theory formulated for recovering the scene from monocular visual motion, determining the local geometry and rigid body motion of surfaces from spatio-temporal parameters of visual motion. This compilation also provides a theoretical and computational framework for future research on visual motion, both in human vision and machine vision areas. Other topics include the computation of image flow from intensity derivatives, instantaneous image flow due to rigid motion, time and space-time derivatives of image flow, and estimation of maximum absolute error. This publication is recommended for professionals and non-specialists intending to acquire knowledge of visual motion.

The most frequently used words in English are highly ambiguous; for example, Webster's Ninth New Collegiate Dictionary lists 94 meanings for the word'run'as a verb alone. Yet people rarely notice this ambiguity. Solving this puzzle has commanded the efforts of cognitive scientists for many years. The solution most often identified is'context': we use the context of utterance to determine the proper meanings of words and sentences. The problem then becomes specifying the nature of context and how it interacts with the rest of an understanding system. The difficulty becomes especially apparent in the attempt to write a computer program to understand natural language. Lexical ambiguity resolution (LAR), then, is one of the central problems in natural language and computational semantics research. A collection of the best research on LAR available, this volume offers eighteen original papers by leading scientists. Part I, Computer Models, describes nine attempts to discover the processes necessary for disambiguation by implementing programs to do the job. Part II, Empirical Studies, goes into the laboratory setting to examine the nature of the human disambiguation mechanism and the structure of ambiguity itself. A primary goal of this volume is to propose a cognitive science perspective arising out of the conjunction of work and approaches from neuropsychology, psycholinguistics, and artificial intelligence--thereby encouraging a closer cooperation and collaboration among these fields. Lexical Ambiguity Resolution is a valuable and accessible source book for students and cognitive scientists in AI, psycholinguistics, neuropsychology, or theoretical linguistics.

'A Bradford book.'