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For millennia, "from Aristotle to almost yesterday," the great problems of philosophy have all been about people: questions of epistemology and philosophy of mind have concerned human capacities and limitations. Still, say the editors of Thinking about Android Epistemology, there should be theories about other sorts of minds, other ways that physical systems can be organized to produce knowledge and competence. The emergence of artificial intelligence in mid-twentieth century provided a way to study the powers and limits of systems that learn, to theorize and to make theories sufficiently concrete so that their properties and consequences can be demonstrated. In this updated version of the 1995 MIT Press book Android Epistemology, computer scientists and philosophers—among them Herbert Simon, Daniel Dennett, and Paul Churchland—offer a gentle, unsystematic introduction to alternative systems of cognition. They look at android epistemology from both theoretical and practical points of view, offering not only speculative proposals but applications—ideas for using computational systems to expand human capacities. The accessible and entertaining essays include a comparison of 2001's HAL and today's computers, a conversation among aliens who have a low opinion of human cognition, an argument for the creativity of robots, and a short story illustrating the power of algorithms for learning causal relations.

Contributors:
Neil Agnew, Margaret Boden, Paul Churchland, Daniel Dennett, Ken M. Ford, Clark Glymour, Pat Hayes, Henry Kyburg, Doug Lenat, Marvin Minsky, Joseph Nadeau, Anatol Rappoport, Herbert Simon, Lynn Andrea Stein, Susan Sterrett

Next Generation Challenges and Future Directions

Data mining, or knowledge discovery, has become an indispensable technology for businesses and researchers in many fields. Drawing on work in such areas as statistics, machine learning, pattern recognition, databases, and high performance computing, data mining extracts useful information from the large data sets now available to industry and science. This collection surveys the most recent advances in the field and charts directions for future research.

The first part looks at pervasive, distributed, and stream data mining, discussing topics that include distributed data mining algorithms for new application areas, several aspects of next-generation data mining systems and applications, and detection of recurrent patterns in digital media. The second part considers data mining, counter-terrorism, and privacy concerns, examining such topics as biosurveillance, marshalling evidence through data mining, and link discovery. The third part looks at scientific data mining; topics include mining temporally-varying phenomena, data sets using graphs, and spatial data mining. The last part considers web, semantics, and data mining, examining advances in text mining algorithms and software, semantic webs, and other subjects.

Edited by Mark T. Maybury

Question answering systems, which provide natural language responses to natural language queries, are the subject of rapidly advancing research encompassing both academic study and commercial applications, the most well-known of which is the search engine Ask Jeeves. Question answering draws on different fields and technologies, including natural language processing, information retrieval, explanation generation, and human computer interaction. Question answering creates an important new method of information access and can be seen as the natural step beyond such standard Web search methods as keyword query and document retrieval. This collection charts significant new directions in the field, including temporal, spatial, definitional, biographical, multimedia, and multilingual question answering.

After an introduction that defines essential terminology and provides a roadmap to future trends, the book covers key areas of research and development. These include current methods, architecture requirements, and the history of question answering on the Web; the development of systems to address new types of questions; interactivity, which is often required for clarification of questions or answers; reuse of answers; advanced methods; and knowledge representation and reasoning used to support question answering. Each section contains an introduction that summarizes the chapters included and places them in context, relating them to the other chapters in the book as well as to the existing literature in the field and assessing the problems and challenges that remain.

Proceedings of the Nineteenth National Conference on Artificial Intelligence

The National Conference on Artificial Intelligence remains the bellwether for research in artificial intelligence. Leading AI researchers and practitioners as well as scientists and engineers in related fields present theoretical, experimental, and empirical results, covering a broad range of topics that include principles of cognition, perception, and action; the design, application, and evaluation of AI algorithms and systems; architectures and frameworks for classes of AI systems; and analyses of tasks and domains in which intelligent systems perform. The Innovative Applications of Artificial Intelligence conference highlights successful applications of AI technology; explores issues, methods, and lessons learned in the development and deployment of AI applications; and promotes an interchange of ideas between basic and applied AI. This volume presents the proceedings of the latest conferences, held in July, 2004.

Proceedings of the Eighteenth National Conference on Artificial Intelligence and the Fourteenth Annual Conference on Innovative Applications of Artificial Intelligence

The annual AAAI National Conference provides a forum for information exchange and interaction among researchers from all disciplines of AI. Contributions include theoretical, experimental, and empirical results. Topics cover principles of cognition, perception, and action; the design, application, and evaluation of AI algorithms and systems; architectures and frameworks for classes of AI systems; and analyses of tasks and domains in which intelligent systems perform. The Innovative Applications Conference highlights successful applications of AI technology and explores issues, methods, and lessons learned in the development and deployment of AI applications.

Multimedia, simulation, computer-mediated communication networks, and distance learning have all become part of the educational toolkit. The next major technology to change the face of education will be based on the widespread use of artificial intelligence (AI). Progress in AI has led to a deeper understanding of how to represent knowledge, to reason, and to describe procedural knowledge. Progress in cognitive science has led to a deeper understanding of how people think, solve problems, and learn. AI scientists use results from cognitive science to create software with more humanlike abilities, which can help students learn better.

This book looks at some of the results of this synergy among AI, cognitive science, and education. Examples include virtual students whose misconceptions force students to reflect on their own knowledge, intelligent tutoring systems, and speech recognition technology that helps students learn to read. Some of the systems described are already used in classrooms and have been evaluated; a few are still laboratory efforts. The book also addresses cultural and political issues involved in the deployment of new educational technologies.

Computational Modeling and Organizational Theories

An organization is more than the sum of its parts, and the individual components that function as a complex social system can be understood only by analyzing their collective behavior. This book shows how state-of-the-art simulation methods, including genetic algorithms, neural networks, and cellular automata, can be brought to bear on central problems of organizational theory related to the emergence, permanence, and dissolution of hierarchical macrostructures. The emphasis is on the application of a new generation of equation- and agent-based computational models that can help students of organizations to reformulate their basic research questions starting from assumptions about how to link—rather than separate—different levels of organizational analysis.

Methods and Applications

Animal-like robots are playing an increasingly important role as a link between the worlds of biology and engineering. The new, multidisciplinary field of biorobotics provides tools for biologists studying animal behavior and testbeds for the study and evaluation of biological algorithms for potential engineering applications. This book focuses on the role of robots as tools for biologists.

An animal is profoundly affected by the many subtle and complex signals within its environment, and because the animal invariably disturbs its environment, it constantly creates a new set of stimuli. Biorobots are now enabling biologists to understand these complex animal-environment relationships. This book unites scientists from diverse disciplines who are using biorobots to probe animal behavior and brain function. The first section describes the sensory systems of biorobotic crickets, lobsters, and ants and the visual system of flies. The second section discusses robots with cockroach motor systems and the intriguing question of how the evolution of complex motor abilities could lead to the development of cognitive functions. The final section discusses higher brain function and neural modeling in mammalian and humanoid robots.

Knowledge discovery and data mining (KDD) deals with the problem of extracting interesting associations, classifiers, clusters, and other patterns from data. The emergence of network-based distributed computing environments has introduced an important new dimension to this problem—distributed sources of data. Traditional centralized KDD typically requires central aggregation of distributed data, which may not always be feasible because of limited network bandwidth, security concerns, scalability problems, and other practical issues. Distributed knowledge discovery (DKD) works with the merger of communication and computation by analyzing data in a distributed fashion. This technology is particularly useful for large heterogeneous distributed environments such as the Internet, intranets, mobile computing environments, and sensor-networks.

When the data sets are large, scaling up the speed of the KDD process is crucial. Parallel knowledge discovery (PKD) techniques addresses this problem by using high-performance multiprocessor machines. This book presents introductions to DKD and PKD, extensive reviews of the field, and state-of-the-art techniques.

Contributors:
Rakesh Agrawal, Khaled AlSabti, Stuart Bailey, Philip Chan, David Cheung, Vincent Cho, Joydeep Ghosh, Robert Grossman, Yi-ke Guo, John Hale, John Hall, Daryl Hershberger, Ching-Tien Ho, Erik Johnson, Chris Jones, Chandrika Kamath, Hillol Kargupta, Charles Lo, Balinder Malhi, Ron Musick, Vincent Ng, Byung-Hoon Park, Srinivasan Parthasarathy, Andreas Prodromidis, Foster Provost, Jian Pun, Ashok Ramu, Sanjay Ranka, Mahesh Sreenivas, Salvatore Stolfo, Ramesh Subramonian, Janjao Sutiwaraphun, Kagan Tummer, Andrei Turinsky, Beat Wüthrich, Mohammed Zaki, Joshua Zhang.

Proceedings of the Seventeenth National Conference on Artificial Intelligence and The Twelfth Annual Conference on Innovative Applications of Artificial Intelligence

The annual AAAI National Conference provides a forum for information exchange and interaction among researchers from all disciplines of AI. Contributions include theoretical, experimental, and empirical results. Topics cover principles of cognition, perception, and action; the design, application, and evaluation of AI algorithms and systems; architectures and frameworks for classes of AI systems; and analyses of tasks and domains in which intelligent systems perform.

Distributed for AAAI Press

Language for Knowledge and Knowledge for Language

Natural language (NL) refers to human language--complex, irregular, diverse, with all its philosophical problems of meaning and context. Setting a new direction in AI research, this book explores the development of knowledge representation and reasoning (KRR) systems that simulate the role of NL in human information and knowledge processing.Traditionally, KRR systems have incorporated NL as an interface to an expert system or knowledge base that performed tasks separate from NL processing. As this book shows, however, the computational nature of representation and inference in NL makes it the ideal level for all tasks in an intelligent computer system. NL processing combines the qualitative characteristics of human knowledge processing with a computer¹s quantitative advantages, allowing for in-depth, systematic processing of vast amounts of information. The essays in this interdisciplinary book cover a range of implementations and designs, from formal computational models to large-scale NL processing systems.Contributors : Syed S. Ali, Bonnie J. Dorr, Karen Ehrlich, Robert Givan, Susan M. Haller, Sanda Harabagiu, Chung Hee Hwang, Lucja Iwanska, Kellyn Kruger, Naveen Mata, David A. McAllester, David D. McDonald, Susan W. McRoy, Dan Moldovan, William J. Rapaport, Lenhart Schubert, Stuart C. Shapiro, Clare R. Voss.

Artificial Intelligence in Hazardous Applications

Computer science and artificial intelligence are increasingly used in the hazardous and uncertain realms of medical decision making, where small faults or errors can spell human catastrophe. This book describes, from both practical and theoretical perspectives, an AI technology for supporting sound clinical decision making and safe patient management. The technology combines techniques from conventional software engineering with a systematic method for building intelligent agents. Although the focus is on medicine, many of the ideas can be applied to AI systems in other hazardous settings. The book also covers a number of general AI problems, including knowledge representation and expertise modeling, reasoning and decision making under uncertainty, planning and scheduling, and the design and implementation of intelligent agents.

The book, written in an informal style, begins with the medical background and motivations, technical challenges, and proposed solutions. It then turns to a wide-ranging discussion of intelligent and autonomous agents, with particular reference to safety and hazard management. The final section provides a detailed discussion of the knowledge representation and other aspects of the agent model developed in the book, along with a formal logical semantics for the language.

Proceedings of the Sixteenth National Conference on Artificial Intelligence and The Eleventh Annual Conference on Innovative Applications of Artificial Intelligence

The annual AAAI National Conference provides a forum for information exchange and interaction among researchers from all disciplines of AI. Contributions include theoretical, experimental, and empirical results. Topics cover principles of cognition, perception, and action; the design, application, and evaluation of AI algorithms and systems; architectures and frameworks for classes of AI systems; and analyses of tasks and domains in which intelligent systems perform.

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Computational Models of Institutions and Groups

The globalization of the economy, increasing number of transnational organizations, and rapid changes in robotics, information, and telecommunication technologies are just a few of the factors significantly altering organizational time scales, forms, complexity, and environments. Time scales have shrunk, new organizational forms are emerging, and organizational environments are expanding and mutating at unprecedented rates. Computational modeling affords opportunities to both understand and respond to these complex changes.

Paralleling developments in the physical sciences, computational modeling is emerging in the social and organizational sciences. Organizational researchers are using computational models to gain insights into organizational phenomena and to explore dynamic processes and configurations that are difficult or impossible to investigate with other methods. Many interesting insights have already resulted from this research, such as how group cooperation arises or dissipates in social dilemma settings, and how honesty and benevolence affect behavior in a group task. On the practical side, computational modeling is increasingly effective for organizational design, analysis, and reengineering.

Although a great deal of work remains to be done, the era is approaching when both theorists and practitioners will routinely state theories, design organizations, and derive their implications using widely shared computational tools. This volume brings together a range of work from many of the leading researchers in the field.

Contributors: Mihai Barbuceanu, Richard Burton, Kathleen Carley, Keith Decker, Edmund Durfee, Mark S. Fox, Natalie Glance, Michael Gruninger, Bernardo Huberman, MinCheol Kang, David Kaplan, Zhiang Lin, Michael Prietula, Kent Sandoe, Walt Scacchi, Young-pa So, William Wallace, Laurie Weissel.

 

Case Studies of Successful Robot Systems


The mobile robot systems described in this book were selected from among the best available implementations by leading universities and research laboratories. These are robots that have left the lab and been tested in natural and unknown environments. They perform many different tasks, from giving tours to collecting trash. Many have distinguished themselves (usually with first- or second-place finishes) at various indoor and outdoor mobile robot competitions.

Each case study is self-contained and includes detailed descriptions of important algorithms, including pseudo-code. Thus this volume serves as a recipe book for the design of successful mobile robot applications. Common themes include navigation and mapping, computer vision, and architecture.

Contributors: Ronald Arkin, Tucker Balch, Michael Brady, Don Brutzman, Arno Bucken, R. James Firby, Erann Gat, Tony Healy, Ian Horswill, Housheng Hu, Sven Koenig, Kurt Konolige David Kortenkamp, Dave Marco, Bob McGhee, Robin Murphy, Karen Myers, Illah Nourbakhsh, Peter Prokopowicz, Bill Schiller, Reid Simmons, Michael Swain, Sebastian Thrun.


Virtual Groups on the Internet

foreword by Ronald Rice



The vast, international web of computer networks that is the Internet offers millions of users the opportunity to exchange electronic mail, photographs, and sound clips; to search databases for books, CDs, cars, and term papers; to participate in real-time audio- and video-conferencing; and to shop for products both virtual and physical. This huge conglomerate of links, hyperlinks, and virtual links is not just a technology for linking computers—it is a medium for communication.

The convergence of computer and communication technologies creates a social convergence as well. People meet in chat rooms and discussion groups to converse on everything from auto mechanics to postmodern art. Networked groups form virtually and on-the-fly, as common interests dictate. Like interpersonal communication, the networks are participatory, their content made up by their audience. Like mass-mediated communication, they involve large audiences. But the networks are neither purely interpersonal nor purely mass—they are a new phenomenon.

Network and Netplay addresses the mutual influences between information technology and group formation and development, to assess the impact of computer-mediated communications on both work and play. Areas discussed include the growth and features of the Internet, network norms and experiences, and the essential nature of network communication.

Contributors: Michael Berthold, Lee Li-Jen Chen, Richard Coyne, Brenda Danet, Patrick Doyle, Brian R. Gaines, Barbara Hayes-Roth, Steve Jones, Sandra Katzman, Edward Mabry, Richard MacKinnon, Margaret McLaughlin, Sid Newton, Kerry Osborne, Sheizaf Rafaeli, Yehudit Rosenbaum-Tamari, Lucia Ruedenberg, Christine Smith, Fay Sudweeks, Alexander Voiskounsky, Diane Witmer.


Proceedings of the Fourteenth National Conference on Artificial Intelligence and The Ninth Annual Conference on Innovative Applications of Artificial Intelligence


The annual AAAI National Conference provides a forum for information exchange and interaction among researchers from all subdisciplines of AI. Contributions include theoretical, experimental, and empirical results. Topics cover principles of cognition, perception, and action; the design, application, and evaluation of AI algorithms and systems; architectures and frameworks for classes of AI systems; and analysis of tasks and domains in which intelligent systems perform.

Two-volume set

Distributed for AAAI Press


Edited by Mark T. Maybury

Foreword by Karen Spärck Jones


Intelligent multimedia information retrieval lies at the intersection of artificial intelligence, information retrieval, human-computer interaction, and multimedia computing. Its systems enable users to create, process, summarize, present, interact with, and organize information within and across different media such as text, speech, graphics, imagery, and video. These systems go beyond traditional hypermedia and hypertext environments to analyze and generate media, and support intelligent interaction with or via multiple media.

The chapters of this volume, which grew out of the 1995 International Joint Conference on Artificial Intelligence Workshop on Intelligent Multimedia Information Retrieval, span a broad range of topics. The book is organized into seven sections: Content-Based Retrieval of Imagery, Content-Based Retrieval of Graphics and Audio, Content-Based Retrieval of Video, Speech and Language Processing for Video Retrieval, Architectures and Tools, Intelligent Hypermedia Retrieval, and Empirical Evaluations.

Contributors: Robert Adams, Phillipe Aigrain, Jonathan Ashley, Thom Blum, Shih-Fu Chang, Mei C. Chuah, W. Bruce Croft, Byron Dom, Ann Doubleday, Florence Dubois, Josef Fink, Myron Flickner, Jonathan Foote, Brian Frew, Monika Gorkani, Morgan Green, James Griffioen, Jon Alte Gulla, Jim Hafner, Qian Hang, Matt Hare, Alexander G. Hauptman, Stacie Hibino, Helmut Horacek, David House, Takafumi Inoue, Philippe Joly, Gareth Jones, Karen Spärck Jones, Douglas Keislaer, Stephen Kerpedjiev, Alfred Kobsa, Denis Lee, Véronique Longueville, Chien Yong Low, R. Manmatha, Inderjeet Mani, Mark T. Maybury, Bernard Mérialdo, Adrian Müller, Wayne Niblac, Andreas Nill, Alex Pentland, Dragutin Petkovic, Steven F. Roth, Neil C. Rowe, Elke A. Rundensteiner, Harpreet Sawhney, John R. Smith, Stephen W. Smoliar, David Steele, Adelheit Stein, Oliviero Stock, Carlo Strapparava, Alistair Sutcliffe, Atshushi Takeshita, Kazuo Tanaka, Ulrich Thiel, Michele Ryan, Julita Vassileva, James Wheaton, Michael J. Witbrock, Erling Wold, JianHua Wu, Peter Yanker, Rajendra Yavatkar, Steven J. Young, Massimo Zancanaro, HongJiang Zhang



Future software will not merely respond to requests for information, but will anticipate the user's needs and actively seek ways to support the user. These systems will also manage cooperation among distributed programs. To describe the many roles of such software, researchers use the term agent.

The essays in Software Agents, by leading researchers and developers of agent-based systems, address both the state-of-the-art of agent technology and its likely evolution in the near future. The introductory chapters in Part I present the views of proponents and a critic of software agents. The chapters in Part II describe how agents are used to enhance learning and provide intelligent assistance to users in situations where traditional direct manipulation interfaces alone are insufficient. The chapters of Part III discuss agent-to-agent communication and the use of agents to provide intelligent interoperability in distributed systems and the Internet.

Contributors: José-Luis Ambite, Ball, P. Benoit, Guy A. Boy, Jeffrey M. Bradshaw, Philip Cohen, Allen Cypher, S. Dutfield, Thomas Erickson, Tim Finin, Michael R. Genesereth, Kenneth R. Grant, Craig A. Knoblock, Kurlander, Yannis Labrou, Kum-Yew Lai, Brenda Laurel, Hector J. Levesque, Ling, Pattie Maes, Thomas W. Malone, James Mayfield, Miller, Nicholas Negroponte, Donald A. Norman, Pugh, Doug Riecken, Ben Shneiderman, Yoav Shoham, Skelly, David C. Smith, Jim Spohrer, Stankosky, Thiel, Van Dantzich, Wax, James E. White, J. Woolley.

Distributed for AAAI Press.


Computerized "expert systems" are among the best-known applications of artificial intelligence. But what is expertise? The nature of knowledge and expertise, and their relation to context, is the focus of active discussion and controversy among psychologists, philosophers, computer scientists, and other cognitive scientists. The questions reach to the very foundations of cognitive theory.

The twenty-three original essay in this volume discuss the essential nature of expert knowledge, as well as such questions as how "expertise" differs from mere "knowledge," the relation between the individual and group processes involved in knowledge in general and expertise in particular, the social and other contexts of expertise, how expertise can be assessed, and the relation between human and computer expertise.

Contributors: N. M. Agnew, D. Bertram, S. Bringsjord, N. Charness, W. Clancey, H. M. Collins, T. M. Converse, R. L. Coulson, D. DuBois, K. A. Ericsson, P. J. Feltovich, K. M. Ford, N. D. Geddes, K. J. Hammond, C. C. Hayes, P. J. Hayes, H. Hexmoor, C. T. Kulik, H. E. Kyburg, M. LaFrance, F. J. Lerch, G. F. Luger, M. Miller, M. Minsky, K. O'Hara, A. L. Patalano, V. L. Patel, D. Perlis, M. J. Prietula, M. F. Ramoni, A. T. Rappaport, C. M. Seifert, N. Shadbolt, V. L. Shalin, S. C. Shapiro, R. J. Spiro, E. W. Stein, C. R. Stern, R. J. Sternberg, M. A. Szczepkowski, C. M. Zeitz

Experiences, Lessons, and Future Directions
Edited by David Leake


Case-based reasoning (CBR) is a flourishing paradigm for reasoning and learning in artificial intelligence, with major research efforts and burgeoning applications extending the frontiers of the field.

This book provides an introduction for students as well as an up-to-date overview for experienced researchers and practitioners. It examines the field in a "case-based" way, through concrete examples of how key issues—including indexing and retrieval, case adaptation, evaluation, and application of CBR methods—are being addressed in the context of a range of tasks and domains. Complementing these case studies are commentaries by leading researchers on the lessons learned from experiences with CBR and visions for the roles in which case-based reasoning can have the greatest impact.

A tutorial introduction by Janet Kolodner, one of the originators of CBR, and David Leake makes the book accessible to students and developers starting to apply case-based reasoning. The volume can also serve as a suitable companion for a CBR or introductory AI textbook.

Proceedings of the Thirteenth National Conference on Artificial Intelligence

August 4-8, 1996, Portland, Oregon

AAAI '96 provides a broad forum for information exchange and interaction among researchers working in different subdisciplines, in different research paradigms, and in different stages of research in artificial intelligence. Topics cover principles underlying cognition, perception and action; design, application, and evaluation of AI algorithms and systems; architectures and frameworks for classes of AI systems; and analysis of tasks and domains in which intelligent systems perform. Included are contributions that describe theoretical, empirical, or experimental results; represent areas of AI that may have been underrepresented in recent conferences; present promising new research concepts, techniques, or perspectives; or discuss issues that cross traditional subdisciplinary boundaries.

Two-volume set

Distributed for the AAAI Press

 


Advances in Knowledge Discovery and Data Mining brings together the latest research—in statistics, databases, machine learning, and artificial intelligence —that are part of the exciting and rapidly growing field of Knowledge Discovery and Data Mining. Topics covered include fundamental issues, classification and clustering, trend and deviation analysis, dependency modeling, integrated discovery systems, next generation database systems, and application case studies. The contributors include leading researchers and practitioners from academia, government laboratories, and private industry.

The last decade has seen an explosive growth in the generation and collection of data. Advances in data collection, widespread use of bar codes for most commercial products, and the computerization of many business and government transactions have flooded us with data and generated an urgent need for new techniques and tools that can intelligently and automatically assist in transforming this data into useful knowledge. This book is a timely and comprehensive overview of the new generation of techniques and tools for knowledge discovery in data.

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Computers and Thought showcases the work of the scientists who not only defined the field of Artificial Intelligence, but who are responsible for having developed it into what it is today. Originally published in 1963, this collection includes twenty classic papers by such pioneers as A. M. Turing and Marvin Minsky who were behind the pivotal advances in artificially simulating human thought processes with computers.

Among the now hard-to-find articles are reports of computer programs that play chess and checkers, prove theorems in logic and geometry, solve problems in calculus, balance assembly lines, recognize visual temporal patterns, and communicate in natural language. The reports of simulation of cognitive processes include computer models of human behavior in logic problems, deciding on common stock portfolios, and carrying out social interaction. Models of verbal learning behavior, predictive behavior in two-choice experiments, and concept formation are also included.

Articles by: Paul Armer. Carol Chomsky. Geoffrey P. E. Clarkson. Edward A. Feigenbaum. Julian Feldman. H. Gelernter. Bert F. Green, Jr. John T. Gullahorn. Jeanne E. Gullahorn. J. R. Hansen. Carl I. Hovland. Earl B. Hunt. Kenneth Laughery. Robert K. Lindsay. D. W. Loveland. Marvin Minsky. Ulric Neisser. Allen Newell. A. L. Samuel. Oliver G. Selfridge. J. C. Shaw. Herbert A. Simon. James R. Slagle. Fred M. Tonge. A. M. Turing. Leonard Uhr. Charles Vossler. Alice K. Wolf.


Cognitive and Computational Perspectives

foreword by Herbert Simon

Diagrammatic reasoning—the understanding of concepts and ideas by the use of diagrams and imagery, as opposed to linguistic or algebraic representations—not only allows us to gain insight into the way we think, but is a potential base for constructing representations of diagrammatic information that can be stored and processed by computers.

Diagrammatic Reasoning brings together recent investigations into the cognitive, the logical, and particularly the computational characteristics of diagrammatic representations and the reasoning that can be done with them. Following a foreword by Herbert Simon and an introduction by the editors, twenty-seven chapters provide an overview of the recent history of the subject, survey and extend the underlying theory of diagrammatic representation, and provide numerous examples of diagrammatic reasoning (human and mechanical) that illustrate both its powers and its limitations.

Each of the book's four sections (Historical and Philosophical Background, Theoretical Foundations, Cognitive and Computational Models, and Problem Solving with Diagrams) begins with an introduction by an eminent researcher. These introductions provide interesting personal perspectives as well as place the work in the proper context.

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