UNIFYING COMPUTING AND COGNITION
The SP Theory and its Applications
J Gerard Wolff
ISBN 0-9550726-1-1 (Print edition),
ISBN 0-9550726-0-3 (Ebook edition).
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
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
“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.