Books

  1. Artificial Intelligence and Cognitive Science: Conceptual Issues: Consciousness and Emotion in Cognitive Science v. 3
    Artificial Intelligence and Cognitive Science: Conceptual Issues: Consciousness and Emotion in Cognitive Science v. 3
  2. Artificial Intelligence and Cognitive Science: Conceptual Issues: Language and Meaning in Cognitive Science - Cognitive Issues and Semantic Theory Vol 4
    Artificial Intelligence and Cognitive Science: Conceptual Issues: Language and Meaning in Cognitive Science - Cognitive Issues and Semantic Theory Vol 4
  3. Robotics: Intelligent Machines for the New Century (Science & Technology in Focus S.)
    Robotics: Intelligent Machines for the New Century (Science & Technology in Focus S.)
  4. Neural Networks and Computational Complexity (Progress in Theoretical Computer Science S.)
    Neural Networks and Computational Complexity (Progress in Theoretical Computer Science S.)
  5. Logic Programming and Soft Computing (Uncertainty Theory in Artificial Intelligence S.)
    Logic Programming and Soft Computing (Uncertainty Theory in Artificial Intelligence S.)
  6. Automata Theory and Their Applications (Progress in Computer Science & Applied Logic)
    Automata Theory and Their Applications (Progress in Computer Science & Applied Logic)
  7. Handbook of Logic and Proof Techniques for Computer Science
    Handbook of Logic and Proof Techniques for Computer Science
  8. Flame Wars: Discovery of Cyberculture
    Flame Wars: Discovery of Cyberculture
  9. Blind Equalization and Identification (Signal Processing & Communications S.)
    Blind Equalization and Identification (Signal Processing & Communications S.)
  10. Image Recognition and Classification: Alogorithms, Systems and Applications (Optical Engineering S.)
    Image Recognition and Classification: Alogorithms, Systems and Applications (Optical Engineering S.)
  11. Competitive Technical Intelligence: A Guide to Design, Analysis and Action (ACS Professional Reference Books)
    Competitive Technical Intelligence: A Guide to Design, Analysis and Action (ACS Professional Reference Books)
  12. Microelectronics Design of Fuzzy Logic-based Systems (International Series on Computational Intelligence)
    Microelectronics Design of Fuzzy Logic-based Systems (International Series on Computational Intelligence)
  13. Intelligent Systems and Technologies in Rehabilitation Engineering (The CRC Press International Series on Computational Intelligence)
    Intelligent Systems and Technologies in Rehabilitation Engineering (The CRC Press International Series on Computational Intelligence)
  14. Adaptive Image Processing
    Adaptive Image Processing
  15. Intelligent Machines: Myths and Realities
    Intelligent Machines: Myths and Realities
  16. Computational Intelligence in Design and Manufacturing Handbook
    Computational Intelligence in Design and Manufacturing Handbook
  17. Computer Aided Design, Engineering, and Manufacturing: Systems Techniques and Applications: Articial Intelligence and Robotics in Manufacturing Volume VII
    Computer Aided Design, Engineering, and Manufacturing: Systems Techniques and Applications: Articial Intelligence and Robotics in Manufacturing Volume VII
  18. Computational Intelligence in Telecommunications Networks
    Computational Intelligence in Telecommunications Networks
  19. Pattern Recognition in Speech and Language Processing
    Pattern Recognition in Speech and Language Processing
  20. Robust Control Systems with Genetic Algorithms (Control S.)
    Robust Control Systems with Genetic Algorithms (Control S.)
  21. Artificial Intelligence and Soft Computing: Behavioral and Cognitive Modeling of the Human Brain
    Artificial Intelligence and Soft Computing: Behavioral and Cognitive Modeling of the Human Brain
  22. Soft Computing in Human-related Sciences (International Series on Computational Intelligence)
    Soft Computing in Human-related Sciences (International Series on Computational Intelligence)
  23. Intelligent Control Systems Using Soft Computing Methodologies
    Intelligent Control Systems Using Soft Computing Methodologies
  24. Industrial Applications of Genetic Algorithms (International Series on Computational Intelligence)
    Industrial Applications of Genetic Algorithms (International Series on Computational Intelligence)
  25. Knowledge-Based Intelligent Techniques in Industry (International Series on Computational Intelligence)
    Knowledge-Based Intelligent Techniques in Industry (International Series on Computational Intelligence)
Conceptual Structures: Inspiration and Application: 14th International Conference on Conceptual Structures, ICCS 2006, Aalborg, Denmark, July 16-21, 2006, ... (Lecture Notes in Computer Science)
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    		Conceptual Structures: Inspiration and Application: 14th International Conference on Conceptual Structures, ICCS 2006, Aalborg, Denmark, July 16-21, 2006, ... (Lecture Notes in Computer Science)

    Manufacturer: Springer
    ProductGroup: Book
    Binding: Paperback

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    ASIN: 3540358935

    Book Description

    This book constitutes the refereed proceedings of the 14th International Conference on Conceptual Structures, ICCS 2006, held in Aalborg, Denmark in July 2006.

    The 24 revised full papers presented together with 6 invited papers were carefully reviewed and selected from 62 submissions. The central focus is the formal representation and analysis of conceptual knowledge with research and business applications focusing on artificial intelligence, computational linguistics, and related areas of computer science. The papers address topics such as conceptual structures; their interplay with language, semantics and pragmatics; formal methods for concept analysis and contextual logic, modeling, representation, and visualization of concepts; conceptual knowledge acquisition; and the theory and applications of formal ontologies.

    Machine Intelligence: Perspectives on the Computational Model (Artificial Intelligence and Cognitive Science: Conceptual Issues)
    Average customer rating: Not rated
      		Machine Intelligence: Perspectives on the Computational Model (Artificial Intelligence and Cognitive Science: Conceptual Issues)
      Andy Clark
      Manufacturer: Routledge
      ProductGroup: Book
      Binding: Library Binding

      GeneralGeneral | Artificial Intelligence | Computer Science | Computers & Internet | Subjects | Books
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      ASIN: 0815327684

      Book Description

      Summarizes and illuminates two decades of research
      Gathering important papers by both philosophers and scientists, this collection illuminates the central themes that have arisen during the last two decades of work on the conceptual foundations of artificial intelligence and cognitive science. Each volume begins with a comprehensive introduction that places the coverage in a broader perspective and links it with material in the companion volumes. The collection is of interest in many disciplines including computer science, linguistics, biology, information science, psychology, neuroscience, iconography, and philosophy.

      Examines initial efforts and the latest controversies
      The topics covered range from the bedrock assumptions of the computational approach to understanding the mind, to the more recent debates concerning cognitive architectures, all the way to the latest developments in robotics, artificial life, and dynamical systems theory. The collection first examines the lineageof major research programs, beginning with the basic idea of machine intelligence itself, then focuses on specific aspects of thought and intelligence, highlighting the much-discussed issue of consciousness, the equally important, but less densely researched issue of emotional response, and the more traditionally philosophical topic of language and meaning.

      Provides a gamut of perspectives
      The editors have included several articles that challenge crucial elements of the familiar research program of cognitive science, as well as important writings whose previous circulation has been limited. Within each volume the papers are organized to reflect a variety of research programs and issues. The substantive introductions that accompany each volume further organize the material and provide readers with a working sense of the issues and the connection between articles.

      Conceptual Spaces: The Geometry of Thought
      Average customer rating: 4.5 out of 5 stars
      • Excellent and Enlightening
      • A little disappointing
      • An eye opener
      • A new model of thought
      • Excellent! Conceptual Spaces make sense to me.
      		 Conceptual Spaces: The Geometry of Thought
      Peter Gärdenfors
      Manufacturer: The MIT Press
      ProductGroup: Book
      Binding: Hardcover

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      Similar Items:
      1. Geometry and Meaning
      2. Knowledge Representation and Reasoning (The Morgan Kaufmann Series in Artificial Intelligence) (The Morgan Kaufmann Series in Artificial Intelligence)
      3. Reasoning about Uncertainty
      4. Knowledge Representation: Logical, Philosophical, and Computational Foundations: Logical, Philosophical, and Computational Foundations
      5. Conceptual Mathematics: A First Introduction to Categories

      ASIN: 0262071991

      Book Description

      Within cognitive science, two approaches currently dominate the problem of modeling representations. The symbolic approach views cognition as computation involving symbolic manipulation. Connectionism, a special case of associationism, models associations using artificial neuron networks. Peter Gardenfors offers his theory of conceptual representations as a bridge between the symbolic and connectionist approaches.

      Symbolic representation is particularly weak at modeling concept learning, which is paramount for understanding many cognitive phenomena. Concept learning is closely tied to the notion of similarity, which is also poorly served by the symbolic approach. Gardenfors's theory of conceptual spaces presents a framework for representing information on the conceptual level. A conceptual space is built up from geometrical structures based on a number of quality dimensions. The main applications of the theory are on the constructive side of cognitive science: as a constructive model the theory can be applied to the development of artificial systems capable of solving cognitive tasks. Gardenfors also shows how conceptual spaces can serve as an explanatory framework for a number of empirical theories, in particular those concerning concept formation, induction, and semantics. His aim is to present a coherent research program that can be used as a basis for more detailed investigations.

      Customer Reviews:

      5 out of 5 stars Excellent and Enlightening.......2004-07-29

      Gardenfors introduces his theory of concept-formation, and at the same time presents a survey of the competing theories and research. He shows a high level of professionalism by accepting that the theories can coexist, presenting the competing theories in their strongest light and letting you decide where to apply each theory. This book is not only a good argument for why his theory deserves a permanent place in your toolbox, but also a good education for anyone wanting to know the tradeoffs in representing concepts -- especially for knowledge representation or machine learning systems. He presents the material in a very logical order so that the subtopics can be consumed individually. And although some of the material is well-known, each chapter presents a series of contrasting pros and cons and synthesizes the information in ways that are thought-provoking and novel. It was well worth the time and money.

      3 out of 5 stars A little disappointing.......2004-07-10

      If one is to design a machine that can formulate concepts and engage in such things as inductive inference and its corollary scientific discovery, then one must be able to quantify the notion of a concept in such a way that it can be implemented into the cognitive structure of the machine. One must be able to distinguish one concept from another, be able to tell when one concept is similar to another, and understand in detail how concepts are related across domains. It would not be enough to have qualitative notions of these distinctions or similarities, since they must be able to be formatted in such a way, either via coding, language, or electronically, so as to be used by the machine.

      This book gives an interesting approach to the problem of concept classification, but it does so only from a qualitative point of view. It is a good start in this regard, and readers will gain a lot of insight into the problems that it addresses. It does not however give any advice on how to implement its ideas into a real thinking machine. Mathematical concepts are brought in order to talk more meaningfully about spaces of concepts, but they are really restricted to metric spaces and not general enough to deal with the plethora of concepts that could present themselves in typical environments. The book should be considered more as a work in philosophy, so those interested in this field might enjoy the book more than those who were expecting a book more geared towards artificial intelligence and computer science. Those readers interested in automated theorem proving or automated mathematical discovery might find the discussion on geometric categorization models of interest, and will find an interesting application of Voronoi tessellations, namely that of accounting for the varying sizes of concepts in a categorization.

      By far the most interesting chapter in the book is chapter 6, wherein the author gives a highly original discussion of inductive inference. The ability of human cognition to generalize from a limited number of observations is viewed (correctly) by the author as very impressive, but he is careful to note that inductive inference cannot be done free of side constraints. Quoting the philosopher J.S. Peirce and his evolutionary explanation of why induction is so effective, the author uses his theory of conceptual spaces to develop a theory of constraints for inductive inferences. The main notion in this theory is that of "projectability", which attempts to delineate the properties and concepts that are may be used in inductive inference. The author wants to arrive at a computational model of induction, and he offers interesting proposals for doing so, even if they lack immediate empirical justification.

      Central to the problem of induction the author argues is how observations are to be represented. This has been neglected in the history of philosophy he says, and so he then proceeds to outline his ideas on how to represent observations, distinguishing three levels, namely the `symbolic', the `conceptual', and the `subconceptual.' At the symbolic level, observations are represented by describing them in a specified language. At the conceptual level, observations are characterized relative to a conceptual space. At this level induction is viewed as concept formation. At the subconceptual level observations are characterized by inputs from sensory receptors. Induction is then viewed as the attaining of connections between various inputs. The author views the processing taking place in artificial neural networks as an example of modeling at the subconceptual level.

      The problem of induction is more complicated than is typically presented in the literature, the author argues. Inductive inference will look different depending on which approach to observations is taken. In his elaborations on the processes of induction, one of the key issues that arises is the how discovery takes place across different domains. The process of conceptualizing across different domains takes place, as expected, at the subconceptual and conceptual levels. The symbolic level is delegated to formulating laws.

      5 out of 5 stars An eye opener.......2003-08-12

      For anyone interested in the cognitive topics, machine learning and artificial intelligence, this book is an eye opener. The point of view it presents attempts to put an order in what "meaning" really means.

      Drawbacks of the book? The lack of conceptualization when it comes to dynamic concepts (treated very superficially). Also, the theory is deficient when modeling the functional aspects of concepts (a "sin" already recognized by the author).

      But considering the pioneering character of this piece of art, these drawbacks are just compelling invitations for further research in the field.

      5 out of 5 stars A new model of thought.......2003-03-02

      Profound piece of work. I am not a cognitive scientist, and this book is a bit technical, but it is still within reach of the motivated lay person.

      Gardenfors puts forward a a model to explain cognition that he calls "conceptual spaces." These conceptual spaces are at a level of abstraction in between the symbolic (used by AI types) and connectionist (Neural Nets). But what makes his conceptual spaces interesting and plausible is the position he takes that in this conceptual space, most reasoning is done by evaluating the analog of a distance between two aspects of a perception. Or, we find things to be similar if they are "geometrically" (measurably) closer on some limited number of dimensional scales.

      This is easy to follow for things like colors, but he doesn't stop there. He goes on to describe how this explains a wide variety of perceptions, as well as how we form and reform categories and concepts, and shows how this informs semantics and the process of induction.

      My only criticism is that some of the illustratios would have been more powerful in color.

      5 out of 5 stars Excellent! Conceptual Spaces make sense to me........2001-12-03

      The essence of conceptual spaces, as I understand it, is that we can define concepts as regions in conceptual spaces. A conceptual space is defined by axes representing qualities. For example, color spaces are conceptual spaces, as is the tasting combos of sweet, bitter, salty.

      Your choice of qualitative measures deeply affects how you understand the world. 'Spose reality is an infinitely dimensional, then we have lots of choices for axes. We simplify and correlate by using all that coordinate transformation and axis projection stuff from 3D graphics! Heck Gardenfors even uses Delauney Triangulation (or polyhedralization).

      Criterion P, page 71

      A natural property is a convex region of a domain in a conceptual space.

      Criterion C, page 105

      A natural concept is represented as a set of regions in a number of domains together with an assignments of salience weights to the domains and information about how the regions in the different domains are correlated.

      Concept Combination, page 122

      The combination CD of two concepts C and D is determined by letting the regions for the domains of C, confined by D replace the values of the corresponding regions for D. (contrast class p. 119), for example the "stone lions" outside the NYC library.

      Six Tenets of Cognitive Semantics, page 160

      i) Meaning is a conceptual structure in a cognitive system (not truth conditions in possible worlds)
      ii) Conceptual Structure are embodied (meaning is not independent of perception or of bodily experience).
      iii) Semantic elements are constructed from geometrical or topological structures (not symbols that can be composed according to some system of rules).
      iv) Cognitive models are primarily image-schematic (not propositional). Image-schemas are transformed by metaphoric and metonymic operations (which are treated as exceptional features on the traditional views).
      v) Semantics is primary to syntax and partly determines it (syntax cannot be described independently of semantics).
      vi) Concepts show prototype effects (instead of showing the Aristotelian paradigm based on necessary and sufficient conditions).

      Process of Abstraction, page 191 - Start with a collection of things. Identify and quantify individual objects. The determine the clusters. Step three: abstract the clusters into dimensions. Simple!

      I especially liked the notion that a metaphor is taking the spatial relationship of a cluster of concepts in one domain and using them in a new domain to help understand the new domain.

      Conceptual Revolutions
      Average customer rating: 5 out of 5 stars
      • This book is brilliant!
      • Essential companion to complete Kuhn's book
      		 Conceptual Revolutions
      Paul Thagard
      Manufacturer: Princeton Univ Pr
      ProductGroup: Book
      Binding: Hardcover

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      Similar Items:
      1. Computational Philosophy of Science (Bradford Books)
      2. Hot Thought: Mechanisms and Applications of Emotional Cognition (Bradford Books)
      3. The Big Book of Concepts (Bradford Books)
      4. Fear of Knowledge: Against Relativism and Constructivism

      ASIN: 0691087458

      Book Description

      In this path-breaking work, Paul Thagard draws on the history and philosophy of science, cognitive psychology, and the field of artificial intelligence to develop a theory of conceptual change capable of accounting for all major scientific revolutions. The history of science contains dramatic episodes of revolutionary change in which whole systems of concepts have been replaced by new systems. Thagard provides a new and comprehensive perspective on the transformation of scientific conceptual systems.

      Thagard examines the Copernican and the Darwinian revolutions and the emergence of Newton's mechanics, Lavoisier's oxygen theory, Einstein's theory of relativity, quantum theory, and the geological theory of plate tectonics. He discusses the psychological mechanisms by which new concepts and links between them are formed, and advances a computational theory of explanatory coherence to show how new theories can be judged to be superior to previous ones.

      Customer Reviews:

      5 out of 5 stars This book is brilliant!.......2007-03-16

      It's clear, logical, easily understandable and has a table that is extremely insightful, helpful and practical for anyone who wants to turn a situation, process or change into a multi-step plan.

      Yes, it builds on Kuhn. But it goes a bit further. And because it's specifically oriented towards implementing a learning process in Artificial Intelligence, that 'learning' orientation can easily be translated to 'organizational' or even 'individual learning'.

      A highly valuable asset!

      5 out of 5 stars Essential companion to complete Kuhn's book.......2005-04-11

      Conceptual Revolutions uses easy-to-follow computer-based models to demonstrate the cognitive mechanisms of the process of what seems most occult and irrational in Kuhn's model of paradigm conversion. Explains complex networks and hierarchies of concepts, mental structures, and conceptual systems, and how conceptual systems develop so that a new conceptual system eventually provides greater explanatory coherence than the previous conceptual system. Covers conceptual hierarchy transformation and how concepts are recombined, added into, and deleted from large-scale conceptual systems. Brings Kuhn's theory to completion, resulting in a fully powerful way of thinking about conceptual revolution in any domain, thus intellectual conversion in general.
      Conceptual Structures: Knowledge Architectures for Smart Applications: 15th International Conference on Conceptual Structures, ICCS 2007, Sheffield, UK, ... (Lecture Notes in Computer Science)
      Average customer rating: Not rated
        		Conceptual Structures: Knowledge Architectures for Smart Applications: 15th International Conference on Conceptual Structures, ICCS 2007, Sheffield, UK, ... (Lecture Notes in Computer Science)

        Manufacturer: Springer
        ProductGroup: Book
        Binding: Unknown Binding

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        ASIN: 3540736808

        Book Description

        This book constitutes the refereed proceedings of the 15th International Conference on Conceptual Structures, ICCS 2007, held in Sheffield, UK in July 2007.

        The 28 revised full papers and 13 revised short papers presented together with 4 invited papers and 1 introductory talk were carefully reviewed and selected from about 60 submissions. A special focus is given to application of conceptual structures in business and technological settings; other papers cover research into conceptual structures, which is supported by mathematical and computational theory, including formal concept analysis, algorithm design and graph theory, and a variety of software tools. The papers are organized in topical sections on conceptual graphs, formal concept analysis, and conceptual structures.

        Language and Meaning in Cognitive Science: Cognitive Issues and Semantic theory (Artificial Intelligence and Cognitive Science: Conceptual Issues)
        Average customer rating: Not rated
          		Language and Meaning in Cognitive Science: Cognitive Issues and Semantic theory (Artificial Intelligence and Cognitive Science: Conceptual Issues)
          Andy Clark
          Manufacturer: Routledge
          ProductGroup: Book
          Binding: Library Binding

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          ASIN: 0815327714

          Book Description

          Summarizes and illuminates two decades of research
          Gathering important papers by both philosophers and scientists, this collection illuminates the central themes that have arisen during the last two decades of work on the conceptual foundations of artificial intelligence and cognitive science. Each volume begins with a comprehensive introduction that places the coverage in a broader perspective and links it with material in the companion volumes. The collection is of interest in many disciplines including computer science, linguistics, biology, information science, psychology, neuroscience, iconography, and philosophy.

          Examines initial efforts and the latest controversies
          The topics covered range from the bedrock assumptions of the computational approach to understanding the mind, to the more recent debates concerning cognitive architectures, all the way to the latest developments in robotics, artificial life, and dynamical systems theory. The collection first examines the lineageof major research programs, beginning with the basic idea of machine intelligence itself, then focuses on specific aspects of thought and intelligence, highlighting the much-discussed issue of consciousness, the equally important, but less densely researched issue of emotional response, and the more traditionally philosophical topic of language and meaning.

          Provides a gamut of perspectives
          The editors have included several articles that challenge crucial elements of the familiar research program of cognitive science, as well as important writings whose previous circulation has been limited. Within each volume the papers are organized to reflect a variety of research programs and issues. The substantive introductions that accompany each volume further organize the material and provide readers with a working sense of the issues and the connection between articles.

          Conceptual Structures: Broadening the Base: 9th International Conference on Conceptual Structures, ICCS 2001, Stanford, CA, USA, July 30-August 3, 2001, Proceedings (Lecture Notes in Computer Science)
          Average customer rating: Not rated
            		Conceptual Structures: Broadening the Base: 9th International Conference on Conceptual Structures, ICCS 2001, Stanford, CA, USA, July 30-August 3, 2001, Proceedings (Lecture Notes in Computer Science)

            Manufacturer: Springer
            ProductGroup: Book
            Binding: Paperback

            Expert SystemsExpert Systems | Artificial Intelligence | Computer Science | Computers & Internet | Subjects | Books
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            ASIN: 3540423443

            Book Description

            This book constitutes the refereed proceedings of the 9th International Conference on Conceptual Structures, ICCS 2001, held in Stanford, CA, USA in July/August 2001.The 26 revised full papers presented were carefully reviewed and selected for inclusion in the proceedings. The book offers topical sections on language and knowledge structures, logical and mathematical foundations of conceptual structures, conceptual structures for data and knowledge bases, conceptual structures and meta-data, and algorithms and systems.
            Structural Knowledge: Techniques for Representing, Conveying, and Acquiring Structural Knowledge
            Average customer rating: 4 out of 5 stars
            • Describes 23 ways of working with Structural Knowledge
            		 Structural Knowledge: Techniques for Representing, Conveying, and Acquiring Structural Knowledge
            David H. Jonassen
            Manufacturer: TF-LEA
            ProductGroup: Book
            Binding: Hardcover

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            ASIN: 0805810099

            Customer Reviews:

            4 out of 5 stars Describes 23 ways of working with Structural Knowledge.......1997-03-09

            Drawing heavily on the academic literature in cognitive science, this book discusses 23 methodologies for working with what the author calls "structural knowledge." The author views structural knowledge as the intermediary between declarative knowledge (know that) and procedural knowledge (know how). Methods discussed in the book include: concept maps, frames, semantic features analysis, pattern notes, graphic organizers, and many other approaches
            Systems That Learn - 2nd Edition: An Introduction to Learning Theory (Learning, Development, and Conceptual Change)
            Average customer rating: 4 out of 5 stars
            • Insightful introduction to computational learning theory
            		 Systems That Learn - 2nd Edition: An Introduction to Learning Theory (Learning, Development, and Conceptual Change)
            Sanjay Jain , Daniel Osherson , James S. Royer , and Arun Sharma
            Manufacturer: The MIT Press
            ProductGroup: Book
            Binding: Hardcover

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            Cognitive ScienceCognitive Science | Behavioral Sciences | Professional Science | Professional & Technical | Subjects | Books
            Cognitive PsychologyCognitive Psychology | Behavioral Sciences | Science | Subjects | Books
            GeneralGeneral | Behavioral Sciences | Science | Subjects | Books
            ASIN: 0262100770

            Book Description

            Formal learning theory is one of several mathematical approaches to the study of intelligent adaptation to the environment. The analysis developed in this book is based on a number theoretical approach to learning and uses the tools of recursive-function theory to understand how learners come to an accurate view of reality. This revised and expanded edition of a successful text provides a comprehensive, self-contained introduction to the concepts and techniques of the theory. Exercises throughout the text provide experience in the use of computational arguments to prove facts about learning.

            Customer Reviews:

            4 out of 5 stars Insightful introduction to computational learning theory.......2004-08-31

            Concentrating on the mathematical and formal underpinnings of learning theory, this book gives a very interesting and general overview of the subject. Basing their discussion on the learning of "texts" and the learning of "functions", the authors address the main issues in the formal modeling of empirical inquiry. The models or "paradigms" they construct are based on five concepts, which they consider of central importance in empirical inquiry. These concepts are: (1) A reality that is theoretically possible; (2) hypotheses that are intelligible; (3) the data that is available about a given reality; (4) a model of a scientist; (5) the successful behavior of the scientist who is investigating a possible reality. The scientist is thought of as playing a game with Nature, with the class of possible realities being known to each of them initially. Nature selects a member from this class, initially unknown to the scientist. After providing a series of clues (data) to the scientist, the scientist forms hypotheses based on these clues. The scientist wins the game if the hypotheses become stable and accurate. The game is easier to win the more constrained Nature's choice of actual world is.

            After a brief philosophical discussion of the paradigms and a review of the theory of computation, the authors begin their study by concentrating on identification of languages and identification of functions. Both of these are considered to be `theoretically possible realities', and in the case of languages, it is "texts" that are to be identified by scientists. Those texts that can are called `identifiable' and the authors prove a theorem that characterizes how scientists identify languages in terms of finite strings of text. Success in function identification is cast as a generalization of that of text. In this case the class of possible realities are the collection of total recursive functions, and hypotheses are programs that compute these functions. The authors show however that a scientist who identifies the entire class of recursive functions cannot be computable. Very interesting in this discussion is the treatment of `parametrized scientists', i.e. those scientists who can incorporate background knowledge from other scientists.

            These considerations involve the view of a scientist as being a certain fixed entity. The authors also consider cases where alternative notions of scientist occur, but the other paradigms are held fixed. The abilities of computable scientists who deploy different inductive `strategies' are studied, with the goal of finding out if a member of a particular strategy can effectively identify languages or functions. Recognizing that the conjectures proposed successively by a particular scientist may not be related to one another, the authors then discuss strategies that result from imposing relations between the conjectures. One of these, called `conservative', insists that a conjecture that generates all the data observed to date should never be abandoned. Also discussed are `generalization strategies' that require scientists to improve upon their successive conjectures. One example of these strategies is called `strong-monotonic', which forbids the revision of a hypothesis if it made a mistake in identification. Another example is called `weak monotonic', which allows the rejection of parts of a hypothesis if it encounters data that cannot be accounted for by this hypothesis. Still another is `monotonic', which allows the correction of mistaken hypotheses, but does not allow hypotheses that will contradict correct classifications. The authors show that monotonicity does not imply weak-monotonicity, and vice versa. Also discussed are `specialization' strategies, which are "dual" to the three generalization strategies, and which involve the pruning of hypotheses in order to obtain convergence.

            The authors also address the case where the conception of a scientist is held fixed, but the criteria for scientific success are varied. This study, in the opinion of this reviewer, more accurately reflects the real behavior of scientists, who typically use very liberal notions of accuracy. For example, anomalies in data could be tolerated, pending alteration of the hypotheses in the future. These anomalies in fact serve to drive further research, with the goal of finding hypotheses or theories that resolve them. It is typically the case, if not always, that the hypotheses are considered approximate explanations, and so one would expect that the authors' discussion would revolve around the consideration of inference of approximations. The authors though do give an interesting twist to this discussion, namely, they attempt to find criteria for success that actually permit an infinite number of anomalies in the final explanations. This serves to better characterize explanations, they argue. A series of identification criteria are outlined each of which involves measure-theoretic notions of `asymptotic agreement.' A scientist presented with a function must arrive at an explanation that agrees asymptotically with the function up to a prespecified amount.

            Also more realistic, due to its emphasis on what happens in actual scientific investigation, is the authors' discussion on alternative conceptions of available data. Noting that data can be missing or have errors, and is presented in a definite order to a scientist, the authors study how to deal with error in the finding of intelligible hypotheses. Their results delineate the extent to which inaccurate data can impede the learning process, with three kinds of "inaccurate" data considered: "incomplete", "noisy", and "imperfect."

            Other topics discussed include the modeling of empirical inquiry when many scientists are collaborating with each other, and that of probabilistic learning. For team identification of functions, several interesting results are proven, but the authors admit that their results do not apply to the (more realistic) scenario where the hypotheses of each individual scientist influence each other. Also discussed are "oracle" scientists, who use information of a noncomputable nature, or "information oracles", in order to perform identifications. When judged by how much information can be given to the scientist, oracles can be "omniscient" or "trivial", and it is thus of interest to determine how much oracles can supply scientists in their identification of functions. The authors discuss various results on this topic, showing how much is to be gained by allowing oracle scientists to make additional queries.
            Consciousness and Emotion in Cognitive Science: Conceptual and Empirical Issues (Artificial Intelligence and Cognitive Science: Conceptual Issues)
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              		Consciousness and Emotion in Cognitive Science: Conceptual and Empirical Issues (Artificial Intelligence and Cognitive Science: Conceptual Issues)
              Andy Clark
              Manufacturer: Routledge
              ProductGroup: Book
              Binding: Library Binding

              GeneralGeneral | Artificial Intelligence | Computer Science | Computers & Internet | Subjects | Books
              GeneralGeneral | Philosophy | Nonfiction | Subjects | Books
              ModernModern | Philosophy | Nonfiction | Subjects | Books
              GeneralGeneral | Psychology & Counseling | Health, Mind & Body | Subjects | Books
              EmotionsEmotions | By Topic | Psychology & Counseling | Health, Mind & Body | Subjects | Books
              IntelligenceIntelligence | By Topic | Psychology & Counseling | Health, Mind & Body | Subjects | Books
              ConsciousnessConsciousness | By Topic | Psychology & Counseling | Health, Mind & Body | Subjects | Books
              CognitiveCognitive | Psychology & Counseling | Health, Mind & Body | Subjects | Books
              Cognitive PsychologyCognitive Psychology | Behavioral Sciences | Professional Science | Professional & Technical | Subjects | Books
              Cognitive ScienceCognitive Science | Behavioral Sciences | Professional Science | Professional & Technical | Subjects | Books
              Cognitive PsychologyCognitive Psychology | Behavioral Sciences | Science | Subjects | Books
              GeneralGeneral | Behavioral Sciences | Science | Subjects | Books
              ASIN: 0815327706

              Book Description

              Summarizes and illuminates two decades of research
              Gathering important papers by both philosophers and scientists, this collection illuminates the central themes that have arisen during the last two decades of work on the conceptual foundations of artificial intelligence and cognitive science. Each volume begins with a comprehensive introduction that places the coverage in a broader perspective and links it with material in the companion volumes. The collection is of interest in many disciplines including computer science, linguistics, biology, information science, psychology, neuroscience, iconography, and philosophy.

              Examines initial efforts and the latest controversies
              The topics covered range from the bedrock assumptions of the computational approach to understanding the mind, to the more recent debates concerning cognitive architectures, all the way to the latest developments in robotics, artificial life, and dynamical systems theory. The collection first examines the lineageof major research programs, beginning with the basic idea of machine intelligence itself, then focuses on specific aspects of thought and intelligence, highlighting the much-discussed issue of consciousness, the equally important, but less densely researched issue of emotional response, and the more traditionally philosophical topic of language and meaning.

              Provides a gamut of perspectives
              The editors have included several articles that challenge crucial elements of the familiar research program of cognitive science, as well as important writings whose previous circulation has been limited. Within each volume the papers are organized to reflect a variety of research programs and issues. The substantive introductions that accompany each volume further organize the material and provide readers with a working sense of the issues and the connection between articles.

              Books:

              1. Artificial Intelligence and Cognitive Science: Conceptual Issues: Consciousness and Emotion in Cognitive Science v. 3
              2. Intelligent Adaptive Control: Industrial Applications (International Series on Computational Intelligence)
              3. Shadows and Silhouettes in Computer Vision (The Kluwer International Series in Engineering & Computer Science; Robotics & Vision)
              4. Socially Intelligent Agents: Creating Relationships with Computers and Robots (Multiagent Systems, Artificial Societies & Simulated Organizations S.)
              5. Inventing the Future: Information Services for a New Millennium (Contemporary Studies in Information Management, Policies, & Services)
              6. Connectionist Models of Learning, Development and Evolution: Proceedings of the Sixth Neural Computation and Psychology Workshop, Liege, Belgium, 16-18 September 2000 (Perspectives in Neural Computing S.)
              7. Robotics Research: Proceedings of the First International Symposium on Robotics Research (Artificial Intelligence S.)
              8. Intelligent Information Systems (Advances in Soft Computing S.)
              9. Advances in Intelligent Data Analysis - Reasoning About Data: Second International Symposium, Ida-97, London, UK, August 4-6, 1997: Proceedings (Lecture Notes in Computer Science S.)
              10. 39th Annual Meeting of the Association for Computational Linguistics and 10th Conference of the European Chapter of the Association for Computational Linguistics

              Books