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SSM-COMPUTING  July 2006

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Subject:

Unifying computing and cognition

From:

Gerry Wolff <[log in to unmask]>

Reply-To:

Gerry Wolff <[log in to unmask]>

Date:

Sun, 23 Jul 2006 12:26:06 +0100

Content-Type:

text/plain

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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.

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