Table Of ContentThe State of the Art
in Computational Intelligence
Advances in Soft Computing
Editor-in-chief
Prof. Janusz Kacprzyk
Systems Research Institute
Polish Academy of Sciences
ul. Newelska 6
01-447 Warsaw, Poland
E-mail: [email protected]
http://www.springer.de/cgi-binlsearch-bock.pl?series=4240
Esko Turunen
Mathematics Behind Fuzzy Logic
1999. ISBN 3-7908-1221-8
Robert Fuller
Introduction to Neuro-Fuzzy Systems
2000. ISBN 3-7908-1256-0
Robert John and Ralph Birkenhead (Eds.)
Soft Computing Techniques
and Applications
2000. ISBN 3-7908-1257-9
Mieczyslaw Klopotek, Maciej Michalewicz
and Slawomir T. Wierzchori (Eds.)
Intelligent Information Systems
2000. ISBN 3-7908-1309-5
Peter Sincak . Jan Vascak
Vladimir Kv asnicka . Radko Mesiar (Eds.)
The State of the Art in
Computational Intelligence
Proceedings of the European Symposium
on Computational Intelligence
held in Kosice, Slovak Republic,
August 30-September 1, 2000
With Forewords by
Lotfi A. Zadeh, David E. Goldberg and Kunihiko Fukushima
With 130 Figures
and 41 Tables
Springer-V erlag Berlin Heidelberg GmbH
Ass. Prof. Dr. Peter Sincâk:
lan
Dr. Vascâk:
Technical University of Kosice
Faculty of Electrical Engineering and Inforrnatics
Letna 9/B
04000 Kosice
Slovakia
E-mail: [email protected]
[email protected]
Prof. Dr. Vladimir K vasnicka
Slovak University of Technology Bratislava
Faculty of Chemi cal Technology
Radlinskeho 9
81237 Bratislava
Slovakia
E-mail: [email protected]
Prof. Dr. Radko Mesiar
Slovak University of Technology Bratislava
Faculty of Civil Engineering
Radlinskeho 11
813 68 Bratislava
Slovakia
E-mail: [email protected]
ISSN 1615-3871
Die Deutsche Bibliothek - CIP-Einheitsaufnahme
The state of the art in computational intelligence: with 41 tables / Peter Sincăk, Jan VaScăk, Vladimir Kvas
nicka, Radko Mesiar (eds.). With Forew. by Lotfi A. Zadeh ... ·-Heidelberg; New York: Physica-Verl., 2000
(Advances in soft computing)
ISBN 978-3-7908-1322-7 ISBN 978-3-7908-1844-4 (eBook)
DOI 10.1007/978-3-7908-1844-4
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Forewords
written by Lotti A. Zadeh
David E. Goldberg
Kunihiko Fukushima
The Role of Fuzzy Systems in Computational
Intelligence
Computational Intelligence was born close to two decades ago, at a time
when there was a growing consensus that traditional AI was too deeply an
chored to Aristotelian logic, and intolerant of uncertainty, imprecision and
partial truth.
Today, Computation Intelligence, or CI, for short, is generally viewed as
a consortium of methodologies which play important roles in the concep
tion, design and utilization of information/intelligent systems. The principal
members of the consortium are: fuzzy logic (FL); neuro computing (NC); evo
lutionary computing (EC); probabilistic computing (PC); chaotic computing
(CC); and parts of machine learning theory (ML). What is important about
these methodologies is that, for the most part, they are complementary and
synergistic, rather than competitive. A common theme is the principle: Ex
ploit the tolerance for imprecision, uncertainty and partial truth to achieve
tractability, robustness, low solution cost and better rapport with reality.
Within CI, fuzzy set theory or fuzzy system theory, or fuzzy logic ~ which
is the label in common use today ~ provide a conceptual framework for dealing
with partiality of truth, possibility and dependence. Thus, fuzzy logic cuts
across other methodologies and provides a common foundation.
A source of frequent misunderstanding is that the term "fuzzy logic" has
two distinct meanings. In a narrow sense, fuzzy logic is a logical system which
underlies the modes of reasoning which are approximate rather than exact.
But in a wide sense ~ which is in dominant use today ~ fuzzy logic is much
more than a logical system; it is, in effect, coextensive with fuzzy set theory.
More specifically, fuzzy logic in its wide sense, FL, has four principal facets
which overlap and have unsharp boundaries.
The first facet, FL/L, is the logical facet ~ a facet which is coextensive
with fuzzy logic in its narrow sense. The second facet, the set-theoretic facet,
FL/S, is the part of FL which is concerned with classes which have unsharp
boundaries. My 1965 paper on fuzzy sets dealt with this facet. Today, most
papers in the mathematical literature on fuzzy sets relate to the set-theoretic
facet.
The third facet, the relational facet, FL/R, is concerned with represen
tation and analysis of imprecise dependencies. Most applications of fuzzy
logic, especially in the realms of consumer electronics, industrial systems and
control fall within the province of this facet.
The fourth facet, the epistemic facet, FL/E, is concerned with knowledge,
meaning and information. Possibility theory is a part of this facet, as is
possibilistic logic, which is shared with the logical facet.
VII
The core of FL is centered in two basic concepts: fuzzification and gran
ulation, along with their conjunction, fuzzy granulation. Fuzzification, or f
generalization, is a mode of generalization in which a set is replaced by a fuzzy
set. Fuzzy granulation, or f.g-generalization, is a mode of generalization in
which a fuzzy set is partitioned into fuzzy granules, with a granule being a
clump of points (objects) which are drawn together by indistinguishability,
similarity, proximity or functionality. For example, the fuzzy granules of a
face are the nose, chin, cheeks, lips, etc. Fuzzy granulation plays a pivotal
role in fuzzy logic and its applications; it underlies the two most important
concepts in FL, namely, the concepts of a linguistic variable and fuzzy if-then
rule sets.
A new and important direction in fuzzy logic is related to what may be
called the computational theory of perceptions (CTP). This theory, which
is based on fuzzy logic, provides a machinery for processing of information
which is perception-based, e.g., "it is likely to rain later in the evening,"
"most Swedes are blond," etc. Existing scientific theories do not have this
capability.
By providing a machinery for computation with perceptions, CTP opens
the door to a major enlargement of the role of natural languages in scientific
theories, especially in probability theory, decision analysis and control. A
countertraditional move in this direction has the potential for leading to a
significant paradigm shift in both basic and applied sciences.
The Proceedings of the ESCI Symposium serves an important function
by providing a broad and authoritative exposition of the constituent method
ologies of C1. What is of particular importance is inclusion of contributions
which describe so-called hybrid systems, that is, systems in which the con
stituent methodologies are used in combination rather than in a stand-alone
mode. In coming years, hybrid systems are likely to be the norm rather than
exception. Such systems are certain to have a much higher MIQ (Machine
IQ) than those we can design today. We are entering the age of informa
tion/intelligent systems - an age in which computational intelligence will
play a principal role.
Lotfi A. Zadeh
Berkeley, CA April 24, 2000
A Meditation on Computational Intelligence and
Its Future
It is an honor for me to have been asked to write a foreword for the proceedings of the
2000 European Symposium on Computational Intelligence in Slovakia. The rise of
computational intelligence (CI) as an alternative to earlier artificial intellegence (AI)
paradigms is reflected in the submitted paper statistics. Of the 106 papers that were
submitted, 35 were devoted to neural networks, 25 involved fuzzy systems, 10 were
devoted to genetic and evolutionary computation, six were devoted to artificial life, and
20 involved hybrids of the constituent methods. Indeed, the symposium organizers
should be congratulated on having assembled such a diverse collection of interesting
and useful papers, but as I scan the statistics, I can't help wonder about the larger
lessons of this symposium for the field and its future. In the remainder of the foreword,
I reflect on the rise of CI, wonder whether CI will ultimately be successful in
influencing the course of human events, and then briefly discuss perhaps the single
most important requirement for C1 systems of the future.
The rise of CI. Computational intelligence has been on the rise whether viewed
individually from the perspective of the constituent disciplines-neural computation,
fuzzy systems, and genetic and evolutionary computation (GEC)-or from the
perspective of joint activities such as this one. In my own discipline, genetic and
evolutionary computation, a recent on-line listing contained exactly 50 workshops,
symposia, and conferences between now (April 2000) and next March. In other words,
one could do nothing but traipse around the world and attend GEC-related events every
week of the year if one was so inclined, and this growing popularity is remarkable. Not
long ago there was only one GEC conference, the first International Conference on
Genetic Algorithms (1985), and all of about 60 people attended. By way of contrast,
last year the Genetic and Evolutionary Computation Conference (GECCO-99) in
Orlando drew 619 attendees, so the growth in both numbers of events and attendees per
event has been substantial. .
Is CI successful? But our success in numbers-our success in talking amongst
ourselves-has not yet translated to having influence in the most prestigious academic
departments, organizations, and industries. With rare exception, these positions are
still held by those who grew up with artificial intelligence as their guiding technology.
This situation reminds me of a story told of the German physicist Max Planck. A
friend approached him and asked him how the acceptance of the new physics was
going. The great man is reputed to have said, "Fine, lots of funerals." Our situation is
simultaneously similar and dissimilar to that of the new physics. A substantial mind
makeover is required to accept the differences in philosophy and outlook of CI versus
IX
the earlier AI, but the time rate of discovery of new knowledge is no longer well
matched to the funeral rate of earlier thinkers.
Of course I should be quick to point out that I am not suggesting that this problem is
one we should actively try to solve through an increase in the number of homicides or
unexplained accidental deaths. But it is an interesting question for the survival of the
field, as we must imagine a pathway to both intellectual and political success if the
field of computational intelligence and its constituent disciplines are to survive and
continue to influence the course of human events. Certainly, the growth of the field is
itself a remarkable fact given the low levels of corporate or governmental support
driving our study. Ours is an insurgency led by passionate researchers, and we should
not underestimate the power of passion in the promotion of important ideas. But the
pathway to wider acceptance takes more than passionate commitment, and I believe the
pathway for widespread CI ascendance is presenting itself to us in the form of
(1) scalable results, (2) with practical import, using (3) little or economic models or
theory.
Scalable results. The AI winter following the bust of the expert systems bubble in the
1980s was the result of systems that worked well in the pilot stages, but were too brittle
as they were tried on larger, real-world systems. CI has been more concerned with
results that scale well from its very inception, and fielded CI systems continue to grow
and prosper; we have not yet "hit the wall" on the growth of these systems in practice.
Practicalilty. Moreover, it seems to me that CI has been tied more closely to practice
from its beginnings with real problems tackled earlier and more often. Early AI was
characterized by the solution of "blocks world" types of problems, but early fuzzy,
neural, and genetic systems have been solving complex simulation, optimization, and
control problems for some time, and the trend seems likely to continue.
Little or economic models. Additionally, the growth of CI has been accompanied by a
concomitant interest in applicable theory or what I have called little models or
economic models. Where early AI seemed content with the firewall that exists in the
computer science curriculum between the rigor of algorithm theory on the one hand
and fielded systems on the other, CI seeks out scaling laws, bounding theories, or
equations that are only complex enough to advance the state of CI design. Some may
decry the loss of rigor this suggests, but I view it as an advantage of CI, plain and
simple, and elsewhere in this proceedings and at the symposium I discuss this view in
more depth. Simply stated, the use of scaling laws is extraordinarily helpful in the
design of complex CI systems. The Wright brothers used lift coefficients and drag
coefficients to build Flyer I, but they did not use the Navier-Stokes equations.
Likewise, we must use models to design complex CI systems, but the models must be
x
simple enough to be tractable in design usage at the same time they are sufficiently
predictive to answer the design questions that arise.
The coming integration in our CI future. These three factors, I believe, bode well
for the future of our field. As more people come to the party, it is essential that our
systems scale well, are practical, and are guided by solid and appropriately complex
theories or models. Many of the papers of this volume reflect these values, and better
understanding and articulation of CI's competitive advantage in the marketplace of
ideas can only help future researchers and practitioners to accentuate these matters in
their own work. But as we turn to the future, one conference statistic stands out as a
harbinger of things to come. Recall that some 20 of the 106 submitted papers involved
a hybrid of two or more of the constituent methods of CI, and this, I believe, is our
collective future.
Individually the constituent methods of computational intelligence are powerful. Each
has a driving metaphor, a driving logic, and a driving base of successful application,
but the future of our field lies in the integration of the best of the constituent
technologies. Some of this drama is being played out today on the pages of this very
proceedings through simple combinations of neural-fuzzy, neuro-genetic, fuzzy
genetic, or fuzzy-neuro-genetic systems, but the careful integration of our methods into
autonomously intelligent systems goes beyond simple combinations. Subtle integration
of the abstraction power of fuzzy systems, the associative powers of neurocomputing,
and the innovating power of genetic systems requires a design sophistication beyond
our current means and a better sense of economy of hybridization than has been
recognized heretofore. Some of these matters are now receiving attention, and their
study will grow. Nonetheless, the integration of the constituent technologies into
powerful competent systems is off on a not-too-distant horizon. This proceedings
represents a few more steps toward that now-visible goal, and I urge the contributors to
and readers of this volume to join together to hasten the day when that goal shall be
reached.
David E. Goldberg
University of Illinois at Urbana-Champaign
Urbana, IL 61801
