Unifying computing and cognition

Discussion in 'Computer Information' started by Gerry, Jul 23, 2006.

  1. Gerry

    Gerry Guest

    New book:

    UNIFYING COMPUTING AND COGNITION

    The SP Theory and its Applications

    J Gerard Wolff

    CognitionResearch.org.uk, 2006,
    ISBN 0-9550726-1-1 (Print edition),
    ISBN 0-9550726-0-3 (Ebook edition).
    Further information:
    www.cognitionresearch.org.uk/books/sp_book/fly_leaf.htm

    The SP theory - which has been under development since 1987 - is
    a radical synthesis of ideas across human perception, cognition and
    development, artificial intelligence, computer science, theoretical
    linguistics, neuroscience, mathematics, logic, and epistemology. It
    is a theory of information processing in all kinds of system, both
    natural and artificial, a new paradigm for information processing
    which incorporates principles of minimum length encoding pioneered
    by Solomonoff, Kolmogoroff, Wallace, Rissanen, and others, but which
    is built from new foundations and differs at a fundamental level from
    any existing theory or system.

    The SP theory has a dual role. It is a theory of engineering, the
    basis for a proposed SP machine with applications in artificial
    intelligence and in data storage and retrieval. At the same time, it
    is a theory of information processing in brains and nervous systems
    both at an abstract level and at the more concrete level of neurons
    and neural processing.

    The theory and its applications - which are the subject of this book -
    will be of interest to a wide range of researchers, academics,
    professionals and students in computer science (especially artificial
    intelligence), cognitive science, human perception, cognition and
    development, theoretical and computational linguistics, neuroscience,
    mathematics, logic, and the philosophy of mind and language.

    The SP theory has a sound mathematical framework but the ideas are
    presented in a way that will be accessible to a wide audience,
    without being overburdened with mathematical equations or logical
    notations.

    After the Introduction, Chapter 2 describes ideas and observations on
    which the SP theory is founded, that have provided some motivation for
    the development of the theory, or are simply part of the background
    thinking for the theory. Chapter 3 describes the theory itself and
    one of the main computer models in which the theory is embodied. And
    Chapter 4 shows how the SP theory can model the operation of a
    universal Turing machine and describes advantages of the theory
    compared with earlier theories of computing.

    In Chapters that follow, applications of the SP theory are explored:
    in the processing of natural languages, in pattern recognition and
    information retrieval, in various kinds of probabilistic reasoning,
    in planning and problem solving, in the unsupervised learning of
    new knowledge (with a second computer model), and in the
    interpretation of concepts in mathematics and logic.

    Further chapters describe how the abstract theory may be realised
    with neural structures and neural processes, how the SP theory relates
    to some current themes in cognitive psychology and how the SP theory
    and projected 'SP machine' may be developed in the future.

    An Appendix describes the version of dynamic programming that forms
    the core of the SP computer models, with significant advantages
    compared with standard forms of dynamic programming.

    ---------------------------------

    An overview of the SP theory is presented in
    Artificial Intelligence Review 19(3), 193-230, 2003
    (see www.cognitionresearch.org.uk/papers/overview/overview.htm).

    ---------------------------------

    "A broad and detailed exploration of the implications of compression
    for computation and cognition, by one of the pioneers in the field."
    Prof. Nick Chater, Department of Psychology, University of Warwick.

    "A sophisticated approach to understanding the inferential potential
    of information compression. Wolff shows that the same computational
    machinery can be successfully applied in areas as diverse as logic,
    perception, and language acquisition. The unifying quality and
    mathematical elegance of his formalism make it an important
    contender amongst paradigms for machine learning and cognitive
    modelling alike." Dr Emmanuel Pothos, Department of Psychology,
    University of Wales, Swansea.
    Gerry, Jul 23, 2006
    #1
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