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Alife Digest Number 105
Alife Digest, Number 105
Monday, June 7th 1993
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
~ Artificial Life Distribution List ~
~ ~
~ All submissions for distribution to: alife@cognet.ucla.edu ~
~ All list subscriber additions, deletions, or administrative details to: ~
~ alife-request@cognet.ucla.edu ~
~ All software, tech reports to Alife depository through ~
~ anonymous ftp at ftp.cognet.ucla.edu in ~ftp/pub/alife (128.97.50.19) ~
~ ~
~ List maintainers: Liane Gabora and Rob Collins ~
~ Artificial Life Research Group, UCLA ~
~ ~
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
Today's Topics:
Calendar of Alife-related Events
Artificial Life research in Brazil ?
Paper available - the Ghost in the Machine
International Summer School "Let's Face Chaos through Non-Linear Dyn."
PhD Dissertation available
----------------------------------------------------------------------
Date: Mon, 7 Jun 93 21:54:33 -0700
From: liane@CS.UCLA.EDU (Liane Gabora)
Subject: Calendar of Alife-related Events
**********************************************************************
Intnl Workshop on Neural Networks, Barcelona Spain June 9-11, 1993 v76
World Congress on Neural Networks, Portland, OR July 11-15, 1993 v95
Intelligent Systems for Molecular Biology, Washington July 7-9, 1993 v84
Fifth Intnl Conf on GAs, Urbana-Champaign IL July 17-22, 1993 v80,100
Dynamically Interacting Robots Workshop Late Aug, 1993 v91
Neural Networks and Telecommunications, Princeton, NJ October 18-20,1993 v100
Fluctuations and Order, Los Alamos, NM Sept 9-12, 1993 v102
Neural Information Processing Systems, Denver, CO Nov 29-Dec 2, 1993 v98
Third Conf on Evolutionary Programming, San Diego, CA Feb 24-25, 1994 v103
Cybernetics and Systems Research, Vienna April 5-8, 1994 v101,103
Intnl Conf Knowledge Rep and Reasoning, Bonn, Germany May 24-27, 1994 v101
Simulation of Adaptive Behavior, Brighton, UK Aug 8-12, 1994 v101
Parallel Problem Solving in Nature, Jerusalem, Israel Oct 9-14, 1994 v102
Congress on Medical Informatics, Sao Paulo, Brazil Sept 9-14, 1995 v91
(Send announcements of other activities to alife@cognet.ucla.edu)
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------------------------------
Date: Fri, 28 May 1993 23:24:53 BSC (-0300 C)
From: SABBATINI@CCVAX.UNICAMP.BR
Subject: Artificial Life research in Brazil ?
I am organizing a symposium on Computer Applications in Biology next
August, in Campinas, Brazil (State University of Campinas). One of the
invited lectures will be devoted to Artificial Life. This is quite a
novelty in Brazil, particularly for biologists.
I am looking for Brazilian groups and individuals who are active in
research in this area. Addressing a plea to this list was the simplest
way I thought of. However, if someone from outside Brazil is willing
to come, I can arrange for an official invitation. Only snag is that I
have no funds available to support travel expenses (only lodging and
food).
The symposium will be held from 4 to 6 August and it is accepting
poster submissions until 30th June.
Best regards and thanks for the help
Renato M.E. Sabbatini, PhD
Director, Center for Biomedical Informatics
State University of Campinas
Campinas, Brazil
sabbatini@ccvax.unicamp.br
sabbatini@bruc.bitnet
------------------------------
Date: 03 Jun 93 11:30:33 EDT
From: Andrew Wuensche <100020.2727@CompuServe.COM>
Subject: Paper available - the Ghost in the Machine
The Ghost in the Machine
========================
Cognitive Science Research Paper 281, University of Sussex.
The following paper describes recent work on the basins of attraction of
random Boolean networks, and implications on memory and learning.
Currently only hard-copies are available. To request copies, send
email to:
andywu@cogs.susx.ac.uk, or write to
Andy Wuensche, 48 Esmond Road, London W4 1JQ, UK
giving a surface mail address.
A B S T R A C T
---------------
The Ghost in the Machine
Basins of Attraction of Random Boolean Networks
This paper examines the basins of attraction of random Boolean networks,
a very general class of discrete dynamical systems, in which cellular
automata (CA) form a special sub-class. A reverse algorithm is presented
which directly computes the set of pre-images (if any) of a network's
state. Computation is many orders of magnitude faster than exhaustive
testing, making the detailed structure of random network basins of
attraction readily accessible for the first time. They are portrayed as
diagrams that connect up the network's global states according to their
transitions. Typically, the topology is branching trees rooted on
attractor cycles.
The homogeneous connectivity and rules of CA are necessary for the
emergence of coherent space-time structures such as gliders, the basis of
CA models of artificial life. On the other hand random Boolean networks
have a vastly greater parameter/basin field configuration space capable
of emergent categorisation.
I argue that the basin of attraction field constitutes the network's
memory; but not simply because separate attractors categorise state space
- in addition, within each basin, sub-categories of state space are
categorised along transient trees far from equilibrium, creating a
complex hierarchy of content addressable memory. This may answer a basic
difficulty in explaining memory by attractors in biological networks
where transient lengths are probably astronomical.
I describe a single step learning algorithm for re-assigning
pre-images in random Boolean networks. This allows the sculpting of their
basin of attraction fields to approach any desired configuration. The
process of learning and its side effects are made visible. In the context
of many semi-autonomous weakly coupled networks, the basin field/network
relationship may provide a fruitful metaphor for the mind/brain.
------------------------------
Date: Thu, 03 Jun 1993 19:08:11 +0200
From: summerschool.chaos@uni-lj.si
Subject: International Summer School "Let's Face Chaos through Non-Linear Dyn."
INTERNATIONAL SUMMER SCHOOL AT THE UNIVERSITY OF LJUBLJANA
"LET'S FACE CHAOS through NON-LINEAR DUNAMICS"
September 26 - October 4, 1993 Ljubljana and Portoroz, Slovenia
I am sure you are wondering:
"WHAT'S IN IT FOR ME?"
If you are already working in this field you can present your work
on a lecture or prepare a workshop. In that case please send us the
material you would like to lecture, your CV and bibliography as soon
as possible. All the materials lectured on the summer school
and presented on the workshops will be printed in the book that will
be available two months after the end of the summer school.
If you are an undergraduate or graduate student, the knowledge you
can acquire by taking part in our summer school is useful in almost
any field in which you are majoring.
In addition, facing chaos through non-linear dynamics is not as
distant from our senses as are the other two scientific revolu-
tions of the twentieth century - the theory of relativity and
quantum mechanics.
As you will learn if you join us, all you need is pencil and
paper or computer, and of course, KNOWLEDGE. The latter will
surely be enriched: for this very purpose, experts from all
around the world have been invited to lecture to us!
"ANY SOCIAL EVENTS?"
The first day's lectures will take place in Portoroz, on the
Adriatic coast. On the way back to Ljubljana, where the rest of
the program continues, we will stop at the Lipica horse stables
and Postojna Cave. We have also prepared a tour through Ljublja-
na, the capital of Slovenia.
Further special events will take place in the evenings: concerts,
plays, etc. There will also be a weekend away to give you a
chance to get to know the Alpine region of Slovenia.
"AND HOW TO APPLY?"
NOTE: THE APPLICATION DEADLINE IS JULY 26, 1993!
DO NOT HESITATE TO CONTACT US IF YOU NEED ANY FURTHER INFORMATION,
AND THEN JOIN US IN SEPTEMBER AT OUR SUMMER SCHOOL HERE IN LJUBLJANA
AND PORTOROZ.
WE WILL MAKE SURE THAT THIS SUMMER SCHOOL IS A MEMORABLE EDUCATIONAL
EVENT WITH LOTS OF FUN!
YOUR ORGANISING COMMITTEE!
PRELIMINARY PROGRAMME:
1. INTRODUCTION
Synergetic approach to self-organising systems
Discrete versus continuous representation of dynamic systems
Mathematical background
Physical background
Open systems
Information dynamics
Dissipative systems
Evolutionary approach
Fractal graphics - geometry of chaos
2. APPLICATIONS
Qualitative and quantitative analysis of time series
Modelling and simulation of system dynamics
Qualitative modelling - problems and perspectives
Artificial intelligence and system dynamics
Prediction of chaotic dynamics with neural networks
Applications in:
Engineering:
- Electrical circuits
- Chemical reactions
- Architecture
- Working processes
- Meteorology
Physiology:
- EEG
- EKG
- Blood flow
- Fractal development of lung's capillaries
- Ion channels
- Calcium oscillations through the membrane
Physics:
- Quantum physics
- Fluid dynamics
- Plasma
Ecological modelling
Economy:
- Share prices
- Financial systems
GENERAL INFORMATION ABOUT THE SUMMER SCHOOL
Participants: Undergraduate and postgraduate students
and others interested in topic.
Educational requirements:
Basic knowledge of differential equations desired.
Theory & Applications:
The participants will be given the opportunity to
extend the theoretical knowledge acquired
at the lectures through practical work at workshops
which are also a part of the summer school programme.
Visits:
- Laboratories at the University of Ljubljana
- Jozef Stefan Institute
- Some sponsoring companies.
Certificate of Attendance: students will receive a
Certificate of Attendance.
Book: A book on the summer school topic will be available
two months after the end of the summer school.
Scientific advisers:
Dr. Aneta Stefanovska, Faculty of Electrical and
Computer Engineering, University of Ljubljana
Prof. Dr. Marko Robnik, Center for Theoretical
Physics and Applied Mathematics, University of
Maribor
Prof. Dr. Igor Grabec, Faculty of Mechanical
Engineering, University of Ljubljana
ORGANISING COMMITTEE:
Maja Malus, President
Matija Golner, Alenka Kavkler, Suzana Domjan
Mateja Forstnaric, Anton Kos, Marko Krek
Peter Groselj, Alenka Lamovec, Spela Nardoni
Natasa Petre, Martin Raic, Vlado Stankovski
Peter Ribaric, Alexander Simonic, Robert Zerjal
DEPARTURE NOTE
(CAN BE HANDED TO US UPON YOUR ARRIVAL!)
Departure date:______________________________(dd-mm-yy)
Time:________________________________________
From: Ljubljana AIRPORT
Ljubljana TRAIN station
Ljubljana BUS station
Do you need to confirm your flight: YES NO
Deadline:____________________________________
APPLICATION FORM
1. Name:____________________
2. Surname:____________________
3. Home address or mailing address:
__________________
4. Phone number:_____________________
5. Fax:____________________
6. E-mail:____________________
7. Country:_____________ 8. Passport number:___________
9. Sex: F M 10. Birth date:__________
11. University:_________________________________
12. Field of study:______________________________
13. Year :____________
14. Would you prefer lodging with the family of one of our students in
the Organising Committee team (IN THIS CASE THE
APPLICATION FEE is 100 ECU instead of 150 ECU):
HOTEL HOME
15. Are you vegetarian: YES NO
16. Are you a Smokin' Joe: YES NO
17. Other special wishes, e. g. for visa requirements etc.:
PLEASE INCLUDE A NOTE OF A FEW LINES
EXPLAINING WHY YOU ARE APPLYING!
YOU CAN COVER THE REGISTRATION FEE ON ARRIVAL!
ARRIVAL NOTE
1. Name:___________________________________
2. Country:_________________________________
3. Arrival date:________________ (dd-mm-yy)
4. Arrival time:________________
5. At: Ljubljana AIRPORT
Ljubljana BUS STATION
Ljubljana TRAIN STATION
OUR OFFICE
6. By: Plane Flight No:____________
Train
Bus
Car
Bike
OUR ADDRESS:
IAESTE LC Ljubljana and BEST Ljubljana
Mednarodna pisarna SOU
Kersnikova 4
61000 Ljubljana
Tel.: + 38 61 318 564
Fax: + 38 61 319 448
E_mail: summerschool.chaos@uni-lj.si
------------------------------
Date: Mon, 7 Jun 93 10:41 MET
From: SCHOLTES@ALF.LET.UVA.NL
Subject: PhD Dissertation available
===================================================================
As I had to disapoint many people because I run out of
copies in the first batch, a high-quality reprint has
been made from.......................................
........REPRINT........
Ph.D. DISSERTATION AVAILABLE
on
Neural Networks, Natural Language Processing, Information Retrieval
292 pages and over 350 references
===================================================================
A Copy of the dissertation "Neural Networks in Natural Language Processing
and Information Retrieval" by Johannes C. Scholtes can be obtained for
cost price and fast airmail- delivery at US$ 25,-.
Payment by Major Creditcards (VISA, AMEX, MC, Diners) is accepted and
encouraged. Please include Name on Card, Number and Exp. Date. Your Credit
card will be charged for Dfl. 47,50.
Within Europe one can also send a Euro-Cheque for Dfl. 47,50 to:
(include 4 or 5 digit number on back of cheque!)
University of Amsterdam
J.C. Scholtes
Dufaystraat 1
1075 GR Amsterdam
The Netherlands
scholtes@alf.let.uva.nl
Do not forget to mention a surface shipping address. Please allow 2-4
weeks for delivery.
Abstract
1.0 Machine Intelligence
For over fifty years the two main directions in machine intelligence (MI),
neural networks (NN) and artificial intelligence (AI), have been studied
by various persons with many dfferent backgrounds. NN and AI seemed
to conflict with many of the traditional sciences as well as with each other.
The lack of a long research history and well defined foundations
has always been an obstacle for the general acceptance of machine
intelligence by other fields.
At the same time, traditional schools of science such as mathematics and
physics developed their own tradition of new or "intelligent" algorithms.
Progress made in the field of statistical reestimation techniques such as the
Hidden Markov Models (HMM) started a new phase in speech recognition.
Another application of the progress of mathematics can be found in the
application of the Kalman filter in the interpretation of sonar and radar
signals. Much more examples of such "intelligent" algorithms can be found in
the statistical classification en filtering techniques of the study of
pattern recognition (PR).
Here, the field of neural networks is studied with that of pattern
recognition in mind. Although only global qualitative comparisons are made,
the importance of the relation between them is not to be underestimated. In
addition it is argued that neural networks do indeed add something to the
fields of MI and PR, instead of competing or conflicting with them.
2.0 Natural Language Processing
The study of natural language processing (NLP) exists even longer than that
of MI. Already in the beginning of this century people tried to analyse
human language with machines. However, serious efforts had to wait until
the development of the digital computer in the 1940s, and even then,
the possibilities were limited. For over 40 years, symbolic AI has been the
most important approach in the study of NLP. That this has not always
been the case, may be concluded from the early work on NLP by Harris. As a
matter of fact, Chomsky's Syntactic Structures was an attack on the lack of
structural proper-ties in the mathematical methods used in those days. But,
as the latter's work remained the standard in NLP, the former has been
forgotten completely until recently. As the scientific community in NLP
devoted all its attention to the symbolic AI-like theories, the only use-
ful practical implementation of NLP systems were those that were based on
statistics rather than on linguistics. As a result, more and more scientists
are redirecting their attention towards the statistical techniques a
vailable in NLP. The field of connectionist NLP can be considered as a
special case of these mathematical methods in NLP.
More than one reason can be given to explain this turn in approach. On the
one hand, many problems in NLP have never been addressed properly by
symbolic AI. Some examples are robust behavior in noisy environments,
disambiguation driven by different kinds of knowledge, commensense
generalizations, and learning (or training) abilities. On the other hand,
mathematical methods have become much stronger and more sensitive to spe-
cific properties of language such as hierarchical structures.
Last but not least, the relatively high degree of success of mathematical
techniques in commercial NLP systems might have set the trend towards the
implementation of simple, but straightforward algorithms.
In this study, the implementation of hierarchical structures and semantical
features in mathematical objects such as vectors and matrices is given much
attention. These vectors can then be used in models such as neural networks,
but also in sequential statistical procedures implementing similar
characteristics.
3.0 Information Retrieval
The study of information retrieval (IR) was traditionally related to
libraries on the one hand and military applications on the other. However,
as PC's grew more popular, most common users loose track of the data they
produced over the last couple of years. This, together with the introduction
of various "small platform" computer programs made the field of IR relevant
to ordinary users.
However, most of these systems still use techniques that have been developed
over thirty years ago and that implement nothing more than a global
surface analysis of the textual (layout) properties. No deep structure
whatsoever, is incorporated in the decision whether or not to retrieve a
text.
There is one large dilemma in IR research. On the one hand, the data
collections are so incredibly large, that any method other than a global
surface analysis would fail. On the other hand, such a global analysis could
never implement a contextually sensitive method to restrict the number of
possible candidates returned by the retrieval system. As a result, all
methods that use some linguistic knowledge exist only in laboratories and
not in the real world. Conversely, all methods that are used in the real
world are based on technological achievements from twenty to thirty
years ago.
Therefore, the field of information retrieval would be greatly indebted
to a method that could incorporate more context without slowing down. As
computers are only capable of processing numbers within reasonable time
limits, such a method should be based on vectors of numbers rather than
on symbol manipulations. This is exactly where the challenge is: on the
one hand keep up the speed, and on the other hand incorporate more context.
If possible, the data representation of the contextual information must not
be restricted to a single type of media. It should be possible to
incorporate symbolic language as well as sound, pictures and video
concurrently in the retrieval phase, although one does not know exactly
how yet...
Here, the emphasis is more on real-time filtering of large amounts of
dynamic data than on document retrieval from large (static) data bases.
By incorporating more contextual information, it should be possible to
implement a model that can process large amounts of unstructured text
without providing the end-user with an overkill of information.
4.0 The Combination
As this study is a very multi-disciplinary one, the risk exists that it
remains restricted to a surface discussion of many different problems
without analyzing one in depth. To avoid this, some central themes,
applications and tools are chosen. The themes in this work are self-
organization, distributed data representations and context. The
applications are NLP and IR, the tools are (variants of) Kohonen feature
maps, a well known model from neural network research.
Self-organization and context are more related to each other than one may
suspect. First, without the proper natural context, self-organization shall
not be possible. Next, self-organization enables one to discover contextual
relations that were not known before.
Distributed data representation may solve many of the unsolved problems in
NLP and IR by introducing a powerful and efficient knowledge integration
and generalization tool. However, distributed data representation and
self-organization trigger new problems that should be solved in an
elegant manner.
Both NLP and IR work on symbolic language. Both have properties in common
but both focus on different features of language. In NLP hierarchical
structures and semantical features are important. In IR the amount of
data sets the limitations of the methods used. However, as computers grow
more powerful and the data sets get larger and larger, both approaches
get more and more common ground. By using the same models on both
applications, a better understanding of both may be obtained.
Both neural networks and statistics would be able to implement
self-organization, distributed data and context in the same manner.
In this thesis, the emphasis is on Kohonen feature maps rather than on
statistics. However, it may be possible to implement many of the
techniques used with regular sequential mathematical algorithms.
So, the true aim of this work can be formulated as the understanding of
self-organization, distributed data representation, and context in NLP and
IR, by in depth analysis of Kohonen feature maps.
==============================================================================
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End of ALife Digest
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