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From alife@COGNET.UCLA.EDU Tue Jun 8 04:18:45 1993 Return-Path: Received: from Regulus.COGNET.UCLA.EDU by world.std.com (5.65c/Spike-2.0) id AA18316; Tue, 8 Jun 1993 04:18:39 -0400 Received: by regulus.cognet.ucla.edu (Sendmail 5.61c+YP/3.20-COG) id AA25806; Mon, 7 Jun 93 23:42:55 -0700 Date: Mon, 7 Jun 93 23:42:55 -0700 From: alife@COGNET.UCLA.EDU Message-Id: <9306080642.AA25806@regulus.cognet.ucla.edu> To: alife@COGNET.UCLA.EDU Subject: Alife Digest Volume #105 Status: R 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) ********************************************************************** ------------------------------ 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. ============================================================================== ------------------------------ End of ALife Digest *******************

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