Berkeley Initiative in Soft Computing (BISC)
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Call for papers for the special issue of the
International Journal of Applied Mathematics and Computer Science
on Computing with Words (CW).
Computing with words is an emerging methodology in which the
objets of computation are words or propositions in a natural language.
Basically, computing with words serves to harness the high expressive
power of natural languages to provide solutions to problems which lie
beyond the reach of conventional computational techniques. In so doing,
computing with words opens the door to a wide-ranging enlargement of
the role of natural languages in the basic and applied sciences, and
especially in information processing, decision and control.
Computing with words draws upon many fields which in one way or
another relate to reasoning and computation in the context of natural
languages. Among such fields are fuzzy logic, granular computting,
semantics of natural languages and computational linguistics.
Papers which relate to computing with words directly or indirectly
are invited for submission. The deadline for submission is October 15,
The notification of acceptance/rejection will be sent by December 15,
2001.The Special Issue is scheduled to appear in
To facilitate the editorial process the interested authors are requested
to send a short e-mail message to the editors with a
tentative title of their intended contribution.
Danuta Rutkowska, E-mail: email@example.com
Janusz Kacprzyk, E-mail: firstname.lastname@example.org
Lotfi A. Zadeh, E-mail: zadeh@cs.Berkeley.edu
You are kindly invited to participate with presentation at the session
paradigms for understanding society" of the XVth World Congress of
Sociology (Brisbane, Australia, 7-13 July, 2002).
More information about the session you can find at
The deadline for a 500-1000 word abstract (sent by E-mail to me) is 31
I would like to invite you to attend the
IEEE International Conference on Software Maintenance, 2001, and
associated workshops: IEEE SCAM, IEEE WESS, IEEE WSE, TABOO.
FLORENCE, ITALY, 6-10 November 2001
Sponsored by IEEE
Supported bt the: EC-IST, University of Florence, O-Groupi, IBM Italy
in collaboration with: TABOO, AICA, AIIA, ERCIM, UNINFO, CESVIT, ...
ICSM is the major international conference in the field of software and
systems maintenance, evolution, and management.
Theme: Systems and Software Evolution in the era of the Internet
kEYWORDS: software evolution, embedded suystems, program analysis,
reengineering, managment, maintenance, lyfe cycle, Internet and
systems, Multimedia systems, User interface evolution, Commercial
(COTS), Program comprehension, Formal methods, Empirical studies,
Testing and regression testing, Measurement of software, METRICS,etc.
Outstanding Keynotes such as:
Prof. David Lorge Parnas and Prof. Dieter Rombach. Kent Beck
110 technical presentations, 4 workshops,
Industrial papers and experiences, reseach papers and award, tutorials,
tool expositions, dissertation forum and award, workshops, panels,
and other exciting activities have been planned.
Please forward the following to anybody who you think may be interested.
The discount for the advanced registration fee will be active for few
Apologies for multiple receptions.
If you would like to be removed from our list please send an email to
email@example.com with REMOVE in the subject.
(ICSM2001 General Chair)
CALL FOR PAPERS
We apologize if you receive multiple copies of this message.
We are organizing an exciting event: HIS'2001: International Workshop on
Hybrid Intelligent Systems in conjunction with The 14th Australian Joint
Conference on Artificial Intelligence (AI'01).
(Technically co-sponsored by The World Federation of Soft Computing)
HIS'01 is an International Workshop that brings together researchers,
developers, practitioners, and users of neural networks, fuzzy inference
systems, evolutionary algorithms and conventional techniques. The aim of
HIS'01 is to serve as a forum to present current and future research
well as to exchange research ideas in this field.
HIS'01 invites authors to submit their original and unpublished work
demonstrate current research using hybrid computing techniques and their
applications in science, technology, business and commercial.
Topics of interest include but not limited to:
Applications / techniques using the following, but not limited to:
* Machine learning techniques (supervised/unsupervised/
* Neural network and evolutionary Algorithms
* Neural networks and fuzzy inference systems
* Fuzzy clustering algorithms optimized using evolutionary
* Evolutionary computation (genetic algorithms, genetic
programming ,evolution strategies, grammatical evolution etc.)
* Hybrid optimization techniques (simulated annealing, tabu search,
* Hybrid computing using neural networks - fuzzy systems -
* Hybrid of soft computing and hard computing techniques
* Models using inductive logic programming, logic synthesis,
grammatical inference, case-based reasoning etc.
* Other intelligent techniques ( support vector machines,
rough sets, Bayesian networks, probabilistic reasoning,
minimum message length etc)
In soft computing, intelligent control theory, and in "data mining,"
is a "simple" basic question which has been revisited again and again by
How can any system (brain or software...) learn to approximate a
mapping from a vector of inputs X to a vector of outputs Y, when given a
database (fixed or real-time) of examples of X and Y?
(One example: Shankar Shastri of Berkeley and his student Claire Tomlin
have done excellent work in "hybrid control" -- which ends up requiring
nonlinear function approximator in the insides of the design. In fact,
is all quite close
to what we have done with approximate dynamic programming or
different words, different spins, but the same underlying mathemnatics.)
In neural networks, we call this the "supervised learning task." In
logic, Jim Bezdek
has called it "system identification." But in any case... one cannot
systems capable of
brain-like decision capability without a subsystem that can perform that
task (among others).
Thus I would argue that no model of learning in the brain could capture
of the brain, UNLESS it had enough richness to be able to handle this
Here is my concern: in the last few years, there has been a certain
of drifting apart
between the computational neuroscience world and the world of
Many people believe this is just fine... but what if the consequence is
that the neuroscience side
is dominated by models which cannot possibly explain the basic learning
capabilities of the system they
are studying? Perhaps there is a great need for some new mathematical
results which would explain
more clearly what the problem is, and encourage more interest in the
of model which can solve it.
(By the way, if anyone is interested... research on these lines would
well as one of the many topics of
great interest to what we fund in computational intelligence...)
Most people on this list probably know already that many types of ANN
fuzzy system are "universal approximators,"
that they can learn any smooth mapping from X to Y.
Some of you may know about the very important results of Andrew Barron
(statistician at Yale), related to some results
of Sontag of Rutgers: he proved, in effect, that some universal
approximators are a lot better than others.
There are lots of simple "smoothed lookup table" approximators which
fine for low dimensions...
but the number of parameters or hidden units required grows
with the number of inputs. But for multilayer perceptrons (MLPs)
the growth in complexity is only polynomial. This is an incredibly
important result. It says that MLPs may be viable
for large (brain-like) induction problems, while those others are not.
usual theorems for fuzzy logic approximation
and RBF approximation(and fuzzy ARTmap) are all based on some kind of
linear basis function argument, or someting very close to it,
which would imply an exponential growth in terms.
Now: DO THOSE RESULTS show that MLPS trained with backprop can perform
basic task of approximating at least smooth
nonlinear functions, and scaling up, while fuzzy and Hebbian systems
cannot? (If virtually all models now used in
computational neureoscience are of the Hebbian variety, continuous
or discrete (spiking), this is serious...)
For example... when I think about Elastic Fuzzy Logic (ELF, first
by me in the IIzuka 1992 proceedings, pretty much equivalent to
parts of some of the later designs of Yager and Fukuda)....
it is clear that feedback to redefine the membership functions and so on
can achieve a lot better, more parsimonious
approximation ability than mere preset lookup tables! I would conjecture
could also achieve Barron-like capability. And Cooper (of Nestor) has
played similar games
of tuning hidden units... long ago...
BUT: to achieve all this, one needs a FEEDBACK to train/select those
One may define a general concept of H-locality (Hebb-locality), similar
some of the rules Grossberg has
discussed, which prohibit both backpropagation and other similar types
feedback. The conjecture
is that ANY system made of simple units, whose learning must obey
H-locality, can never acheive
Barron-like polynomial scaling in approximating smooth functions. (
Technical point: **IF** one allows sneaky re-use
of weights, as in some of the multiplexing schemes Hecht-Nielsen has
about, one can actually
implement backpropagation itself in networks which obey some of the
Grossberg rules -- but is this a plausible way to model
biological systems? Or do the biologists implicitly add further rules
rule out this kind of multiplexing?)
In any case, to prove or disprove this would be of ENORMOUS scientific
Proving it... would eliminate the main reason for not using GENERALIZED
backpropagation more in neuroscience models...
Disproving it would presumably lead to new Hebbian-style learning models
that could actually work on the difficult
kinds of tasks which engineers need to address.
More capable neuroscience models would of course make it much more
plausible for engineers to seriously consider trying to use/miomic such
in addressing difficult computational tasks.
Either way, the resolution of this issue will be important to bridging
deep divide between "machine learning" (in the largest
sense) and studies of learning in the brain.
Best of luck.
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