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Alife Digest Number 115
Alife Digest, Number 115
Sunday, December 5th 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 maintainer: Greg Werner ~
~ Artificial Life Research Group, UCLA ~
~ ~
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
Today's Topics:
Calendar of Alife related events
paper available
paper available
cellular neural networks newsgroup
CFP: IEEE transactions on Systems, Man, and Cybernetics
universal cellular automata simulator available (4 PC)
----------------------------------------------------------------------
**********************************************************************
Subject: Calendar of Alife related events
Neural Information Processing Systems, Denver, CO Nov 29-Dec 2, 1993 v98
A. L.: A Bridge towards a New AI,San Sebastian, Spain Dec 10-11, 1993 v113
Vancouver Cognitive Science Conference, BC, Canada Feb 11-12, 1994 v111
Third Conf on Evolutionary Programming, San Diego, CA Feb 24-25, 1994 v103
AAAI Spring Symposium, Stanford CA Mar 21-23, 199 v110
Cybernetics and Systems Research, Vienna April 5-8, 1994 v101,103
Florida AI Research Symposium, Pensacola Beach, FL May 5-7, 1994 v113
Integrating Knowledge and Neural Heuristics May 9-10, 1994 v111
Intnl Conf Knowledge Rep and Reasoning, Bonn, Germany May 24-27, 1994 v101
IEEE Computational Intelligence, Lake Buena Vista FL Jun 26-Jul 2, 1994 v106
Alife IV, Cambridge MA July 6-8, 1994 v108
Simulation of Adaptive Behavior, Brighton, UK Aug 8-12, 1994 v101
Intnl Congress on Cybernetics, Namur, Belgium August 21-26, 1995 v114
**********************************************************************
From: mm@santafe.edu (Melanie Mitchell)
Subject: paper available
The following paper is available via anonymous ftp:
Evolving Cellular Automata to Perform Computations:
Mechanisms and Impediments
Melanie Mitchell James P. Crutchfield Peter T. Hraber
Santa Fe Institute UC Berkeley Santa Fe Institute
Santa Fe Institute Working Paper 93-11-071
Submitted to Physica D
October 18, 1993
Abstract
We present results from experiments in which a genetic algorithm was
used to evolve cellular automata (CAs) to perform a particular
computational task---one-dimensional density classification. We look
in detail at the evolutionary mechanisms producing the GA's behavior
on this task and the impediments faced by the GA. In particular, we
identify four ``epochs of innovation'' in which new CA strategies for
solving the problem are discovered by the GA, describe how these
strategies are implemented in CA rule tables, and identify the GA
mechanisms underlying their discovery. The epochs are characterized
by a breaking of the task's symmetries on the part of the GA. The
symmetry breaking results in a short-term fitness gain but ultimately
prevents the discovery of the most highly fit strategies. We discuss
the extent to which symmetry breaking and other impediments are
general phenomena in any GA search.
To obtain an electronic copy of this paper:
Note that the paper (44 pages) is broken up into two halves that must be
retrieved separately.
ftp ftp.santafe.edu
login: anonymous
password: <your email address>
cd /pub/Users/mm
binary
get sfi-93-11-071.part1.ps.Z
get sfi-93-11-071.part2.ps.Z
quit
Then at your system:
uncompress sfi-93-11-071.part1.ps.Z
uncompress sfi-93-11-071.part2.ps.Z
lpr -P<printer-name> sfi-93-11-071.part1.ps
lpr -P<printer-name> sfi-93-11-071.part2.ps
If you cannot obtain an electronic copy, send a request for a hard copy to
dlu@santafe.edu.
------------------------------
From: mm@santafe.edu (Melanie Mitchell)
Subject: paper available
The following paper is available via anonymous ftp:
Genetic Algorithms and Artificial Life
Melanie Mitchell Stephanie Forrest
Santa Fe Institute University of New Mexico
Santa Fe Institute Working Paper 93-11-072
To appear in _Artificial Life_
Abstract
Genetic algorithms are computational models of evolution that
play a central role in many artificial-life models. We review
the history and current scope of research on genetic
algorithms in artificial life, using illustrative examples in
which the genetic algorithm is used to study how learning and
evolution interact, and to model ecosystems, immune system,
cognitive systems, and social systems. We also outline a
number of open questions and future directions for genetic
algorithms in artificial-life research.
To obtain an electronic copy of this paper:
ftp ftp.santafe.edu
login: anonymous
password: <your email address>
cd /pub/Users/mm
binary
get sfi-93-11-072.ps.Z
quit
Then at your system:
uncompress sfi-93-11-072.ps.Z
lpr -P<printer-name> sfi-93-11-072.ps
If you cannot obtain an electronic copy, send a request for a hard copy to
dlu@santafe.edu.
------------------------------
From: Marco Balsi <guest3@fred.EECS.Berkeley.EDU>
Subject: cellular neural networks newsgroup
************************************************************
* ANNOUNCING A NEW MAILING LIST ON *
* CELLULAR NEURAL NETWORKS: *
* cells@tce.ing.uniroma1.it *
************************************************************
Cellular Neural Networks (CNN) are continuous-time dynamical systems,
consisting of a grid of processing elements (neurons, or cells) connected
only to neighbors within a given (typically small) distance. It is
therefore a class of recurrent neural networks, whose particular topology
is most suited for integrated circuit realization. In fact, while in
typical realizations of other neural systems most of silicon area is taken
by connections, in this case connection area is neglectible, so that
processor density can be much larger.
Since their first definition by L.O. Chua and L. Yang in 1988, many
applications were proposed, mainly in the field of image processing. In
most cases a space-invariant weight pattern is used (i.e. weights are
defined by a template, which repeats identically for all cells), and
neurons are characterized by simple first order dynamics. However, many
different kinds of dynamics (e.g. oscillatory and chaotic) have also been
used for special purposes.
A recent extension of the model is obtained by integrating the analog
CNN with some simple logic components, leading to the realization of a
universal programmable "analogic" machine.
Essential bibliography:
1) L.O. Chua & L. Yang, "Cellular Neural Networks: Theory", IEEE Trans. on
Circ. and Systems, CAS-35(10), p. 1257, 1988
2) -----, "Cellular Neural Networks: Applications", ibid., p. 1273
3) Proc. of IEEE International Workshop on Cellular Neural Networks and
their Applications (CNNA-90), Budapest, Hungary, Dec. 16-19, 1990
4) Proc. of IEEE Second International Workshop on Cellular Neural Networks
and their Applications (CNNA-92), Munich, Germany, Oct. 14-16, 1992
5) International Journal of Circuit Theory and Applications, vol.20, no. 5
(1992), special issue on Cellular Neural Networks
6) IEEE Transactions on Circuits and Systems, parts I & II, vol.40, no. 3
(1993), special issue on Cellular Neural Networks
7) T. Roska, L.O. Chua, "The CNN Universal Machine: an Analogic Array
Computer", IEEE Trans. on Circ. and Systems, II, 40(3), 1993, p. 163
8) V. Cimagalli, M. Balsi, "Cellular Neural Networks: a Review", Proc. of
Sixth Italian Workshop on Parallel Architectures and Neural Networks,
Vietri sul Mare, Italy, May 12-14, 1993. (E. Caianiello, ed.), World
Scientific, Singapore.
Our research group at "La Sapienza" University of Rome, Italy, has
been involved in CNN research for several years, and will host next IEEE
International Workshop on Cellular Neural Networks and their Applications
(CNNA-94), which will be held in Rome, December 18-21, 1994.
We are now announcing the start of a new mailing list dedicated to
Cellular Neural Networks. It will give the opportunity of discussing
current research, exchanging news, submitting questions. Due to memory
shortage, we are currently not able to offer an archive service, and hope
that some other group will be able to volunteer for the establishment of
this means of fast distribution of recent reports and papers.
The list will not be moderated, at least as long as the necessity
does not arise.
THOSE INTERESTED IN BEING INCLUDED IN THE LIST SHOULD SEND A MESSAGE
to Marco Balsi (who will be supervising the functioning of the list) at
address mb@tce.ing.uniroma1.it (151.100.8.30). This is the address to
which any communication not intended to go to all subscribers of the list
should be sent.
We would also appreciate if you let us know the address of
colleagues who might be interested in the list (rather than just forward
the announcement directly), so that we can send them this announcement and
keep track of those that were contacted, avoiding duplications.
TO SEND MESSAGES TO ALL SUBSCRIBERS PLEASE USE THE FOLLOWING ADDRESS:
cells@tce.ing.uniroma1.it (151.100.8.30)
We hope that this service will encourage communication and foster
collaboration among researchers working on CNNs and related topics.
We are looking forward for your comments, and subscriptions to the
list!
Yours,
Prof. V. Cimagalli
Dipartimento di Ingegneria Elettronica
Universita' "La Sapienza" di Roma
via Eudossiana, 18, 00184 Roma Italy
fax: +39-6-4742647
------------------------------
From: mdorigo@ulb.ac.be (Marco DORIGO)
Subject: CFP: IEEE transactions on Systems, Man, and Cybernetics
Call For Papers:
Special issue of IEEE Transactions on Systems, Man and Cybernetics
(IEEE-SMC) on:
Learning Approaches to Autonomous Robots Control.
Guest Editor: Marco Dorigo
Submission deadline: May 20, 1994.
Recent research on control of autonomous robots (or agents) has
increasingly focused on the development and application of new learning
paradigms. This issue of robot control has been addressed in a number of
research areas including the following:
- Reinforcement learning (Q-learning, Classifier systems, etc.).
- Evolutionary Computation.
- Evolving neural nets.
- Neurocontrol and neurodynamics.
- Adaptive fuzzy systems.
- Artificial life.
The aim of the special issue of IEEE-SMC is to draw together current
research on a variety of these learning techniques (used and developed in
some or all of the above research fields) which have been applied to real
robots' control, as well as to discuss the implications this research has
on the design and development of robots in general. This includes the
following (not exhaustive) sub-topics:
- Learning approaches to stimulus-response robots.
- Learning approaches to robots acting in real environments.
- Supervised (that is, with a trainer) and unsupervised type of behavior
learning.
- Hierarchical architectures for learning robots.
- Trade-offs between learning and design.
- Interplay between reactive and reasoning type of robot activity in a
"learning perspective."
- Robustness of learning techniques to noisy environments and to unreliable
sensors and/or actuators.
- Ethologically inspired learning architectures for autonomous robots.
- Foundational analysis of interdependence among situated activity,
learning algorithms, and degree of environmental complexity.
- Cooperative learning robots.
Papers on research still in its "simulation" phase, that is, yet to be
implemented on real autonomous robots, will also be considered if it has
clear and relevant implications for still to come concrete realization.
To be considered for the special issue, five copies of each paper must be
received by the editor at the address below by May 20, 1994. The first page
should include a descriptive title, the names and addresses of all authors,
a brief abstract, and salient keywords. Submissions will be carefully
refereed for technical contribution, originality of the approach, practical
significance, and clarity of presentation (according to the standard IEEE
Transactions criteria), as well as salience to the topic of the special
issue.
Notifications will be sent by September 15, 1994, and final versions of
accepted papers will be due two months later.
Expected publication is mid-1995.
Marco Dorigo (Editor)
IRIDIA
Universite' Libre de Bruxelles
Avenue Franklin Roosvelt 50
CP 194/6 1050 Bruxelles
Belgium
tel. +32-2-6503167
fax +32-2-6502715
mdorigo@ulb.ac.be.
All prospective contributors should get in touch with the editor as soon as
possible, and in any case well before the submission deadline, in order to
receive more detailed information on the sort of research that the IEEE-SMC
special issue is expected to cover. Such responses will also help us with
the organization of reviews, and with last minute communications (such as
change of Editor's address).
Queries on any aspect of the above should also be directed to the
above address.
Marco Dorigo, Ph.D.
IRIDIA
Universite Libre de Bruxelles
Avenue Franklin Roosvelt 50
CP 194/6
1050 Bruxelles
Belgium
mdorigo@ulb.ac.be
Tel. +32-2-6503167
Fax +32-2-6502715
------------------------------
Date: Fri, 19 Nov 1993 11:25:02 +0100
From: kampis@ludens.elte.hu
Subject: universal cellular automata simulator available (4 PC)
CAM-PC (v1.0) simulation program available
==========================================
A general purpose cellular automata simulation program, called CAM-PC,
based on CAM-6 (Toffoli and Margolus), has been uploaded to the Alife
archives. The simulator extends the possibilities of CAM-6, but (at least
this first version) is not fully downwards compatible with the original.
It is a GNOME.
Authors: Zoltan Belso and Miklos Vargyas
ELTE University, Budapest, Hungary
The program requires an IBM compatible computer, MCGA or VGA display and
about 188 kB-s of free memory to run. It requires 100 kB-s to install.
Instructions for downloading and installation:
>ftp ftp.cognet.ucla.edu (or ftp 128.97.8.19)
ftp> cd pub/alife/public (or maybe later pub/alife/software/ibm ?)
ftp> binary
ftp> get cam.zip (ca. 35 kB-s)
or
ftp> get cam.tar.Z (ca. 50 kB-s)
ftp> quit
(An alternative site is cogsci.elte.hu, in cogsci/alife/CA).
To install cam.zip, use pkunzip (version 2.04g or newer) (available at all
major ftp sites, for instance, at wuarchive.wustl.edu (128.252.135.4) in or
near the directory systems/ibmpc/msdos/archivers.
To install cam.tar.Z use uncompress and tar.
Below you find the README file of the package.
======================================================================
CA General (CAM Simulator), V1.0 R1 Date: 09/27/93
by Zoltan Belso and Miklos Vargyas
GNOME Project
Department of Computer Science
Eotvos University
Hungary
I Introduction
The CAM Simulator program is a general purpose cellular automata simulation
environment based on CAM-6 (see Toffoli, T. and Margolus, N., Cellular
Automata Machines, MIT Press (1987), for details). The simulator extends
the possibilities of CAM-6, but this first version is not fully compatible
with the original CAM definition.
The main difference is that this simulator supports 8 bit planes. In
consequence the size of cell state alphabet is 256. This implies a change
of color map definition (because more colors are accessible). The bit
planes can be accessed with no restriction, and inspite of this, any cell can
be treated as neighbour.
II The program
The simulator is an interactive FORTH interpreter. Available words are
listed in file forth.txt.
The program requires an IBM compatible computer, MCGA or VGA display and
about 188 KB of free memory to run. You need about 100Kb room on HD.
To run the system, say CAM foo.fth at the DOS prompt, where foo is your
own favorite. Alternatively, you can start the system interactively by saying
CAM at the DOS prompt. In the first case CAM loads the file foo.fth, executes
it, then enters interactive mode. In the latter case it gets into interactive
mode directly. Since the program is a GNOME product (see file GNOME.TXT) there
is no prompt and no cursor at all :-). Type always CAPITALS.
Several example .fth programs are provided. Here are a few:
1. Time-tunnel. Say: CAM TIMETUNE.FTH
- the CAM simulator will run the time-tunnel rule (see Toffoli p52)
: TIME-TUNNEL CENTER NORTH SOUTH WEST EAST + + + + { 0 1 1 1 1 0 }
CENTER' XOR >PLN0 ;
: ECHO CENTER >PLN1 ;
(this definition is exactly the same than the original one)
- the size of the cellular space is 50 times 50
50 MAXX ! 50 MAXY !
- the initial configuration is a 5 times 5 squre of not quiescent cells
in the center of the space
: SQUARE 28 23 DO 28 23 DO I J SETXY 1 >PLN0 1 >PLN1 LOOP LOOP ;
- the simulation will take 100 steps
100 STEPS !
- when it is finished the interpreter says Ok. and enters interactive
mode
- since time-tunnel is a reversible rule (see Toffoli for details) you
can run it backward
to do this type:
SWAP-A ( this exchanges bit-plane 0 with bit-plane 1 )
EXCHANGE ( this exchanges current and shadow planes )
( see file CHANGES.TXT for further details )
RUN ( this comments aren't neccassary to type )
it will take 100 steps backward so you get back the initial
configuration
- the simulator remains in graphic mode, to switch to text mode type:
TEXT
- to exit CAM environment type:
BYE
2. Life game. Say: CAM LIFE.FTH
- the cam simulator loads the LIFE rule (see Toffoli p20) but it won't
run
- to run it type
RUN
- it takes 10 step in a 256 times 200 size cellular space starting from
a random initial configuration
- if you want more (say 10000 steps) say:
10000 STEPS !
RUN
3. Running life game from a given initial configuration:
- initial configuration can be loaded from bmp files, too
to do this simply type LOADIM image.bmp
(if you want some changes load the image editor by typing \editor.fth
then say EDITIM (for editing details see file FORTH.TXT))
- type CAM at the DOS prompt
- type
\mlife.fth ( CAM simulator loads the file mlife.fth )
LOADIM mouse.bmp ( CAM loads the file mouse.bmp )
GRAPH ( swicth to graphic mode )
SHOW ( show loaded image )
RUN ( ? )
- the mlife rule is the following:
: MLIFE
CENTERB >PLNBT
CENTER 0= IF
8SUM { 0 0 0 1 0 0 0 0 0 } ELSE
8SUM { 0 0 1 1 0 0 0 0 0 } ENDIF
>PLN0 ;
Notice, that only PLN0 is changed, other bit-planes are copied without
any change. Question: what happens if CENTERB >PLNBT is omitted?
(Help: see CHANGES.TXT)
III Files
This distribution contains the following files:
GNOME.TXT - the GNOME project
README.TXT - this file
FORTH.TXT - list of implemented FORTH words
CHANGES.TXT - differences between CAM-6 and the simulator
CAM.EXE - the simulator program
ASYNC.FTH - asynchronous deterministic computation (p90)
DENDRITE.FTH - dendritic growth (p167)
DEVELOP.FTH - Poisson updating (p86)
EDITOR.FTH - defines simple image editor
FILLS.FTH - squre fills
FRACTAL.FTH - simple fractal (p132)
GAS.FTH - HPP-gas, TM-gas (p123 and p131)
HGLASS.FTH - HGLASS (p28)
LIFE.FTH - game of life (p20)
MLIFE.FTH - game of life with multiple planes
MARGS.FTH - defines Margolus neighbourhood (p119)
PARITY.FTH - parity rule (p31)
RAND.FTH - defines random number generator
RANDWOLK.FTH - 1D random walk (p106)
REL.FTH - defines neighbours
RGB.FTH - defines colors and color maps
SUMS.FTH - defines 8SUM, 8SUM', 9SUM etc.
TIMETUNE.FTH - time tunel (p52)
TUBEWORM.FTH - tube worm (p83)
HODGE.FTH - hodge-podge machine
CASE.FTH - defines CASE statement
(page numbers between parantheses refers to Toffoli's and Margolus' book)
III Further releases
- full CAM-6 compatibility
- improved displaying
- more cells
- Sun version
- and more!
Coming soon!
gubi & mico
------------------------------
End of ALife Digest
*******************
