From owner-gann-list Wed Jan 10 11:45:26 1996 Received: (from mdomo@localhost) by cs.iastate.edu (8.7.1/8.7.1) id LAA29335 for gann-list-outgoing; Wed, 10 Jan 1996 11:14:35 -0600 (CST) Received: from ren.cs.iastate.edu (ren.cs.iastate.edu [129.186.3.41]) by cs.iastate.edu (8.7.1/8.7.1) with ESMTP id LAA29330 for ; Wed, 10 Jan 1996 11:14:29 -0600 (CST) From: GANN List Admin Received: (from gannadm@localhost) by ren.cs.iastate.edu (8.7.1/8.7.1) id LAA18862 for gann-list@cs.iastate.edu; Wed, 10 Jan 1996 11:14:28 -0600 (CST) Reply-To: mm@santafe.edu (Melanie Mitchell) X-Originator: mm@santafe.edu (Melanie Mitchell) Date: Fri, 29 Dec 95 15:39:17 MST Message-Id: <9512292239.AA05802@sfi.santafe.edu> To: gann@cs.iastate.edu Subject: book announcement Sender: owner-gann-list@cs.iastate.edu Precedence: bulk Announcing a new book: An Introduction to Genetic Algorithms by Melanie Mitchell MIT Press Complex Adaptive Systems series. A Bradford Book Available January 1996 ISBN 0-262-13316-4 232 pp. $30.00 Genetic algorithms have been used in science and engineering as adaptive algorithms for solving practical problems and as computational models of natural evolutionary systems. This brief, accessible introduction describes some of the most interesting research in the field and also enables readers to implement and experiment with genetic algorithms on their own. It focuses in depth on a small set of important and interesting topics --- particularly in machine learning, scientific modeling, and artificial life --- and reviews a broad span of research, including the work of Mitchell and her colleagues. The descriptions of applications and modeling projects stretch beyond the strict boundaries of computer science to include dynamical systems theory, game theory, molecular biology, ecology, evolutionary biology, and population genetics, underscoring the exciting "general purpose" nature of genetic algorithms as search methods that can be employed across disciplines. An Introduction to Genetic Algorithms is accessible to students and researchers in any scientific discipline. It includes many thought and computer exercises that build on and reinforce the reader's understanding of the text. The first chapter introduces genetic algorithms and their terminology and describes two provocative applications in detail. The second and third chapters look at the use of genetic algorithms in machine learning (computer programs, data analysis and prediction, neural networks) and in scientific models (interactions among learning, evolution, and culture; sexual selection; ecosystems; evolutionary activity). Several approaches to the theory of genetic algorithms are discussed in depth in the fourth chapter. The fifth chapter takes up implementation, and the last chapter poses some currently unanswered questions and surveys prospects for the future of evolutionary computation. Melanie Mitchell is Research Professor and Director of the Adaptive Computation Program at the Santa Fe Institute. Complex Adaptive Systems series. A Bradford Book ------------------------------------------------------------- Table of contents: Chapter 1: Genetic Algorithms: An Overview A Brief History of Evolutionary Computation The Appeal of Evolution Biological Terminology Search Spaces and Fitness Landscapes Elements of Genetic Algorithms A Simple Genetic Algorithm Genetic Algorithms and Traditional Search Methods Some Applications of Genetic Algorithms Two Brief Examples How do Genetic Algorithms Work? Thought Exercises Computer Exercises Chapter 2: Genetic Algorithms in Problem-Solving Evolving Computer Programs Data Analysis and Prediction Evolving Neural Networks Thought Exercises Computer Exercises Chapter 3: Genetic Algorithms in Scientific Models Modeling Interactions Between Learning and Evolution Modeling Sexual Selection Modeling Ecosystems Measuring Evolutionary Activity Thought Exercises Computer Exercises Chapter 4: Theoretical Foundations of Genetic Algorithms Schemas and the Two-Armed Bandit Problem Royal Roads Exact Mathematical Models of Simple Genetic Algorithms Statistical Mechanics Approaches Thought Exercises Computer Exercises Chapter 5: Implementing a Genetic Algorithm Introduction When Should a Genetic Algorithm Be Used? Encoding a Problem for a Genetic Algorithm Adapting the Encoding Selection Methods Genetic Operators Parameters for Genetic Algorithms Thought Exercises Computer Exercises Chapter 6: Conclusions and Future Directions Appendices Selected General References Other Resources Bibliography ------------------------------------------------------------- For more information, see http://www-mitpress.mit.edu/mitp/recent-books/cog/mitnh.html Ordering via WWW: http://www-mitpress.mit.edu/ Orders via email: mitpress-orders@mit.edu Toll Free: 1-800-356-0343 Orders and Book Information: (617) 625-8569 Fax: (617) 625-6660 Snail mail: The MIT Press 55 Hayward Street Cambridge, MA 02142-1399 From owner-gann-list Wed Jan 10 11:45:27 1996 Received: (from mdomo@localhost) by cs.iastate.edu (8.7.1/8.7.1) id LAA29291 for gann-list-outgoing; Wed, 10 Jan 1996 11:13:12 -0600 (CST) Received: from ren.cs.iastate.edu (ren.cs.iastate.edu [129.186.3.41]) by cs.iastate.edu (8.7.1/8.7.1) with ESMTP id LAA29286 for ; Wed, 10 Jan 1996 11:13:09 -0600 (CST) From: GANN List Admin Received: (from gannadm@localhost) by ren.cs.iastate.edu (8.7.1/8.7.1) id LAA17939 for gann-list@cs.iastate.edu; Wed, 10 Jan 1996 11:13:07 -0600 (CST) Reply-To: vnissen X-Originator: vnissen Mon, 20 Nov 1995 13:13:20 +0100 Mon, 20 Nov 1995 13:13:15 +0100 Message-Id: <9511201213.AA18163@gwdu03.gwdg.de> Subject: New Book on Evol. Algorithms Available To: gann@cs.iastate.edu Date: Mon, 20 Nov 1995 13:13:15 +0100 (MET) Content-Type: text Sender: owner-gann-list@cs.iastate.edu Precedence: bulk The following book is now available from Springer-Verlag Publishers: Biethahn, J; Nissen, V. (eds.): Evolutionary Algorithms in Management Applications 1995. XVI, 378 pp. 116 fig., 56 tabs., Hardcover, DM 148,- ISBN 3-540-60382-4 Contents ======== Part I - Foundations V. Nissen, J. Biethahn: An Introduction to Evolutionary Algorithms V. Nissen: An Overview of Evolutionary Algorithms in Management Applications Part II - Applications in Industry B. Filipic: A GA Applied to Resource Management in Production Systems I. Rixen; C. Bierwirth, H. Kopfer: A Case Study of Operational Just-in-time Scheduling Using GAs R. Bowden; S. Bullington: An EA for Discovering Manufacturing Control Strategies M. Ettl; M. Schwehm: Determining the Optimal Network Partition and Kanban Allocation in JIT Production Lines M. Krause; V. Nissen: On Using Penalty Functions and Multicriteria Optimisation Techniques in Facility Layout E. Falkenauer:Tapping the Full Power of GA through Suitable Representation and Local Optimization: Application to Bin Packing Two-Dimensional Guillotine Cutting Problem Part III - Applications in Trade R. Broekmeulen: Facility Management of Distribution Centres for Vegetables and Fruits T. Terano; Y. Ishino; K. Yoshinaga: Integrating Machine Learning and Simulated Breeding Techniques to Analyze the Characteristics of Consumer Goods R. Marks; D. Midgley; L. Cooper: Adaptive Behaviour in an Oligopoly V. Nissen; J. Biethahn: Determining a Good Inventory Policy with a GA Part IV - Applications in Financial Services R. Bauer: GAs and the Management of Exchange Rate Risk N. Ireson; T. Fogarty: Evolving Decision Support Models for Credit Control S. Vere: Genetic Classification Trees M. de la Maza; D. Yuret: A Model of Stock Market Participants Part V - Applications in Traffic Management J. McDonnell, D. Fogel; C. Rindt; W. Recker; L. Fogel: Using Evolutionary Programming to Control Metering Rates on Freeway Ramps I. Gerdes: Application of GAs for Solving Problems Related to Free Routing for Aircraft F. Baita; F. Mason; C. Poloni; W. Ukovich: GA with Redundancies for the Vehicle Scheduling Problem Part VI - Planning in Education W. Junginger: Course Scheduling by GAs Appendix Please order through your bookseller or from: Springer-Verlag Postfach 31 13 40 D-10643 Berlin; Germany ---------------------------------------------------------------------------- From owner-gann-list Wed Jan 10 11:45:30 1996 Received: (from mdomo@localhost) by cs.iastate.edu (8.7.1/8.7.1) id LAA29354 for gann-list-outgoing; Wed, 10 Jan 1996 11:15:06 -0600 (CST) Received: from ren.cs.iastate.edu (ren.cs.iastate.edu [129.186.3.41]) by cs.iastate.edu (8.7.1/8.7.1) with ESMTP id LAA29349 for ; Wed, 10 Jan 1996 11:15:00 -0600 (CST) From: GANN List Admin Received: (from gannadm@localhost) by ren.cs.iastate.edu (8.7.1/8.7.1) id LAA19027 for gann-list@cs.iastate.edu; Wed, 10 Jan 1996 11:14:59 -0600 (CST) Reply-To: "John R. Koza" X-Originator: "John R. Koza" Date: Mon, 8 Jan 1996 13:58:27 -0800 (PST) Posted-Date: Mon, 8 Jan 1996 13:58:27 -0800 (PST) Message-Id: <199601082158.NAA16536@Sunburn.Stanford.EDU> To: gann-list@cs.iastate.edu Subject: GP-96 Jan 15 Weather Extension  Sender: owner-gann-list@cs.iastate.edu Precedence: bulk In view of the weather today (and likely continuing weather problems for the next few days on the East Coast of the US), we are extending the deadline for submitting papers to 5 PM Monday January 15, 1996 for ARRIVAL at the AAAI offices in California. Please send your submissions ONLY to the following address: GP-96 Conference c/o American Association for Artificial Intelligence 445 Burgess Drive Menlo Park, CA 94025 USA Please be sure to mark the package "GP-96 Conference." If anyone sent a package to me at Stanford, please notify me separately so I can look for it. The Post Office refuses to deliver mail to the new CSD Building and there are many unopened mail bags at this moment. Best wishes to the snowy East Coast. John Koza From owner-gann-list Wed Jan 10 11:45:31 1996 Received: (from mdomo@localhost) by cs.iastate.edu (8.7.1/8.7.1) id LAA29315 for gann-list-outgoing; Wed, 10 Jan 1996 11:13:35 -0600 (CST) Received: from ren.cs.iastate.edu (ren.cs.iastate.edu [129.186.3.41]) by cs.iastate.edu (8.7.1/8.7.1) with ESMTP id LAA29301 for ; Wed, 10 Jan 1996 11:13:32 -0600 (CST) From: GANN List Admin Received: (from gannadm@localhost) by ren.cs.iastate.edu (8.7.1/8.7.1) id LAA18212 for gann-list@cs.iastate.edu; Wed, 10 Jan 1996 11:13:30 -0600 (CST) Reply-To: stefano@kant.irmkant.rm.cnr.it X-Originator: stefano@kant.irmkant.rm.cnr.it Date: Mon, 20 Nov 1995 18:18:04 GMT Message-Id: <9511201818.AA25293@kant.irmkant.rm.cnr.it> To: gann@cs.iastate.edu Subject: Paper available: Learning to adapt to changing environments Sender: owner-gann-list@cs.iastate.edu Precedence: bulk Papers available via WWW / FTP: Keywords: Learning, Adaptation to changing environments, Evolutionary Robotics Neural Networks, Genetic Algorithms, ------------------------------------------------------------------------------ LEARNING TO ADAPT TO CHANGING ENVIRONMENTS IN EVOLVING NEURAL NETWORKS Stefano Nolfi & Domenico Parisi Institute of Psychology, C.N.R., Rome. In order to study learning as an adaptive process it is necessary to take into consideration the role of evolution which is the primary adaptive process. In addition, learning should be studied in (artificial) organisms that live in an independent physical environment in such a way that the input from the environment can be at least partially controlled by the organisms' behavior. To explore these issues we used a genetic algorithm to simulate the evolution of a population of neural networks each controlling the behavior of a small mobile robot that must explore efficiently an environment surrounded by walls. Since the environment changes from one generation to the next each network must learn during its life to adapt to the particular environment it happens to be born in. We found that evolved networks incorporate a genetically inherited predisposition to learn that can be described as: (a) the presence of initial conditions that tend to canalize learning in the right directions; (b) the tendency to behave in a way that enhances the perceived differences between different environments and determines input stimuli that facilitate the learning of adaptive changes, and (c) the ability to reach desirable stable states. http://kant.irmkant.rm.cnr.it/public.html or ftp-server: kant.irmkant.rm.cnr.it (150.146.7.5) ftp-file : /pub/econets/nolfi.changing.ps.Z for the homepage of our research group with most of our publications available online and pointers to ALIFE resources see: http://kant.irmkant.rm.cnr.it/gral.html ---------------------------------------------------------------------------- Stefano Nolfi Institute of Psychology, C.N.R. Viale Marx, 15 - 00137 - Rome - Italy voice: 0039-6-86090231 fax: 0039-6-824737 e-mail: stefano@kant.irmkant.rm.cnr.it www: http://kant.irmkant.rm.cnr.it/nolfi.html From owner-gann-list Thu Jan 11 15:56:30 1996 Received: (from mdomo@localhost) by cs.iastate.edu (8.7.1/8.7.1) id PAA28111 for gann-list-outgoing; Thu, 11 Jan 1996 15:41:10 -0600 (CST) Received: from ren.cs.iastate.edu (ren.cs.iastate.edu [129.186.3.41]) by cs.iastate.edu (8.7.1/8.7.1) with ESMTP id PAA28106 for ; Thu, 11 Jan 1996 15:41:06 -0600 (CST) From: GANN List Admin Received: (from gannadm@localhost) by ren.cs.iastate.edu (8.7.1/8.7.1) id PAA04475 for gann-list@cs.iastate.edu; Thu, 11 Jan 1996 15:41:04 -0600 (CST) Reply-To: "John R. Koza" X-Originator: "John R. Koza" Date: Wed, 27 Dec 1995 20:11:35 -0800 (PST) Posted-Date: Wed, 27 Dec 1995 20:11:35 -0800 (PST) Message-Id: <199512280411.UAA07581@Sunburn.Stanford.EDU> To: gann-list@cs.iastate.edu Subject: Book of 34 Student Papers Sender: owner-gann-list@cs.iastate.edu Precedence: bulk NOW AVAILABLE!!! A new collection of 34 student papers on GA and GP "Genetic Algorithms and Genetic Programming at Stanford 1995" Compiled by John R. Koza, Computer Science Department, Stanford University This volume (ISBN 0-18-195720-5) contains 34 papers written and submitted by students describing their term projects for the course "Genetic Algorithms and Genetic Programming" (Computer Science 426) at Stanford University offered during the fall quarter 1995 (both on campus and on SITN TV). The appendix to this volume contains material providing basic information about the course, including schedules, reading lists, project instructions, and the take-home final. In the take-home final examination in this course, each student "peer reviews" 4 papers written by other students in the class. Copies of the 1995 volume (ISBN 0-18-195720-5) are available DIRECTLY from Stanford University Bookstore for $14.96 plus $6.00 shipping and handling (in the USA) by calling 415-329-1217 or 800-533-2670 or by writing Stanford Bookstore Stanford University Stanford, California 94305-3079 USA The E-Mail address of the bookstore for e-mail orders is mailorder@bookstore.stanford.edu. Be sure to mention the ISBN number, exact title, refer to "Custom Publishing" and "CSD 000" when ordering to papers, and other materials associated with my courses at Stanford. John R. Koza Consulting Professor Computer Science Department Gates Building Stanford University Stanford, California 94305 USA PHONE: 415-941-0336 E-MAIL: Koza@Cs.Stanford.Edu WWW: http://www-cs-faculty.stanford.edu/~koza/ ------------------------------------------------------------- TABLE OF CONTENTS Evolving Efficient Algorithms by Genetic Programming: A Case Study in Sorting by Eric T. Bauer Using Genetic Algorithms and Convolution to Find Optimal Strategies in Games without Perfect Information by Joey Beheler Genetic Fitting: Evolutionary Search of Optimal Approximations for Discrete Functions by Luca Benini Location Independent Pattern Recognition using Genetic Programming by Markus M. Breunig Programming by King Choi Chan Optimizing Local Area Networks Using Genetic Algorithms by Andy Choi Predator-Prey Interactions in a Simulated World by Adam Clark Evolution of General Algorithmic Solutions for Simple Sliding Tile Puzzles by Thomas Dillon Evolving Effective Solutions in Effective Amounts The Application of Genetic Programming to Cooperative Movement Planning and Execution by John Hart Genetic Programming of Near Minimum Time Spacecraft Attitude Maneuvers by Brian Howley A Genetic Algorithm for a Stochastic Network An Attempt to Evolve Cooperation Among Separately Evolved Structure in Genetic Programming by Bryan H. Johnson Error Driven Parallelization of a Genetic Program by Sesha Kalyur Behavior Learning and Individual Cooperation in Autonomous Agents as a Result of Interaction Dynamics with the Environment by Sejal Kamani The Genetically Determined Dream Team by Mark Kanok A Variable Complexity Genetic Algorithm for Job Allocation by Sanjay Kapoor Using Genetic Algorithm and Decision Trees to by D'ondria L. Kennard Development of Navigational Controllers for Vehicles in Highway Traffic Situations via Genetic Programming by Lisa A. Laane Camera Placement for Optimal Visibility by Vui Chiap Lam The Genetic Algorithm applied to Gate Sizing by Jeremy R. Levitt An Evolutionary Approach to CPU Fault Isolation by Keith Mac Donald Emergent Behavior in Traffic Light Controllers using Genetic Programming by Ari W. Mozes The Hannibal Project by Carl Orthlieb On the Use of Genetic Programming in Elevator Control Design by Dan Pietrasik Evolution of Communication and Division of Labor via Genetic Programming by Hanno Sander Genetic Algorithms Applied to Machine Language by Christian R. Shelton An Empirical Comparison of 3 Population-Based Search Algorithms for the Traveling Salesman Problem by Sanjeev Singh Discovering Patterns in Two-Dimensional Cellular Automata by Caz Taylor Are Your Ready for Some Football? Genetically Produced Ratings for College Football Teams by Howard Thompson Recognition and Reconstruction of Visibility Graphs Using a Genetic Algorithm by Marshall S. Veach Genetic Evolution of Behavior-Oriented Robots by Thomas Willeke Playing Tetris Using Genetic Programming by Michael Yurovitsky Genetic Algorithms in the Solution of Assembly Line Balancing Problems by Greg Zaric Appendix containing materials about the course ------------------------------------------------------------- ALSO AVAILABLE Contact the bookstore directly for current prices on these past items: --- Genetic Algorithms at Stanford 1994 (ISBN 0-18- 187263-3) P 20 papers from the fall quarter 1994. --- Artificial Life at Stanford 1994 (ISBN 0-18-182105-2) P 22 papers from the spring quarter 1994. --- Artificial Life at Stanford 1993 (ISBN 0-18-171957-6) --- Genetic Algorithms at Stanford 1993 (ISBN 0-18- 1738252). --- A course reader entitled Course Reader for Computer Science 426 (Genetic Algorithms) for Fall Quarter 1995 (ISBN 0-18-192183-9) contains 10 selected papers from the current genetic algorithms and genetic programming literature to supplement the two textbooks used in the CS 426 course. From owner-gann-list Thu Jan 11 15:56:32 1996 Received: (from mdomo@localhost) by cs.iastate.edu (8.7.1/8.7.1) id PAA28088 for gann-list-outgoing; Thu, 11 Jan 1996 15:40:05 -0600 (CST) Received: from ren.cs.iastate.edu (ren.cs.iastate.edu [129.186.3.41]) by cs.iastate.edu (8.7.1/8.7.1) with ESMTP id PAA28083 for ; Thu, 11 Jan 1996 15:39:58 -0600 (CST) From: GANN List Admin Received: (from gannadm@localhost) by ren.cs.iastate.edu (8.7.1/8.7.1) id PAA03398 for gann-list@cs.iastate.edu; Thu, 11 Jan 1996 15:39:56 -0600 (CST) Reply-To: Olivier MICHEL X-Originator: Olivier MICHEL Date: Tue, 21 Nov 1995 16:02:52 +0100 Message-Id: <199511211502.QAA16789@alto.unice.fr> To: reinforce@cs.uwa.edu.au, gann-list@cs.iastate.edu, GA-List@aic.nrl.navy.mil, connectionists@cs.cmu.edu, neuron@cattell.psych.upenn.edu Subject: Announcement: Khepera Simulator 1.0 Sender: owner-gann-list@cs.iastate.edu Precedence: bulk * ANNOUNCEMENT OF NEW PUBLIC DOMAIN SOFTWARE PACKAGE * ------------------------------------------------------------- - Khepera Simulator version 1.0 - ------------------------------------------------------------- Khepera Simulator is a public domain software package written by Olivier MICHEL. It allows to write your own controller for the mobile robot Khepera using C or C++ languages, to test them in a simulated environment and features a nice colorful X11 graphical interface. Moreover, if you own a Khepera robot, it can drive the real robot using the same control algorithm. It is mainly destinated to researchers studying autonomous agents. o Requirements: UNIX system, X11 library. o User Manual and examples of controllers included. o This software is free of charge for research and teaching. o Commercial use is forbidden. o Khepera is a mini mobile robot developped at EPFL by Francesco Mondada, Edo Franzi and Andre Guignard (K-Team). o You can download it from the following web site: http://wwwi3s.unice.fr/~om/khep-sim.html Olivier MICHEL om@alto.unice.fr http://wwwi3s.unice.fr/~om/ From owner-gann-list Thu Jan 11 15:56:31 1996 Received: (from mdomo@localhost) by cs.iastate.edu (8.7.1/8.7.1) id PAA28125 for gann-list-outgoing; Thu, 11 Jan 1996 15:41:45 -0600 (CST) Received: from ren.cs.iastate.edu (ren.cs.iastate.edu [129.186.3.41]) by cs.iastate.edu (8.7.1/8.7.1) with ESMTP id PAA28120 for ; Thu, 11 Jan 1996 15:41:40 -0600 (CST) From: GANN List Admin Received: (from gannadm@localhost) by ren.cs.iastate.edu (8.7.1/8.7.1) id PAA04945 for gann-list@cs.iastate.edu; Thu, 11 Jan 1996 15:41:38 -0600 (CST) Reply-To: N.Sharkey@dcs.shef.ac.uk X-Originator: N.Sharkey@dcs.shef.ac.uk Date: Fri, 5 Jan 96 12:14:33 GMT Message-Id: <9601051214.AA28430@entropy.dcs.shef.ac.uk> To: intcon@phoenix.ee.unsw.edu.au, gann-list@cs.iastate.edu Subject: 2nd and FINAL call for papers Sender: owner-gann-list@cs.iastate.edu Precedence: bulk CALL FOR PAPERS ** LEARNING IN ROBOTS AND ANIMALS ** An AISB-96 two-day workshop University of Sussex, Brighton, UK: April, 1st & 2nd, 1996 Co-Sponsored by IEE Professional Group C4 (Artificial Intelligence) WORKSHOP ORGANISERS: Noel Sharkey (chair), University of Sheffield, UK. Gillian Hayes, University of Edinburgh, UK. Jan Heemskerk, University of Sheffield, UK. Tony Prescott, University of Sheffield, UK. PROGRAMME COMMITTEE: Dave Cliff, UK. Marco Dorigo, Italy. Frans Groen, Netherlands. John Hallam, UK. John Mayhew, UK. Martin Nillson, Sweden Claude Touzet, France Barbara Webb, UK. Uwe Zimmer, Germany. Maja Mataric, USA. For Registration Information: alisonw@cogs.susx.ac.uk In the last five years there has been an explosion of research on Neural Networks and Robotics from both a self-learning and an evolutionary perspective. Within this movement there is also a growing interest in natural adaptive systems as a source of ideas for the design of robots, while robots are beginning to be seen as an effective means of evaluating theories of animal learning and behaviour. A fascinating interchange of ideas has begun between a number of hitherto disparate areas of research and a shared science of adaptive autonomous agents is emerging. This two-day workshop proposes to bring together an international group to both present papers of their most recent research, and to discuss the direction of this emerging field. WORKSHOP FORMAT: The workshop will consist of half-hour presentations with at least 15 minutes being allowed for discussion at the end of each presentation. Short videos of mobile robot systems may be included in presentations. Proposals for robot demonstrations are also welcome. Please contact the workshop organisers if you are considering bringing a robot as some local assistance can be arranged. The workshop format may change once the number of accepted papers is known, in particular, there may be some poster presentations. WORKSHOP CONTRIBUTIONS: Contributions are sought from researchers in any field with an interest in the issues outlined above. Areas of particular interest include the following * Reinforcement, supervised, and imitation learning methods for autonomous robots * Evolutionary methods for robotics * The development of modular architectures and reusable representations * Computational models of animal learning with relevance to robots, robot control systems modelled on animal behaviour * Reviews or position papers on learning in autonomous agents Papers will ideally emphasise real world problems, robot implementations, or show clear relevance to the understanding of learning in both natural and artificial systems. Papers should not exceed 5000 words length. Please submit four hard copies to the Workshop Chair (address below) by 30th January, 1996. All papers will be refereed by the Workshop Committee and other specialists. Authors of accepted papers will be notified by 24th February Final versions of accepted papers must be submitted by 10th March, 1996. A collated set of workshop papers will be distributed to workshop attenders. We are currently negotiating to publish the workshop proceedings as a book. SUBMISSIONS TO: Noel Sharkey Department of Computer Science Regent Court University of Sheffield S1 4DP, Sheffield, UK email: n.sharkey@dcs.sheffield.ac.uk For further information about AISB96 ftp ftp.cogs.susx.ac.uk login as Password: cd pub/aisb/aisb96 From owner-gann-list Thu Jan 11 15:56:31 1996 Received: (from mdomo@localhost) by cs.iastate.edu (8.7.1/8.7.1) id PAA28099 for gann-list-outgoing; Thu, 11 Jan 1996 15:40:27 -0600 (CST) Received: from ren.cs.iastate.edu (ren.cs.iastate.edu [129.186.3.41]) by cs.iastate.edu (8.7.1/8.7.1) with ESMTP id PAA28094 for ; Thu, 11 Jan 1996 15:40:22 -0600 (CST) From: GANN List Admin Received: (from gannadm@localhost) by ren.cs.iastate.edu (8.7.1/8.7.1) id PAA03657 for gann-list@cs.iastate.edu; Thu, 11 Jan 1996 15:40:21 -0600 (CST) Reply-To: "John R. Koza" X-Originator: "John R. Koza" Date: Wed, 27 Dec 1995 19:49:51 -0800 (PST) Posted-Date: Wed, 27 Dec 1995 19:49:51 -0800 (PST) Message-Id: <199512280349.TAA06959@Sunburn.Stanford.EDU> To: gann-list@cs.iastate.edu Subject: Univ. GA Course Information Book Sender: owner-gann-list@cs.iastate.edu Precedence: bulk NOW AVAILABLE!!! "THE GA 30" Information for Instructors and Prospective Instructors of University Courses on Genetic Algorithms TITLE: "University Courses on Genetic Algorithms 1995 Edition No. 1 - December, 1995 Compiled by John R. Koza, Computer Science Department, Stanford University This volume contains lightly-edited information about 30 different university courses on genetic algorithms that are offered by universities around the world. The information was contributed by the instructors of the various courses. This information was solicited by posting "requests for information" during 1995 on electronic mailing lists on genetic algorithms, genetic programming, and other topics related to evolutionary computation. It is hoped this collection will be useful to both instructors of existing courses on genetic algorithms and instructors considering starting up their own course on this subject. Copies of this volume (ISBN 0-18-195903P8) are available DIRECTLY from Stanford University Bookstore for $9.30 plus $6.00 shipping and handling (in the USA) by calling 415-329-1217 or 800-533-2670 or by writing Stanford Bookstore Stanford University Stanford, California 94305-3079 USA The E-Mail address of the bookstore for e-mail orders is mailorder@bookstore.stanford.edu. Be sure to mention the ISBN number, exact title, refer to "Custom Publishing" and "CSD 000" when ordering to papers, and other materials associated with my courses at Stanford. John R. Koza Consulting Professor Computer Science Department Gates Building Stanford University Stanford, California 94305 USA PHONE: 415-941-0336 E-MAIL: Koza@Cs.Stanford.Edu WWW: http://www-cs-faculty.stanford.edu/~koza/ From owner-gann-list Thu Jan 11 15:56:34 1996 Received: (from mdomo@localhost) by cs.iastate.edu (8.7.1/8.7.1) id PAA28071 for gann-list-outgoing; Thu, 11 Jan 1996 15:38:55 -0600 (CST) Received: from ren.cs.iastate.edu (ren.cs.iastate.edu [129.186.3.41]) by cs.iastate.edu (8.7.1/8.7.1) with ESMTP id PAA28064 for ; Thu, 11 Jan 1996 15:38:50 -0600 (CST) From: GANN List Admin Received: (from gannadm@localhost) by ren.cs.iastate.edu (8.7.1/8.7.1) id PAA03178 for gann-list@cs.iastate.edu; Thu, 11 Jan 1996 15:38:48 -0600 (CST) Reply-To: robert@fit.qut.edu.au (Robert Andrews) X-Originator: robert@fit.qut.edu.au (Robert Andrews) Date: Mon, 20 Nov 1995 11:55:02 +1000 (EST) Message-Id: <199511200155.LAA02960@ocean.fit.qut.edu.au> Mime-Version: 1.0 Content-Type: text/plain; charset="us-ascii" To: gannadm@cs.iastate.edu X-Mailer: Sender: owner-gann-list@cs.iastate.edu Precedence: bulk ============================================================= FIRST CALL FOR PAPERS AISB-96 WORKSHOP Society for the Study of Artificial Intelligence and Simulation of Behaviour (SSAISB) University of Sussex, Brighton, England April 2, 1996 -------------------------------------------- RULE-EXTRACTION FROM TRAINED NEURAL NETWORKS -------------------------------------------- Robert Andrews Neurocomputing Research Centre Queensland University of Technology Brisbane 4001 Queensland, Australia Phone: +61 7 864-1656 Fax: +61 7 864-1969 E-mail: robert@fit.qut.edu.au Joachim Diederich Neurocomputing Research Centre Queensland University of Technology Brisbane 4001 Queensland, Australia Phone: +61 7 864-2143 Fax: +61 7 864-1801 E-mail: joachim@fit.qut.edu.au Lee Giles NEC Research Institute 4 Independence Way Princeton, NJ 08540 The objective of the workshop is to provide a discussion platform for researchers interested in Artificial Neural Networks (ANNs), Artificial Intelligence (AI) and Cognitive Science. The workshop should be of considerable interest to computer scientists and engineers as well as to cognitive scientists and people interested in ANN applications which require a justification of a classification or inference. INTRODUCTION It is becoming increasingly apparent that without some form of explanation capability, the full potential of trained Artificial Neural Networks may not be realised. The problem is an inherent inability to explain in a comprehensible form, the process by which a given decision or output generated by an ANN has been reached. For Artificial Neural Networks to gain a even wider degree of user acceptance and to enhance their overall utility as learning and generalisation tools, it is highly desirable if not essential that an `explanation' capability becomes an integral part of the functionality of a trained ANN. Such a requirement is mandatory if, for example, the ANN is to be used in what are termed as `safety critical' applications such as airlines and power stations. In these cases it is imperative that a system user be able to validate the output of the Artificial Neural Network under all possible input conditions. Further the system user should be provided with the capability to determine the set of conditions under which an output unit within an ANN is active and when it is not, thereby providing some degree of transparency of the ANN solution. Craven & Shavlik (1994) define the rule-extraction from neural networks task as follows: "Given a trained neural network and the examples used to train it, produce a concise and accurate symbolic description of the network." The following discussion of the importance of rule-extraction algorithms is based on this definition. THE IMPORTANCE OF RULE-EXTRACTION ALGORITHMS Since rule extraction from trained Artificial Neural Networks comes at a cost in terms of resources and additional effort, an early imperative in any discussion is to delineate the reasons why rule extraction is an important, if not mandatory, extension of conventional ANN techniques. The merits of including rule extraction techniques as an adjunct to conventional Artificial Neural Network techniques include: Data exploration and the induction of scientific theories Over time neural networks have proven to be extremely powerful tools for data exploration with the capability to discover previously unknown dependencies and relationships in data sets. As Craven and Shavlik (1994) observe, `a (learning) system may discover salient features in the input data whose importance was not previously recognised.' However, even if a trained Artificial Neural Network has learned interesting and possibly non-linear relationships, these relationships are encoded incomprehensibly as weight vectors within the trained ANN and hence cannot easily serve the generation of scientific theories. Rule-extraction algorithms significantly enhance the capabilities of ANNs to explore data to the benefit of the user. Provision of a `user explanation' capability Experience has shown that an explanation capability is considered to be one of the most important functions provided by symbolic AI systems. In particular, the salutary lesson from the introduction and operation of Knowledge Based systems is that the ability to generate even limited explanations (in terms of being meaningful and coherent) is absolutely crucial for the user-acceptance of such systems. In contrast to symbolic AI systems, Artificial Neural Networks have no explicit declarative knowledge representation. Therefore they have considerable difficulty in generating the required explanation structures. It is becoming increasingly apparent that the absence of an `explanation' capability in ANN systems limits the realisation of the full potential of such systems and it is this precise deficiency that the rule extraction process seeks to redress. Improving the generalisation of ANN solutions Where a limited or unrepresentative data set from the problem domain has been used in the ANN training process, it is difficult to determine when generalisation can fail even with evaluation methods such as cross-validation. By being able to express the knowledge embedded within the trained Artificial Neural Network as a set of symbolic rules, the rule-extraction process may provide an experienced system user with the capability to anticipate or predict a set of circumstances under which generalisation failure can occur. Alternatively the system user may be able to use the extracted rules to identify regions in input space which are not represented sufficiently in the existing ANN training set data and to supplement the data set accordingly. A CLASSIFICATION SCHEME FOR RULE EXTRACTION ALGORITHMS The method of classification proposed here is in terms of: (a) the expressive power of the extracted rules; (b) the `translucency' of the view taken within the rule extraction technique of the underlying Artificial Neural Network units; (c) the extent to which the underlying ANN incorporates specialised training regimes; (d) the `quality' of the extracted rules; and (e) the algorithmic `complexity' of the rule extraction/rule refinement technique. The `translucency' dimension of classification is of particular interest. It is designed to reveal the relationship between the extracted rules and the internal architecture of the trained ANN. It comprises two basic categories of rule extraction techniques viz `decompositional' and `pedagogical' and a third - labelled as `eclectic' - which combines elements of the two basic categories. The distinguishing characteristic of the `decompositional' approach is that the focus is on extracting rules at the level of individual (hidden and output) units within the trained Artificial Neural Network. Hence the `view' of the underlying trained Artificial Neural Network is one of `transparency'. The translucency dimension - `pedagogical' is given to those rule extraction techniques which treat the trained ANN as a `black box' ie the view of the underlying trained Artificial Neural Network is `opaque'. The core idea in the `pedagogical' approach is to `view rule extraction as a learning task where the target concept is the function computed by the network and the input features are simply the network's input features'. Hence the `pedagogical' techniques aim to extract rules that map inputs directly into outputs. Where such techniques are used in conjunction with a symbolic learning algorithm, the basic motif is to use the trained Artificial Neural Network to generate examples for the learning algorithm. As indicated above the proposed third category in this classification scheme are composites which incorporate elements of both the `decompositional' and `pedagogical' (or `black-box') rule extraction techniques. This is the `eclectic' group. Membership in this category is assigned to techniques which utilise knowledge about the internal architecture and/or weight vectors in the trained Artificial Neural Network to complement a symbolic learning algorithm. An ancillary problem to that of rule extraction from trained ANNs is that of using the ANN for the `refinement' of existing rules within symbolic knowledge bases. The goal in rule refinement is to use a combination of ANN learning and rule extraction techniques to produce a `better' (ie a `refined') set of symbolic rules which can then be applied back in the original problem domain. In the rule refinement process, the initial rule base (ie what may be termed `prior knowledge') is inserted into an ANN by programming some of the weights. The rule refinement process then proceeds in the same way as normal rule extraction viz (1) train the network on the available data set(s); and (2) extract (in this case the `refined') rules - with the proviso that the rule refinement process may involve a number of iterations of the training phase rather than a single pass. DISCUSSION POINTS FOR WORKSHOP PARTICIPANTS 1. Decompositional vs. learning approaches to rule- extraction from ANNs - What are the advantages and disadvantages w.r.t. performance, solution time, computational complexity, problem domain etc. Are decompositional approaches always dependent on a certain ANN architecture? 2. Rule-extraction from trained neural networks vs. symbolic induction. What are the relative strength and weaknesses? 3. What are the most important criteria for rule quality? 4. What are the most suitable representation languages for extracted rules? How does the extraction problem vary across different languages? 5. What is the relationship between rule-initialisation (insertion) and rule-extraction? For instance, are these equivalent or complementary processes? How important is rule-refinement by neural networks? 6. Rule-extraction from trained neural networks and computational learning theory. Is generating a minimal rule-set which mimics an ANN a hard problem? 7. Does rule-initialisation result in faster learning and improved generalisation? 8. To what extent are existing extraction algorithms limited in their applicability? How can these limitations be addressed? 9. Are there any interesting rule-extraction success stories? That is, problem domains in which the application of rule-extraction methods has resulted in an interesting or significant advance. ACKNOWLEDGEMENT Many thanks to Mark Craven, and Alan Tickle for comments on earlier versions of this proposal. RELEVANT PUBLICATIONS Andrews, R Diederich, J and Tickle, A.B.: A survey and critique of techniques for extracting rules from trained artificial neural networks. To appear: Knowledge-Based Systems, 1995 (ftp:fit.qut.edu.au//pub/NRC/ps/QUTNRC-95-01- 02.ps.Z) Andrews, R and Geva, S: `Rule extraction from a constrained error back propagation MLP' Proc. 5th Australian Conference on Neural Networks Brisbane Queensland (1994) pp 9-12 Andrews, R and Geva, S `Inserting and extracting knowledge from constrained error back propagation networks' Proc. 6th Australian Conference on Neural Networks Sydney NSW (1995) Craven, M W and Shavlik , J W `Using sampling and queries to extract rules from trained neural networks' Machine Learning: Proceedings of the Eleventh International Conference (San Francisco CA) (1994) (in print) Diederich, J `Explanation and artificial neural networks' International Journal of Man-Machine Studies Vol 37 (1992) pp 335-357 Fu, L M `Neural networks in computer intelligence' McGraw Hill (New York) (1994) Fu, L M `Rule generation from neural networks' IEEE Transactions on Systems, Man, and Cybernetics Vol 28 No 8 (1994) pp 1114-1124 Gallant, S `Connectionist expert systems' Communications of the ACM Vol 31 No 2 (February 1988) pp 152-169 Giles, C L and Omlin C W `Rule refinement with recurrent neural networks' Proc. of the IEEE International Conference on Neural Networks (San Francisco CA) (March 1993) pp 801-806 Giles, C L and Omlin C W `Extraction, insertion, and refinement of symbolic rules in dynamically driven recurrent networks' Connection Science Vol 5 Nos 3 and 4 (1993) pp 307-328 Giles, C L, Miller, C B, Chen, D, Chen, H, Sun, G Z and Lee, Y C `Learning and extracting finite state automata with second-order recurrent neural networks' Neural Computation Vol 4 (1992) pp 393-405 Hayward, R.; Pop, E.; Diederich, J.: Extracting Rules for Grammar Recognition from Cascade-2 Networks. Proceeding, IJCAI-95 Workshop on Machine Learning and Natural Language Processing. McMillan, C, Mozer, M C and Smolensky, P `The connectionist scientist game: rule extraction and refinement in a neural network' Proc. of the Thirteenth Annual Conference of the Cognitive Science Society (Hillsdale NJ) 1991 Omlin, C W, Giles, C L and Miller, C B `Heuristics for the extraction of rules from discrete time recurrent neural networks' Proc. of the International Joint Conference on Neural Networks (IJCNN'92) (Baltimore MD) Vol 1 (1992) pp 33 Pop, E, Hayward, R, and Diederich, J `RULENEG: extracting rules from a trained ANN by stepwise negation' QUT NRC (December 1994) Sestito, S and Dillon, T `Automated knowledge acquisition of rules with continuously valued attributes' Proc. 12th International Conference on Expert Systems and their Applications (AVIGNON'92) (Avignon France) (May 1992) pp 645-656. Sestito, S and Dillon, T `Automated knowledge acquisition' Prentice Hall (Australia) (1994) Artificial Neural Networks' Technical Report IAI-TR-93-5 Institut fur Informatik III Universitat Bonn (1994) Tickle, A B, Orlowski, M, and Diederich, J `DEDEC: decision detection by rule extraction from neural networks' QUT NRC (September 1994) Towell, G and Shavlik, J `The Extraction of Refined Rules Tresp, V, Hollatz, J and Ahmad, S `Network Structuring and Training Using Rule-based Knowledge' Advances In Neural Information Processing Vol 5 (1993) pp871-878 SUBMISSION OF WORKSHOP EXTENDED ABSTRACTS/PAPERS Authors are invited to submit 3 copies of either an extended abstract or full paper relating to one of the topic areas listed above. Papers should be written in English in single column format and should be limited to no more than eight, (8) sides of A4 paper including figures and references. Centered at the top of the first page should be complete title, author name(s), affiliation(s), and mailing and email address(es), followed by blank space, abstract(15-20 lines), and text. Please include the following information in an accompanying cover letter: Full title of paper, presenting author's name, address, and telephone and fax numbers, authors e-mail address. Submission Deadline is January 15th,1996 with notification to authors by 31st January,1996. For further information, inquiries, and paper submissions please contact: Robert Andrews Queensland University of Technology GPO Box 2434 Brisbane Q. 4001. Australia. phone +61 7 864-1656 fax +61 7 864-1969 email robert@fit.qut.edu.au More information about the AISB-96 workshop series is available from: ftp: ftp.cogs.susx.ac.uk pub/aisb/aisb96 WWW: http://www.cogs.susx.ac.uk/aisb/aisb96/CFP/rule_extraction.html WORKSHOP PARTICIPATION CHARGES The workshop fees are listed below. Note that these fees include lunch. Student charges are shown in brackets. AISB NON-ASIB MEMBERS MEMBERS 1 Day Workshop 65 (45) 80 LATE REGISTRATION: 85 (60) 100 PROGRAM COMMITTEE MEMBERS R. Andrews, Queensland University of Technology A. Tickle, Queensland University of Technology S. Sestito, DSTO, Australia J. Shavlik, University of Wisconsin From owner-gann-list Thu Jan 11 15:56:37 1996 Received: (from mdomo@localhost) by cs.iastate.edu (8.7.1/8.7.1) id PAA28136 for gann-list-outgoing; Thu, 11 Jan 1996 15:42:24 -0600 (CST) Received: from ren.cs.iastate.edu (ren.cs.iastate.edu [129.186.3.41]) by cs.iastate.edu (8.7.1/8.7.1) with ESMTP id PAA28128 for ; Thu, 11 Jan 1996 15:42:18 -0600 (CST) From: GANN List Admin Received: (from gannadm@localhost) by ren.cs.iastate.edu (8.7.1/8.7.1) id PAA05419 for gann-list@cs.iastate.edu; Thu, 11 Jan 1996 15:42:16 -0600 (CST) Reply-To: "Phil Husbands" X-Originator: "Phil Husbands" Message-Id: Subject: Tutorial on ALife and Adaptive Behaviour To: ca@Think.COM, GA-List@aic.nrl.navy.mil, gann-list@cs.iastate.edu Date: Mon, 8 Jan 1996 17:51:39 +0000 (GMT) Cc: philh@cogs.susx.ac.uk (Phil Husbands), davec@cogs.susx.ac.uk (Dave Cliff), alisonw@cogs.susx.ac.uk (Alison White) MIME-Version: 1.0 Content-Type: text/plain; charset=US-ASCII Content-Transfer-Encoding: 7bit Sender: owner-gann-list@cs.iastate.edu Precedence: bulk AISB96 Workshop and Tutoria1l Series 31 March-2 April 1996 University of Sussex Falmer, Brighton, UK One day Tutorial: Artificial Life and Adaptive Behaviour --------------------------------------- Date of Tutorial: 31st March 1996 Presenter(s) - Dave Cliff and Phil Husbands School of Cognitive and Computing Sciences University of Sussex Falmer, Brighton BN1 9QH Email: davec or philh @cogs.susx.ac.uk -------------------------------------------------------------------------- Description ----------- This tutorial will provide an introduction to the burgeoning fields of Artificial Life and Adaptive Behaviour. Artificial Life is concerned with the use of computational methods to both model and synthesize phenomena normally associated with living systems. The related, but more focused, discipline of Adaptive Behaviour brings together ideas from a range of disciplines, such as ethology, cognitive science and robotics, to further our understanding of the behaviours and underlying mechanisms that allow animals, and, potentially, robots to survive in uncertain environments. Topics to be covered include: the historical roots of Artificial Life and Adaptive Behaviour; Strong Alife and Weak Alife; principles of behaviour-based robotics; artificial evolution and its application to autonomous robotics; modelling and synthesizing neural and other learning mechanisms for autonomous agents; collective behaviour; artificial worlds; software agents; understanding the origins of life; applications; the philosophical implications of these approaches. The material will be presented in lecture format with liberal use of video, computer and robot demonstrations. Although only key work will be discussed, extensive bibliographies and suggestions for further reading will be provided along with lecture notes and other supporting literature. -------------------------------------------------------------------------- Prerequisites: None -------------------------------------------------------------------------- Tutorial Numbers: Maximum of 50 (constrained by room size) -------------------------------------------------------------------------- Audience: Anyone who thinks the tutorial description sounds interesting and is willing to part with the cash. They won't be sorry. -------------------------------------------------------------------------- Tutorial Fees: Tutorial Fees include course materials, refreshments and lunch. All prices are in pounds Sterling. (AISB Student fees are in parentheses) Early Registration Deadline: 1 March 1996 AISB NON-AISB MEMBERS MEMBERS 1 Day Tutorial 80.00 (55.00) 100.00 LATE REGISTRATION 100.00 (75.00) 120.00 For Full Details of Registration please contact: AISB96 Local Organisation COGS University of Sussex Falmer, Brighton, BN1 9QH Tel: +44 1273 678448 Fax: +44 1273 671320 Email: aisb@cogs.susx.ac.uk ========================================================================== From owner-gann-list Fri Jan 12 11:40:36 1996 Received: (from mdomo@localhost) by cs.iastate.edu (8.7.1/8.7.1) id LAA13765 for gann-list-outgoing; Fri, 12 Jan 1996 11:34:41 -0600 (CST) Received: from ren.cs.iastate.edu (ren.cs.iastate.edu [129.186.3.41]) by cs.iastate.edu (8.7.1/8.7.1) with ESMTP id LAA13758 for ; Fri, 12 Jan 1996 11:34:34 -0600 (CST) From: GANN List Admin Received: (from gannadm@localhost) by ren.cs.iastate.edu (8.7.1/8.7.1) id LAA03422 for gann-list@cs.iastate.edu; Fri, 12 Jan 1996 11:34:31 -0600 (CST) Reply-To: raffaele@caio.irmkant.rm.cnr.it X-Originator: raffaele@caio.irmkant.rm.cnr.it Date: Thu, 11 Jan 1996 16:44:26 -0600 Message-Id: <9601112244.AA22020@caio.irmkant.rm.cnr.it> To: gann-list@cs.iastate.edu Sender: owner-gann-list@cs.iastate.edu Precedence: bulk 'Diploidy and dominance in artificial genetic search' (Smith and Goldberg. Complex Systems, 1992)? I am particularly interested in applications to neural nets. Any pointers and/or references would be greatly appreciated. Please reply to me by email: raffaele@caio.irmkant.rm.cnr.it Thanks in advance Raffaele Calabretta ************************************************ Department of Neural Systems and Artificial Life Institute of Psychology National Research Council V.le Marx, 15 00137 ROME - ITALY Phone number +39-6-86.09.02.33 Fax number +39-6-82.47.37 E-mail: raffaele@caio.irmkant.rm.cnr.it WWW: http://kant.irmkant.rm.cnr.it/gral.html ************************************************ From owner-gann-list Fri Jan 12 11:41:51 1996 Received: (from mdomo@localhost) by cs.iastate.edu (8.7.1/8.7.1) id LAA13822 for gann-list-outgoing; Fri, 12 Jan 1996 11:36:16 -0600 (CST) Received: from ren.cs.iastate.edu (ren.cs.iastate.edu [129.186.3.41]) by cs.iastate.edu (8.7.1/8.7.1) with ESMTP id LAA13817 for ; Fri, 12 Jan 1996 11:36:04 -0600 (CST) From: GANN List Admin Received: (from gannadm@localhost) by ren.cs.iastate.edu (8.7.1/8.7.1) id LAA05419 for gann-list@cs.iastate.edu; Fri, 12 Jan 1996 11:36:01 -0600 (CST) Reply-To: "Peter J. Angeline" X-Originator: "Peter J. Angeline" Message-Id: <9601101643.ZM12751@barbarian.endicott.ibm.com> Date: Wed, 10 Jan 1996 16:43:38 -0500 X-Face: +BWP-8[^J|_CYV*m/-^6oCy6EL@?[z--(wd6,ItJoRdBH`DO7n0[UwL;DT(wli=*Wt%I"[HUb%.#(u~srB.(Ub)LVUJ?V-PJt?fwkG)g,U{[w%o%"{al;|OM)q(1^"L2cY,i$Rt| X-Mailer: Z-Mail (3.2.1 15feb95) To: EP-List@magenta.me.fau.edu, Genetic-Programming@CS.Stanford.EDU, TIERRA@life.slhs.udel.edu, alife@cognet.ucla.edu, cells@tce.ing.uniroma1.it, cellular-automata@think.com, colt@cs.uiuc.edu, connectionists@MAILBOX.SRV.CS.CMU.EDU, evolutionary-computing@mailbase.ac.uk, ga-molecule-approval@interval.com, gann-list@cs.iastate.edu, ml@ics.uci.edu, neuron-request@CATTELL.psych.upenn.edu Subject: EP96 Conference Announcement Mime-Version: 1.0 Content-Type: text/plain; charset=us-ascii Sender: owner-gann-list@cs.iastate.edu Precedence: bulk The Fifth Annual Conference on Evolutionary Programming (EP96) February 29 to March 2, 1996 The Sheraton Harbor Island Hotel San Diego, CA, USA The EP conference has earned a reputation for being a broad, single session conference covering all Evolutionary Computations and their applications with a special emphasis on Evolutionary Programming. This year's conference has a very strong collection of invited and submitted papers covering topics on the theory, practice and analysis of all forms of evolutionary computations in addition to the application of evolutionary computations to artificial life, economics, biology and biochemistry. Conference Program specifics include: Keynote Speaker: Bernardo Huberman - Xerox PARC "The Dynamics of Multiagent Systems" Banquet Speaker: Bill Schopf- UCLA (Discoverer of the oldest fossil on record!) "Modeling Evolutinary Tempo and Mode: Are the Right Questions Being Asked?" Special Sessions: Evolution and Economic Modeling The High Level Control of Evolutionary Learning Evolutionary Computation in Biology and Biochemistry Submitted Paper Sessions: Theory and Analysis of Evolutionary Computations Self-Adaptive Evolutionary Computations Issues in Evolutionary Optimization Genetic Programming Evolution and Computational Intelligence Learning and Control Applications and Implementation Issues For specific details regarding the content of the various sessions or other conference information, see the Conference WWW page at http://www.owego.com/~pja/ep96.html or contact Peter Angeline at pja@lfs.loral.com. A registration form is included below for your convenience. We encourage everyone to attend what looks to be an exciting EP conference! Pete Angeline Thomas Baeck EP96 Technical Co-Chairs ---------------------------------------------------------------------- EP96 Fifth Annual Conference on Evolutionary Programming Registration Form Prof / Dr / Mr / Ms / Mrs (circle one) Name ________________________________________________________________ Last First MI I would like my name tag to read _____________________________________________ Affiliation/Business ______________________________________________________ Address ______________________________________________________ City ______________________________________________________ State ___________________ Zip ________________________ Country_____________________________________________ Telephone (include area code) Business _______________________________ Home______________________________ Email ____________________________ Fax (include area code) _______________________ FEES (all figures in US dollars) CONFERENCE REGISTRATION FEE On or before January 31, 1996 ___ Regular, $225 ___ Student, $50 =$_________ On or after February 1, 1996 ___ Regular, $275 ___ Student, $90 =$_________ SINGLE DAY REGISTRATION FEES (Does not include proceedings or banquet ticket) Thursday, February 19 ___ Regular, $100 ___ Student, $40 Friday, March 1 ___ Regular, $100 ___ Student, $40 Saturday, March 2 ___ Regular, $100 ___ Student, $40 =$_________ Extra Banquet Tickets (cost of one banquet ticket is included with only CONFERENCE REGULAR registration fee; extra tickets may not be available at the Registration Desk) Adult #______ ticket(s) @ $40 =$_________ child #______ ticket(s) @ $10 =$_________ EP Society Membership (qualifies registrant for reduced rates for this conference) ___ Regular, $40 ___ Student, $10 =$_________ FOR EP SOCIETY MEMBERS ONLY One year subscription to journal BioSystems (usually $575) ___ $75 =$_________ Send journal subscription to the following address: ___________________________________________________ ___________________________________________________ ___________________________________________________ ___________________________________________________ ___________________________________________________ ___________________________________________________ TOTAL (US dollars) $____________ METHOD OF PAYMENT ___ Check (payable to the Evolutionary Programming Society in US Funds only) ___ MasterCard ___ VISA #__________________________________________ Expiration Date ____________________ Signature of card holder ______________________________________________ Note: Students must submit with their registration a photocopy of their Mail EP96 Registration Natural Selection Inc. Attn: Bill Porto 3333 N. Torrey Pines Ct. Ste. 200 La Jolla CA 92037 Fax 619-455-1560 World Wide Web (WWW) For up-to-date conference information: http://www.owego.com/~pja/ep96.html -- +----------------------------------------------------------------------------+ | Peter J. Angeline, PhD | | | Advanced Technologies Dept. | | | Loral Federal Systems | | | State Route 17C | Why should I be limited to just | | Mail Drop 0210 | a CAUSAL chain of events? | | Owego, NY 13827-3994 | | | Voice: (607)751-4109 | - Anonymous | | Fax: (607)751-6025 | | | Email: pja@lfs.loral.com | | +----------------------------------------------------------------------------+ From owner-gann-list Fri Jan 12 11:43:24 1996 Received: (from mdomo@localhost) by cs.iastate.edu (8.7.1/8.7.1) id LAA13806 for gann-list-outgoing; Fri, 12 Jan 1996 11:35:36 -0600 (CST) Received: from ren.cs.iastate.edu (ren.cs.iastate.edu [129.186.3.41]) by cs.iastate.edu (8.7.1/8.7.1) with ESMTP id LAA13800 for ; Fri, 12 Jan 1996 11:35:32 -0600 (CST) From: GANN List Admin Received: (from gannadm@localhost) by ren.cs.iastate.edu (8.7.1/8.7.1) id LAA04516 for gann-list@cs.iastate.edu; Fri, 12 Jan 1996 11:35:30 -0600 (CST) Reply-To: Mr Kai Chen X-Originator: Mr Kai Chen Thu, 11 Jan 1996 09:44:40 +0000 11 Jan 96 09:50:49 GMT Organization: University of Plymouth To: evolutionary-computing@mailbase.ac.uk, ep-list@magenta.me.fau.edu, genetic-programming@CS.Stanford.EDU, gann@cs.iastate.edu, ga-list@aic.nrl.navy.mil, engineering-design@mailbase.ac.uk Date: Thu, 11 Jan 1996 09:50:26 GMT X-file: ACEDC1.TXT X-finfo: DOS,"ACEDC1.TXT",,,,Text Subject: Priority: normal X-mailer: Pegasus Mail v3.1 (R1) Message-ID: <1601B650F66@cs_fs15.csd.plym.ac.uk> Sender: owner-gann-list@cs.iastate.edu Precedence: bulk CONFERENCE ANNOUNCEMENT: ======================= ADAPTIVE COMPUTING IN ENGINEERING DESIGN AND CONTROL'96 (ACEDC'96) 26-28th March 1996 Plymouth Engineering Design Centre, University of Plymouth, UK 2ND INTERNATIONAL CONFERENCE 'The Integration of Genetic Algorithms, Neural Computing and Related Adaptive Techniques with Current Engineering Practice' LOCATION: Plymouth Engineering Design Centre University of Plymouth Plymouth, Devon, UK CONFERENCE CHAIRS: Dr. I C Parmee, University of Plymouth Prof. M J Denham, University of Plymouth KEYNOTE SPEAKERS: Prof. E Goodman, Michigan State University Prof. G Thierauf, Universitat GH Essen Prof. J Taylor, Kings College, London Prof. J Morris, University of Newcastle-upon-Tyne Dr. P Husbands, University of Sussex ASSOCIATED SOCIETIES: Institution of Engineering Designers Institution of Mechanical Engineers Institution of Civil Engineers British Computer Society AISB INVITED PAPERS Design of Special Purpose Composite Material Plates via Genetic Algorithms - Professor E Goodman, Michigan State University, USA Multi-Variant Methods in Process Performance Monitoring and Control - Professor J Morris, University of Newcastle-Upon-Tyne, UK Structural Optimization of a Transmission Tower by Using Parallel Evolution Strategy - Professor G Thierauf, Universitat GH Essen, Germany New Models of Control from Biology - Professor J Taylor, Kings College, Cambridge, UK The Artificial Evolution of Robot Control Systems Doctor P Husbands - University of Sussex, UK SELECTION OF ACCEPTED PAPERS Parallelisation of Genetic Algorithm for Aerodynamic Design Optimisation C Poloni - University of Trieste, Italy An Experiment in Knowledge Abstraction from Cognitive Maps to Behaviours A G Pipe, T C Fogarty, A Winfield - University of the West of England, UK Case Retrieval using Associative Networks C T Charlton, N R Ball - University of Cambridge, UK Usage of Back Propagation Networks in a CAD/CAM System A Scherer, G Schlageter - University of Hagen, Germany Intelligent Control and Adaptive Neural Networks Computing A V Timofeyev - Russian Academy of Science Constrained and Multi-Modal Optimization with an Ant Colony Search Model G Bilchev, I C Parmee - University of Plymouth, UK A Genetic Algorithm for VLSI Physical Design Automation V Schnecke, O Vornberger - University of Osnabruck Novel Computing Technologies Applied to the Optimization of Suction Distribution in Multi-Channel Drag Reduction Systems W Allan, P A Nelson, E Rogers, O R Tutty, M C M Wright - University of Southampton, UK E Semenkin, O Semenkina - Siberian Aerospace Academy, Russia Genetic Searching using On-line Structural Vibration Measurements J M Hale, N S Brown - University of Newcastle-Upon-Tyne, UK Optimal Sensor Placement for Neural Network Fault Diagnosis W J Staszewski, K Worden, G R Tomlinson - Manchester University, UK A Classifier System Approach to Control in Changing Environments Gilles Venturini - Universite de Tours, France Adaptive Search Strategies to Maintain Diverse Global Search for Preliminary and Whole System Design I C Parmee - University of Plymouth, UK Stochastic Optimisation of Mathematical Models for Electric and Magnetic Fields K Hameyer, R Belmans - Katholieke University, Belgium Ship Subdivision using Genetic Algorithms P Sen, R Subramani - University of Newcastle-upon-Tyne, UK Use of Genetic Algorithms to Solve Optimal Regional Water Quality Management Problems C A Coello Coello, J A F Gallegos - Tulane University, USA Notes on the Evolution of Adaptive Hardware T Hirst - HCRL, Open University, UK Understanding Chaotic Dissipative Dynamics in the State Space with Fuzzy Logic T Schilhabel, M Brown & C Harris - University of Southampton, UK and a Neural Network H Rowlands - University of Wales, UK Effective Optimisation of Complex Systems using "Neural Know-how Recycling" from Simulation and Test, by Way of Example Applied to Petrol Engine Management Calibration R Stricker, T Fleischhauer - Germany Self-tuning Robust Multi-loop AVRs for Synchronous Generators in Primary Regulation for Power Systems I Eker - University of Gaziantep, Turkey Evolutionary Learning of Controllers using Temporal Fuzzy Classifier Systems B Carse, T Fogarty - University of the West of England On a Genetic Algorithm for the Selection of Optimally Generalizing Neural Network Topologies S Rudolph - University of Stuttgart, Germany Integrating the GA with the Preliminary Design of Gas Turbine Blade Cooling Systems R Roy, I C Parmee, G Purchase - University of Plymouth, UK Form/Function/Cost Tradeoffs through Adaptive Search T Hill - University of the West of England The Design of a Satellite Boom with Enhanced Vibration Performance using Genetic Algorithm and Other Optimization Techniques A J Keane - University of Oxford, UK The Optimal Control of Vehicle Suspension Systems M Ali, C Storey - University of Loughborough, UK K Popplewell, J A Harding - University of Loughborough, UK Myoelectric Signal Recognition using Genetic Programming J Fernandex, J Cheatham - Rice University, USA Systems Identification using Genetic Programming A Watson, I C Parmee - University of Plymouth, UK Multi-rate Multi-loop Explicit Adaptive Cascade Control for Process Control Industries I Eker, University of Gaziantep, Turkey The Provision of Concurrent Engineering Design Support through the Use of Captured, Simulated Experiences R MacDonald, C Irgens, B Lees - University of Paisley, UK Partial Automated Design Optimization based on Adaptive Search Techniques W Suss, M Gorges-Schleuter, W Jakob, S Meinzer, A Quinte, H Eggert - Institut fur Angewandte Informatik, Germany SELECTION OF POSTER PRESENTATIONS An Immune Network Model for Constraint Satisfaction in Engineering Design G Bilchev, I C Parmee - University of Plymouth, UK Combined GA-NNW Methodology for Modelling Non-linear Dynamic Systems of Unknown Structure N E Gough , A H Abu-Alola, P B Musgrove, Q Mehdi - University of Wolverhampton, UK Automated Design Knowledge Y James-Gordon - Bournemouth University, UK A Framework for Conceptual Design of Buildings using the Genetic Algorithm J Mathews, Y Rafiq, G Bullock - University of Plymouth, UK Edge Eaters + JPEG: An Artificial Life Approach to Image Compression F Cecconi, S Mancuso, D Parisi - Institute of Psychology, Italy Development of Artificial Neural Networks for Conceptual Design of Power Transmission Systems D Su - The Nottingham Trent University, UK Practical Implementation and Use of Group Method of Data Handling (GMDH): Prospects and Problems S A Dolenko, Y V Orlov, I G Persiantsev - Moscow State University, Russia A Genetic Algorithm for VQ Codebook Generation J S Pan, F R McInnes, M A Jack - University of Edinburgh, UK, Kaohsiung Institute of Technology, Taiwan Some New Features in Genetic Solution of the Travelling Salesman Problem V Kureichick, A N Melikhov, V V Miagkikh, O V Savelev, A P Topchy - Taganrog State University, Russia A Multi-Operator Self-Tuning Genetic Algorithm for Fuzzy Control Rule Optimization C C Hsu, S Yamada, H Fujikawa, K Shida - Musashi Institute of Technology, Japan Interactive Evolution in Engineering Design J Graf - University of Dortmund, Germany Component Shape Encodings for Genetic Algorithms F Mill, S Warrington, R Smith - University of Edinburgh, UK Automatic Test Generation from Mathematical Software Specifications using Genetic Algorithms X Yang, B F Jones, D E Eyres - University of Glamorgan, UK FEES AND CHARGES: Registration Fee (Before 15th Feb'96) Delegate 195.00 (Pounds Sterling) Student 100.00 (Pounds Sterling) Registration Fee (After 15th Feb '96) Delegate 235.00 (Pounds Sterling) Student 140.00 (Pounds Sterling) Payment for registration is required in advance of the conference. For further information and Registration Form contact: Ms J Levers Plymouth Engineering Design Centre University of Plymouth, Drakes Circus, Plymouth, Devon, UK PL4 8AA Tele: +44(0)1752-233508 Fax: +44(0)1752-233505 Email: iparmee@plymouth.ac.uk From owner-gann-list Sat Jan 20 23:11:20 1996 Received: (from mdomo@localhost) by cs.iastate.edu (8.7.1/8.7.1) id WAA09206 for gann-list-outgoing; Sat, 20 Jan 1996 22:47:27 -0600 (CST) Received: from ren.cs.iastate.edu (ren.cs.iastate.edu [129.186.3.41]) by cs.iastate.edu (8.7.1/8.7.1) with ESMTP id WAA09201 for ; Sat, 20 Jan 1996 22:47:20 -0600 (CST) From: GANN List Admin Received: (from gannadm@localhost) by ren.cs.iastate.edu (8.7.1/8.7.1) id WAA18812 for gann-list@cs.iastate.edu; Sat, 20 Jan 1996 22:47:18 -0600 (CST) Reply-To: h94d607d@eds.ecip.nagoya-u.ac.jp (Riyuu Chiyanu ) X-Originator: h94d607d@eds.ecip.nagoya-u.ac.jp (Riyuu Chiyanu ) Date: Thu, 18 Jan 96 08:34:48 JST Message-Id: <9601172334.AA02914@sunshine.eds.ecip.nagoya-u.ac.jp> To: gann-list@cs.iastate.edu Subject: For Help Sender: owner-gann-list@cs.iastate.edu Precedence: bulk Dear All List Member, allocation. Thank you very much in advance. Chunlu LIU ******************************************************* * * * Doctoral Student, Nagoya University * * Department of Civil Engineering * * Chikusa-ku, Nagoya 464-01, Japan * * Phone: 52-7893733 (lab.) 52-7642165 (home) * * E-Mail: h94d607d@eds.ecip.nagoya-u.ac.jp * * fttp://133.6.102.236/HTML/structure/liu/liu.html * * * ******************************************************* From owner-gann-list Sat Jan 20 23:11:23 1996 Received: (from mdomo@localhost) by cs.iastate.edu (8.7.1/8.7.1) id WAA09225 for gann-list-outgoing; Sat, 20 Jan 1996 22:47:57 -0600 (CST) Received: from ren.cs.iastate.edu (ren.cs.iastate.edu [129.186.3.41]) by cs.iastate.edu (8.7.1/8.7.1) with ESMTP id WAA09218 for ; Sat, 20 Jan 1996 22:47:52 -0600 (CST) From: GANN List Admin Received: (from gannadm@localhost) by ren.cs.iastate.edu (8.7.1/8.7.1) id WAA19403 for gann-list@cs.iastate.edu; Sat, 20 Jan 1996 22:47:50 -0600 (CST) Reply-To: "Dave Cliff" X-Originator: "Dave Cliff" Message-Id: Subject: MSc in Evolutionary and Adaptive Systems To: ca@Think.COM, GA-List@aic.nrl.navy.mil, gann-list@cs.iastate.edu Date: Fri, 19 Jan 1996 11:05:19 +0000 (GMT) Cc: davec@cogs.susx.ac.uk (Dave Cliff) MIME-Version: 1.0 Content-Type: text/plain; charset=US-ASCII Content-Transfer-Encoding: 7bit Sender: owner-gann-list@cs.iastate.edu Precedence: bulk Please distribute: The University of Sussex School of Cognitive and Computing Sciences Graduate Research Centre (COGS GRC) Master of Science (MSc) Degree in EVOLUTIONARY AND ADAPTIVE SYSTEMS Applications are invited for entry in October 1996 to the Master of Science (MSc) degree in Evolutionary and Adaptive Systems. The degree can be taken in one year full-time, or part-time over two years. Students initially follow taught courses, as preparation for an individual research project leading to a Masters Thesis. This email gives a brief summary of the degree. For further details, see: World-wide web: http://www.cogs.susx.ac.uk/lab/adapt/easy_msc.html Anonymous ftp: ftp to cogs.susx.ac.uk cd to pub/users/davec get (in binary mode) easy_msc.ps.Z (69K) Or contact the address at the end of this email to request hard-copy. The MSc is sponsored in part by: BNR Europe Ltd, Hewlett-Packard, Millennium Interactive. BACKGROUND The past decade has seen the formation of new research fields, crossing traditional boundaries between biology, computer science, and cognitive science. Known variously as Artificial Life, Simulation of Adaptive Behavior, and Evolutionary Computation, the common theme is a focus on adaptation in natural and artificial systems. This research has the potential both to further our understanding of living and adaptive mechanisms in nature, and to construct artificial systems which show the same flexibility, robustness, and capacity for adaptation as is seen in animals. The international research community is sufficiently large to support five series of biennial conferences on various aspects of the field (ICGA, ALife, ECAL, SAB, PPSN), and there are currently three international journals (all produced by MIT Press) for archival publication of significant research findings. The Evolutionary and Adaptive Systems (EASy) Research Group at the University of Sussex School of Cognitive and Computing Sciences (COGS) is now widely recognised as one of the world's foremost groups of researchers in this area, with approximately 35 people actively engaged in research. Students on the EASy MSc will be involved in this lively interdisciplinary environment. At the end of the course, students will have been trained to a standard where they are capable of pursuing doctoral research in any area of Evolutionary and Adaptive Systems; and of applying those techniques in industry. INTERNATIONAL STEERING GROUP M. A. Arbib (Uni. of Southern California, USA); M. Bedau (Reed College, USA); R. D. Beer (Case Western Reserve Uni, USA); R. A. Brooks (MIT, USA); H. Cruse (Universitat Bielefeld, Germany); K. De Jong (George Mason Uni., USA); D. Dennett (Tufts, USA); D. Floreano (LCT, Italy); J. Hallam (Uni. of Edinburgh, UK); I. Horswill (North Western Uni., USA); L. P. Kaelbling (Brown Uni., USA); C. G. Langton (Santa Fe Inst., USA); M. J. Mataric (Brandeis Uni., USA); J.-A. Meyer (Ecole Normale Superieure, France); G. F. Miller (MPIPF, Germany); R. Pfeiffer (Uni. of Zurich, Switz.); T. S. Ray (ATR, Japan); C. Reynolds (Silicon Graphics Inc, USA); H. L. Roitblat (Uni. of Hawaii, USA); T. Smithers (Euskal Herriko Unibertsitatae, Spain); L. Steels (VUB, Belgium); P. Todd (MPIPF, Germany); B. H. Webb (Uni. of Nottingham, UK); S. W. Wilson (Rowland Inst., USA). FULL-TIME SYLLABUS Autumn Term (Oct--Dec) ---------------------- Four compulsory courses: Artificial Life Introduction to Computer Science Formal Computational Skills Adaptive Behavior in Animals and Robots Spring Term (Jan-Mar) --------------------- Two compulsory courses: Adaptive Systems Neural Networks Two options chosen from the following list (further options may become available; some options may not be available in some years): Simulation of Adaptive Behavior History and Philosophy of Adaptive Systems Development in Human and Artificial Life Computer Vision Philosophy of Cognitive Science Computational Neuroscience Summer (Apr-Aug) ---------------- Research project, which should include a substantial practical (programming) element, leading to submission of a 12000-word masters thesis. It is intended that there will be industrial involvement in some projects. SUSSEX FACULTY INVOLVED IN THE MSc Prof. H. G. Barrow; Prof. M. A. Boden; Dr. H. Buxton; R. Chrisley; Prof. A. J. Clark; Dr. D. Cliff; Dr. T. S. Collett; Dr. P. Husbands; Dr. D. Osorio; Dr. J. C. Rutkowska; Dr. D. S. Young. APPLICATION PROCEDURE Application forms are available from: Postgraduate Admissions Office Sussex House University of Sussex Brighton BN1 9RH England, U.K. Tel: +44 (0)1273 678412 Email: PG.Admissions@admin.susx.ac.uk Early application is encouraged: there are a limited number of places on the MSc. If you have any further queries about this degree, please contact: Dr D Cliff School of Cognitive and Computing Sciences University of Sussex Brighton BN1 9QH England, U.K. Tel: +44 (0)1273 678754 Fax: +44 (0)1273 671320 E-mail: davec@cogs.susx.ac.uk From owner-gann-list Fri Jan 26 15:04:48 1996 Received: (from mdomo@localhost) by cs.iastate.edu (8.7.1/8.7.1) id PAA11931 for gann-list-outgoing; Fri, 26 Jan 1996 15:04:46 -0600 (CST) From: gendalia@iastate.edu Date: Fri, 26 Jan 1996 15:04:40 -0600 Message-Id: <9601262104.AA11824@esoteric.agron.iastate.edu> To: gann-list@cs.iastate.edu Subject: GANN: test Sender: owner-gann-list@cs.iastate.edu Precedence: bulk Reply-To: gendalia@iastate.edu ugh From owner-gann-list Fri Jan 26 15:08:08 1996 Received: (from mdomo@localhost) by cs.iastate.edu (8.7.1/8.7.1) id PAA13002 for gann-list-outgoing; Fri, 26 Jan 1996 15:08:04 -0600 (CST) From: gendalia@iastate.edu Date: Fri, 26 Jan 1996 15:07:45 -0600 Message-Id: <9601262107.AA11846@esoteric.agron.iastate.edu> To: gann-list@cs.iastate.edu Subject: GANN: test Sender: owner-gann-list@cs.iastate.edu Precedence: bulk Reply-To: gendalia@iastate.edu ugh From owner-gann-list Fri Jan 26 15:11:44 1996 Received: (from mdomo@localhost) by cs.iastate.edu (8.7.1/8.7.1) id PAA14229 for gann-list-outgoing; Fri, 26 Jan 1996 15:11:42 -0600 (CST) Date: Fri, 26 Jan 1996 15:11:32 -0600 (CST) From: "Tracy J. Di Marco" Message-Id: <199601262111.PAA14222@cs.iastate.edu> To: gann-list@cs.iastate.edu Subject: GANN: bah Sender: owner-gann-list@cs.iastate.edu Precedence: bulk Reply-To: "Tracy J. Di Marco" test From owner-gann-list Fri Jan 26 15:13:48 1996 Received: (from mdomo@localhost) by cs.iastate.edu (8.7.1/8.7.1) id PAA14900 for gann-list-outgoing; Fri, 26 Jan 1996 15:13:47 -0600 (CST) Date: Fri, 26 Jan 1996 15:13:41 -0600 (CST) From: "Tracy J. Di Marco" Message-Id: <199601262113.PAA14892@cs.iastate.edu> To: gann-list@cs.iastate.edu Subject: GANN: bah Sender: owner-gann-list@cs.iastate.edu Precedence: bulk Reply-To: "Tracy J. Di Marco" test From owner-gann-list Fri Jan 26 16:47:23 1996 Received: (from mdomo@localhost) by cs.iastate.edu (8.7.1/8.7.1) id QAA19811 for gann-list-outgoing; Fri, 26 Jan 1996 16:11:24 -0600 (CST) From: GANN List Admin Message-Id: <199601262211.QAA06107@ren.cs.iastate.edu> Subject: GANN: Apologies.. To: gann-list@cs.iastate.edu Date: Fri, 26 Jan 1996 16:11:17 -0600 (CST) X-Mailer: ELM [version 2.4 PL25] Content-Type: text Sender: owner-gann-list@cs.iastate.edu Precedence: bulk Reply-To: GANN List Admin Dear Colleagues, As you might have observed, a couple of messages sent out in the past week on the GANN list were incomplete, each missing a few sentences at the beginning --- rendering them quite unintelligible. This message is to inform you that our list handling software (and hence we!) are solely responsible for these unfortunate events. The originators of those mails had no hand in the mishaps. Our sincere apologies for the inconvenience caused. Due to a recent system upgrade (that is what the systems people call it :-)), the list functioning has been thrown a little out of gear, but things are fast returning to normal. Please bear with us. Finally, if you sent a message in the past week that hasn't made it to the list yet, it probably means the message was lost by our system here. In that event, kindly resend the message and we will ensure its broadcast. We are extremely sorry for this extra trouble. Thanks, - GANN Admin - From owner-gann-list Sun Jan 28 05:46:48 1996 Received: (from mdomo@localhost) by cs.iastate.edu (8.7.1/8.7.1) id FAA11596 for gann-list-outgoing; Sun, 28 Jan 1996 05:20:46 -0600 (CST) From: N.Sharkey@dcs.shef.ac.uk Date: Sun, 28 Jan 96 11:18:58 GMT Message-Id: <9601281118.AA28201@entropy.dcs.shef.ac.uk> To: intcon@phoenix.ee.unsw.edu.au, reinforce@cs.uwa.edu.au, gann-list@cs.iastate.edu, connectionists@CS.CMU.EDU Subject: GANN: EANN-96 - ROBOTICS Cc: N.Sharkey@dcs.shef.ac.uk Sender: owner-gann-list@cs.iastate.edu Precedence: bulk Reply-To: N.Sharkey@dcs.shef.ac.uk Sorry if you get this twice, but I messed up the mailing last week. *** ROBOTICS TRACK of EANN-96 *** London, UK: 17-19 June, 1996. For those of you a bit late in submitting you abstracts (200-400 words) for the Robotics track of EANN-96, you can send them directly to me electronically at n.sharkey@dcs.shef.ac.uk (or fax). But please let me know of your intention to do so. For more information on EANN '96: http://www.lpac.ac.uk/EANN96 For reports on EANN '95, contents of the proceedings, etc.: http://www.abo.fi/~abulsari/EANN95.html Please mention two to four keywords, and whether you prefer it to be a short paper or a full paper. The short papers will be 4 pages in length, and full papers may be upto 8 pages. Notification of acceptance will be sent around 15 February. noel Noel Sharkey Professor of Computer Science Department of Computer Science Regent Court University of Sheffield S1 4DP, Sheffield, UK N.Sharkey@dcs.shef.ac.uk FAX: (0114) 2780972