2002 XX, 534 p. 20 illus. Softcover
3-7908-1510-1
A comprehensive introduction to mathematical
structures essential
for Rough Set Theory. The book enables the
reader to
systematically study all topics of rough
set theory. After a
detailed introduction in Part 1 along with
an extensive
bibliography of current research papers.
Part 2 presents a self-contained
study that brings together all the relevant
information from
respective areas of mathematics and logics.
Part 3 provides an
overall picture of theoretical developments
in rough set theory,
covering logical, algebraic, and topological
methods. Topics
covered include: algebraic theory of approximation
spaces,
logical and set-theoretical approaches to
indiscernibility and
functional dependence, topological spaces
of rough sets. The
final part gives a unique view on mutual
relations between fuzzy
and rough set theories (rough fuzzy and fuzzy
rough sets). Over
300 excercises allow the reader to master
the topics considered.
The book can be used as a textbook and as
a reference work.
Keywords: Fuzzy Set Theory, Fuzzy Sets, Mathematical
Foundations,
Rough Sets, Rough Sets Theory
Series: Advances in Soft Computing.
2003 IX, 283 p. Hardcover
3-540-42027-4
This book provides a systematic approach
to knowledge
representation, computation, and learning
using higher-order
logic. It is aimed at researchers, graduate
students, and senior
undergraduates working in computational logic
and/or machine
learning. For those interested in computational
logic, it
provides a framework for knowledge representation
and computation
based on higher-order logic, and demonstrates
its advantages over
more standard approaches based on first-order
logic. For those
interested in machine learning, the book
explains how higher-order
logic provides suitable knowledge representation
formalisms and
hypothesis languages for machine learning
applications in which
the individuals about which something is
to be learned have
complex internal structure requiring graphs,
sets, multisets,
lists, and so on, for their representation.
Keywords: Machine learning, algorithmic learning,
artificial
intelligence, computational learning, computational
logic, higher-order
logic, knowledge representation
Contents: Part I: Prologue.- Overview.- Introduction
to Learning
and Logic.- Part II: Logic.- Higher-order
Logic.- Representation
of Individuals.- Predicate Construction.-
Programming with
Equational Theories.- Part III: Learning.-
The Problem of
Learning.- Knowledge Representation for Learning.-
Learning
Systems.- Illustrations for Various Types.-
Applications.-
References.- Notation.- Index.
Series: Cognitive Technologies.
3rd ed. 2003 XI, 236 p. 120 illus., 107 in
color. With CD-ROM.
Hardcover
3-540-44010-0
The patterns on the shells of tropical sea
snails are not only
compellingly beautiful but also tell a tale
of biological
development. The decorative patterns are
records of their own
genesis, which follows laws like those of
dune formation or the
spread of a flu epidemic. Hans Meinhardt
has analyzed the
dynamical processes that form these patterns
and retraced them
faithfully in computer simulations. His book
is exciting not only
for the astonishing scientific knowledge
it reveals but also for
its fascinating pictures. An accompanying
CD-ROM with the
corresponding algorithms offers wide scope
to those who wish to
try their hand at simulating and varying
the patterns.
Keywords: Dynamische Prozesse, Visualisierung,
Wachstumsprozesse,
artificial life, computer graphics, naturliche
Phanomene
Contents: Shell patterns as dynamic systems.-
Pattern formation.-
Oscillation and travelling waves.- Superposition
of stable and
periodic patterns.- Meshwork of oblique lines
and staggered dots.-
Branch initiation by global control.- The
big problem: two or
more time-dependent patterns.- Triangles.-
Parallel lines with
tongues.- Shell models in three dimensions.-
The computer program.-
Appendix: Pattern formation in the development
of higher-level
organisms.
System requirements: CD-ROM fur Mac OS und
Windows-PC (browserfahig),
mit MS-Explorer, Netscape
Series: The Virtual Laboratory.
2003 XV, 346 p. 94 illus. Hardcover
0-387-95461-9
This book offers a detailed account of IBM's
Deep Blue chess
program, the people who created it, and its
historic battles with
World Chess Champion Garry Kasparov. The
text examines the
progress made by the creators of Deep Blue,
beginning with the1989
two-game match against Kasparov. The heroes
are: IBM researchers
Feng-hsiung Hsu, Murray Campbell, and Joe
Hoane, along with team
leader Chung-Jen Tan and International Grandmaster
Joel Benjamin.
The text chronicles one of the great technology
achievements of
the 20th Century. It establishes the point
in history when
mankind's exciting new tool, the computer,
came of age and
competed with its human creators in the ultimate
intellectual
competition: a game of chess. This book will
serve as the premier
story documenting that achievement and a
milestone in the
development of artificial intelligence.
Contents: Intellectual equals.- Testing the
waters.- Gaining
experience with Deep though, 1990-1992.-
Surviving Deep cuts.-
Deep thought II: Waiting for Deep Blue, 1994-1995.-
Deep Blue
prototype debuts in Beijing, September 1995.-
Preparing for
Philly.- ACM Chess Challenge, February 1996.-
Warm reception
initiates rematch negotiations.- A faster
and smarter Deep Blue.-
Kasparov's career peaks.- Countdown to the
rematch.- IBM Kasparov
versus Deep Blue rematch, games 1-5, May
1997.- IBM Kasparov
versus Deep Blue rematch, game 6.- An incredible
ending.- Deep
Blue is triumphant.- Kasparov's difficulties
in retrospect.- The
bottom line.- Milestone in advancement in
computer technology.-
Light side of Deep
2003 X, 265 p. Softcover
1-85233-689-7
Information usually comes in pieces, from
different sources. It
refers to different, but related questions.
Therefore information
needs to be aggregated and focused onto the
relevant questions.
Considering combination and focusing of information
as the
relevant operations leads to a generic algebraic
structure for
information. This book introduces and studies
information from
this algebraic point of view. Algebras of
information provide the
necessary abstract framework for generic
inference procedures.
They allow the application of these procedures
to a large variety
of different formalisms for representing
information. At the same
time they permit a generic study of conditional
independence, a
property considered as fundamental for knowledge
presentation.
Information algebras provide a natural framework
to define and
study uncertain information. Uncertain information
is represented
by random variables that naturally form information
algebras.
This theory also relates to probabilistic
assumption-based
reasoning in information systems and is the
basis for the belief
functions in the Dempster-Shafer theory of
evidence.
Keywords: AI, Applied Mathematics, Computer
Science, Economics,
Engineering, Management Science, Statistics
Series: Discrete Mathematics and Theoretical
Computer Science.