Received: from ics.uci.edu by paris.ics.uci.edu id aa03445; 30 Dec 94 17:06 PST Received: from ics.uci.edu by q2.ics.uci.edu id aa25927; 30 Dec 94 17:05 PST To: ML-LIST: ; Subject: Machine Learning List: Vol. 6 No. 32 Reply-to: ml@ics.uci.edu Date: Fri, 30 Dec 1994 16:33:57 -0800 From: Michael Pazzani Message-ID: <9412301705.aa25927@q2.ics.uci.edu> Machine Learning List: Vol. 6 No. 32 Friday, Decemeber 30, 1994 Contents: JAIR ML pubs Re: disjunctive concept learning More Info, Special Issue of AIJ on Empirical AI Workshop on Data Engineering for Inductive Learning IJCAI95-workshop: Learning for Natural Language Processing Connectionist-Symbolic Integration ML in Engineering - Call for participation Biological and Evolutionary Information Processing The Machine Learning List is moderated. Contributions should be relevant to the scientific study of machine learning. Mail contributions to ml@ics.uci.edu. Mail requests to be added or deleted to ml-request@ics.uci.edu. Back issues may be FTP'd from ics.uci.edu in pub/ml-list/V/ or N.Z where X and N are the volume and number of the issue; ID: anonymous PASSWORD: URL- http://www.ics.uci.edu/AI/ML/Machine-Learning.html ---------------------------------------------------------------------- Date: Tue, 20 Dec 94 14:35:58 PST From: Steve Minton Subject: JAIR ML pubs Readers of this group may be interested in the following to two ML-related papers recently published in JAIR. Instructions for obtaining JAIR articles are given below: Soderland, S. and Lehnert. W. (1994) "Wrap-Up: a Trainable Discourse Module for Information Extraction", Volume 2, pages 131-158. Postscript: volume2/soderland4a.ps (442K) Abstract: The vast amounts of on-line text now available have led to renewed interest in information extraction (IE) systems that analyze unrestricted text, producing a structured representation of selected information from the text. This paper presents a novel approach that uses machine learning to acquire knowledge for some of the higher level IE processing. Wrap-Up is a trainable IE discourse component that makes intersentential inferences and identifies logical relations among information extracted from the text. Previous corpus-based approaches were limited to lower level processing such as part-of-speech tagging, lexical disambiguation, and dictionary construction. Wrap-Up is fully trainable, and not only automatically decides what classifiers are needed, but even derives the feature set for each classifier automatically. Performance equals that of a partially trainable discourse module requiring manual customization for each domain. Buntine, W.L. (1994) "Operations for Learning with Graphical Models", Volume 2, pages 159-225 Postscript: volume2/buntine94a.ps (1.53M) compressed, volume2/buntine94a.ps.Z (568K) Abstract: This paper is a multidisciplinary review of empirical, statistical learning from a graphical model perspective. Well-known examples of graphical models include Bayesian networks, directed graphs representing a Markov chain, and undirected networks representing a Markov field. These graphical models are extended to model data analysis and empirical learning using the notation of plates. Graphical operations for simplifying and manipulating a problem are provided including decomposition, differentiation, and the manipulation of probability models from the exponential family. Two standard algorithm schemas for learning are reviewed in a graphical framework: Gibbs sampling and the expectation maximization algorithm. Using these operations and schemas, some popular algorithms can be synthesized from their graphical specification. This includes versions of linear regression, techniques for feed-forward networks, and learning Gaussian and discrete Bayesian networks from data. The paper concludes by sketching some implications for data analysis and summarizing how some popular algorithms fall within the framework presented. The main original contributions here are the decomposition techniques and the demonstration that graphical models provide a framework for understanding and developing complex learning algorithms. The PostScript files are available via: -- comp.ai.jair.papers -- World Wide Web: The URL for our World Wide Web server is http://www.cs.washington.edu/research/jair/home.html -- Anonymous FTP from either of the two sites below: CMU: p.gp.cs.cmu.edu directory: /usr/jair/pub/volume2 Genoa: ftp.mrg.dist.unige.it directory: pub/jair/pub/volume2 -- automated email. Send mail to jair@cs.cmu.edu or jair@ftp.mrg.dist.unige.it with the subject AUTORESPOND, and the body GET VOLUME2/BUNTINE94A.PS OR GET VOLUME2/SODERLAND94A.PS. (Either upper or lowercase is fine.) Note: Your mailer might find these files too large to handle. The compressed version of this paper cannot be mailed. -- JAIR Gopher server: At p.gp.cs.cmu.edu, port 70. For more information about JAIR, check out our WWW or FTP sites, or send electronic mail to jair@cs.cmu.edu with the subject AUTORESPOND and the message body HELP, or contact jair-ed@ptolemy.arc.nasa.gov. ------------------------------ Date: Tue, 20 Dec 1994 11:09:39 +1100 From: Ross Quinlan Subject: Re: disjunctive concept learning > I've been doing some experiments in multistrategy learning, and I've been > surprised at how poorly the ml techniques I've been using (lfc++, c4.5 & NNs > trained with conjugate gradient descent backprop) do a learning disjunctive > concepts. > > For example, none of the learners do very well at learning the concept "any > three consecutive bits on" in a length 9 bit string. The neural nets do the > best, but are impractical for similar but larger problems. There must be > symbolic learners that are good at disjunctive concepts, but a quick search > of the literature (well, of the MLj) didn't turn up anything clearly > relevant. With all the CoLT talk of k-DNF learning, I would have thought > there would be a good one somewhere. > > Does anyone have any suggestions for algorithms, or, better yet, ftp-able > programs that do well at these kinds of concepts? No decision-tree method will do well on the 9-bit task above unless it has a lot of data (since the tree representation of the concept is so verbose). I would have thought that FRINGE might do well, and CN2 would certainly be worth a try. However, I think all attribute-value learners would require examples and counter-examples for each of the seven possible positions for consecutive `on' bits. First-order learners have an advantage in this respect. For instance, this problem can be encoded for FOIL using the relations on(A,N) bit N is on in vector A succ(N,N') bit N' follows bit N (i.e. the usual successor relation) threebit(A) vector A is an instance of the concept >From only ten randomly-selected examples and counter-examples of the concept, FOIL generated the correct definition: threebit(A) :- on(A,B), succ(C,B), on(A,C), succ(D,C), on(A,D). Another advantage of using first-order learning is that the vectors do not have to be all the same length -- the representation above allows for arbitrary lengths. [FOIL is available by anonymous ftp from ftp.cs.su.oz.au, file pub/foil6.sh.] ------------------------------ From: Edgar Sommer Subject: Re: disjunctive concept learning Date: Mon, 19 Dec 94 10:08:20 +0100 Perhaps you'd like to stray away from the attr/val world a bit? Disjunctive concepts are quite common in relational learning. (Though I must admit I've never tried learning any three consequutive bits ...) Try Foil, Golem, or Mobal (which includes interfaces to both). http://nathan.gmd.de/projects/ml/home.html (the Machine Learning Group@GMD) ftp://ftp.gmd.de/gmd/mlt/Mobal e. sommer http://nathan.gmd.de/persons/edgar.sommer.html AI Division GMD German National Research Center for Computer Science ------------------------------ From: Matt Schmill Subject: More Info, Special Issue of AIJ on Empirical AI Date: Thu, 22 Dec 1994 15:54:23 -0500 (EST) This is to remind you that papers for the Special Issue of the AI Journal on Empirical AI, edited by Paul Cohen and Bruce Porter, are due on January 10, 1995. The Call for Papers is published at ftp://ftp.cs.umass.edu/pub/eksl/misc/cfp.txt or http://eksl-www.cs.umass.edu/cfp.html Or you can send email to cohen@cs.umass.edu. Please send three copies of your paper to: Paul Cohen Computer Science Department, LGRC University of Massachusetts Box 34610 Amherst, MA 01003-4610 ------------------------------ Date: Mon, 19 Dec 1994 08:40:40 +0500 From: Peter Turney Subject: Workshop on Data Engineering for Inductive Learning CALL FOR PARTICIPATION: Workshop on Data Engineering for Inductive Learning ___________________________________________________________________________ IJCAI-95, Montreal (Canada), August 19/20/21, 1995 Objective _________ In inductive learning, algorithms are applied to data. It is well-understood that attention to both elements is critical __ unless instances are represented so as to make its generalization methods appropriate, no inductive learner can succeed. In applied work, it is not uncommon for practitioners to spend the bulk of their time exploring and transforming data in efforts to enable the use of existing induction techniques. Despite widespread acceptance of these facts, however, research reports normally give data work short shrift. In fact, a report devoted mainly to the data in an induction problem rather than to the algorithms that process it might well be difficult to publish in mainstream machine learning and neural network venues. Our goal in this workshop is to counterbalance the predominant focus on algorithms by providing a forum in which data takes center stage. Specifically, we invite discussion of issues relevant to data engineering, which we define as the transformation of raw data into a form useful as input to algorithms for inductive learning. Data engineering is a concern in industrial and commercial applications of machine learning, neural networks, genetic algorithms, and traditional statistics. Among others, papers of the following kind are welcome: 1. Detailed case studies of data engineering in real-world applications of inductive learning. 2. Descriptions of data engineering techniques or methods that have proven useful across a number of applications. 3. Studies of the data requirements of important inductive learning algorithms, the specifications to which data must be engineered for these algorithms to function. 4. Reports on software tools and environments for data engineering, including papers on "interactive induction" algorithms. 5. Empirical studies documenting the effect of data engineering on the success of induced models. 6. Surveys of data engineering practice in related fields: statistics, pattern recognition, etc. (but not problem-solving or theorem-proving). 7. Papers on constructive induction, feature selection and related techniques. 8. Papers on (re)formulating a problem, to make it suitable for inductive learning techniques. For example, a paper on reformulating the problem of information filtering as learning to classify. This workshop will enable an overview of current work in data engineering. Since the problem of data engineering has received relatively little published attention, it is difficult to anticipate the work that will be presented at this workshop. We expect that the workshop will make it possible to see common trends, shared problems, and clever solutions that we cannot guess at, given our current, limited view of data engineering. We have allowed ample time for discussion of each paper (10 minutes), to foster an atmosphere that will encourage data engineers to share their stories and to seek common elements. We aim to leave the workshop with a vision of the research directions that might bring science into data engineering. Participation _____________ During the workshop, we anticipate approximately 14 presentations. Each paper will be given 25 minutes, 15 minutes for presentation and 10 minutes for discussion. There will be at most 30 participants in the workshop. If you wish to participate in the workshop, you may either submit a paper or a description of work that you have done (are doing, plan to do) that is relevant to the workshop. Papers should be at most 10 pages long. The first page should include the title, the author's name(s) and affiliation(s), a complete mailing address, phone number, fax number, e-mail, an abstract of at most 300 words, and up to five keywords. For those who do not choose to submit a paper, a description of relevant work should be at most 1 page long and should include complete address information. Workshop participants are required to register for the main IJCAI-95 conference. All submissions (papers or descriptions of relevant work) will be reviewed by at least two members of the organizing committee. Please send your submissions to the contact address below. Submissions should be PostScript files, sent by e-mail. Accepted submissions will be available before the workshop through ftp. Workshop participants will also be given copies of the papers on the day of the workshop. In selecting the papers, the committee will aim for breadth of coverage of the topics listed above. Ideally, each of the eight kinds of papers listed above would have at least one representative in the workshop. A paper with new ideas on data engineering will be preferred to a high- quality paper on a familiar idea. The workshop organizers plan to publish revised versions of selected papers from the workshop. The papers would be published either as a book or as a special issue of a journal. The exact date for the workshop has not yet been decided by IJCAI. The workshop is one day in duration and will be held on one of August 19, 20, or 21. Schedule ________ Deadline for submissions: March 31, 1995 Notification of acceptance: April 21, 1995 Submissions available by ftp: April 28, 1995 Actual Workshop: August 19/20/21, 1995 Organizing Committee ____________________ Peter Turney, National Research Council (Canada) Cullen Schaffer, CUNY/Hunter College (USA) Rob Holte, University of Ottawa (Canada) Contact Address _______________ Dr. Peter Turney Knowledge Systems Laboratory Institute for Information Technology National Research Council Canada Ottawa, Ontario, Canada K1A 0R6 (613) 993-8564 (office) (613) 952-7151 (fax) peter@ai.iit.nrc.ca ------------------------------ Date: Tue, 20 Dec 94 12:28:41 +0100 From: Stefan Wermter Subject: IJCAI95-workshop: Learning for Natural Language Processing CALL FOR PAPERS AND PARTICIPATION IJCAI-95 Workshop on New Approaches to Learning for Natural Language Processing International Joint Conference on Artificial Intelligence (IJCAI-95) Palais de Congres, Montreal, Canada currently scheduled for August 21, 1995 ORGANIZING COMMITTEE ____________________ Stefan Wermter Gabriele Scheler Ellen Riloff University of Hamburg Technical University Munich University of Utah PROGRAM COMMITTEE _________________ Jaime Carbonell, Carnegie Mellon University, USA Joachim Diederich, Queensland University of Technology, Australia Georg Dorffner, University of Vienna, Austria Jerry Feldman, ICSI, Berkeley, USA Walther von Hahn, University of Hamburg, Germany Aravind Joshi, University of Pennsylvania, USA Ellen Riloff, University of Utah, USA Gabriele Scheler, Technical University Munich, Germany Stefan Wermter, University of Hamburg, Germany WORKSHOP DESCRIPTION ____________________ In the last few years, there has been a great deal of interest and activity in developing new approaches to learning for natural language processing. Various learning methods have been used, including - connectionist methods/neural networks - machine learning algorithms - hybrid symbolic and subsymbolic methods - statistical techniques - corpus-based approaches. In general, learning methods are designed to support automated knowledge ac- quisition, fault tolerance, plausible induction, and rule inferences. Using learning methods for natural language processing is especially important be- cause language learning is an enabling technology for many other language pro- cessing problems, including noisy speech/language integration, machine trans- lation, and information retrieval. Different methods support language learning to various degrees but, in general, learning is important for building more flexible, scalable, adaptable, and portable natural language systems. This workshop is of interest particularly at this time because systems built by learning methods have reached a level where they can be applied to real-world problems in natural language processing and where they can be compared with more traditional encoding methods. The workshop will bring together researchers from the US/Canada, Europe, Japan, Australia and other countries working on new approaches to language learning. The workshop will provide a forum for discussing various learning approaches for supporting natural language processsing. In particular the workshop will focus on questions like: - How can we apply suitable existing learning methods for language processing? - What new learning methods are needed for language processing and why? - What language knowledge should be learned and why? - What are similarities and differences between different approaches for language learning? (e.g., machine learning algorithms vs neural networks) - What are strengths and limitations of learning rather than manual encoding? - How can learning and encoding be combined in symbolic/connectionist systems? - Which aspects of system architectures and knowledge engineering have to be considered? (e.g., modular, integrated, hybrid systems) - What are successful applications of learning methods in various fields? (speech/language integration, machine translation, information retrieval) - How can we evaluate learning methods using real-world language? (text, speech, dialogs, etc.) WORKSHOP FORMAT _______________ The workshop will provide a forum for the interactive exchange of ideas and knowledge. Approximately 30-40 participants are expected and there will be time for up to 15 presentations depending on the number and quality of paper contri- butions received. Normal presentation length will be 15+5 minutes, leaving time for direct questions after each talk. There may be a few invited talks of 25+5 minutes length. In addition to prepared talks, there will be time for moderat- ed discussions after two related sessions. Furthermore, the moderated discus- sions will provide an opportunity for an open exchange of comments, questions, reactions, and opinions. PUBLICATION ___________ Workshop proceedings will be published by AAAI. If there is sufficient in- terest of the participants of the workshop there may be a possibility to pub- lish the results of the workshop as a book. REGISTRATION ____________ This workshop will take place directly before the general IJCAI-conference. It is an IJCAI policy, that workshop participation is not possible without regis- tration for the general conference. SUBMISSIONS ___________ All submissions will be refereed by the program committee and other experts in the field. Please submit 4 hardcopies AND a postscript file. The paper format is the IJCAI95 format: 12pt article style latex, no more than 43 lines, 15 pages maximum, including title, address and email address, abstract, figures, references. Papers should fit to 8 1/2" x 11" size. Notifications will be sent by email to the first author. Postscript files can be uploaded with anonymous ftp: ftp nats4.informatik.uni-hamburg.de (134.100.10.104) login: anonymous password: cd incoming/ijcai95-workshop binary put quit Hardcopies AND postscript files must arrive not later than 24th February 1995 at the address below. ##############Submission Deadline: 24th February 1995 ##############Notification Date: 24th March 1995 ##############Camera ready Copy: 13th April 1995 Please send correspondence and submissions to: ################################################ Dr. Stefan Wermter Department of Computer Science University of Hamburg Vogt-Koelln-Strasse 30 D-22527 Hamburg Germany phone: +49 40 54715-531 fax: +49 40 54715-515 e-mail: wermter@informatik.uni-hamburg.de ################################################ ------------------------------ Date: Tue, 20 Dec 1994 08:43:30 -0600 From: Ron Sun Subject: Connectionist-Symbolic Integration Call For Papers to the Workshop Connectionist-Symbolic Integration: From Unified to Hybrid Approaches to be held at IJCAI'95 Montreal, Canada August 19-20, 1994 There has been a considerable amount of research in integrating connectionist and symbolic processing. While such an approach has clear advantages, it also encounters serious difficulties and challenges. Therefore, various models and ideas have been proposed to address various problems and aspects in this integration. There is a growing interest from many segments of the AI community, ranging from expert systems, to cognitive modeling, to logical reasoning. Two major trends can be identified in the state of the art: these are the unified or purely and the hybrid approaches to integration. Whereas the purely connectionist ("connectionist-to-the-top") approach claims that complex symbol processing functionalities can be achieved via neural networks alone, the hybrid approach is premised on the complementarity of the two paradigms and aims at their synergistic combination in systems comprising both neural and symbolic components. In fact, these trends can be viewed as two ends of an entire spectrum. Up till now, overall, there is still relatively little work in comparing and combining these fairly isolated efforts. This workshop will provide a forum for discussions and exchanges of ideas in this area, to foster cooperative work. The workshop will tackle important issues in integrating connectionist and symbolic processing. A tentative Schedule Day 1: A. Introduction: * Invited talks These talks will provide an overview of the field and set the tone for ensuing discussions. * Theoretical foundations for integrating connectionist and symbolic processing B. Definition of the two approaches: * Do they exhaust the space of current research in connectionist-symbolic integration, or is there room for additional categories? * How do we compare the unified and hybrid approaches? * Do the unified and hybrid approaches constitute a clearcut dichotomy or are they just endpoints of a continuum? * What class of processes and problems is well-suited to unified or hybrid integration? The relevant motivations and objectives. * What type of model is suitable for what type of application? Enumerate viable target domains. C. State of the art: * Recent or ongoing theoretical or experimental research work * Implemented models belonging to either the unified or hybrid approach * Practical applications of both types of systems Research addressing key issues concerning: * the unified approach: theoretical or practical issues involving systematicity, compositionality and variable binding, biologically inspired models, connectionist knowledge representation, other high-level connectionist models. * the hybrid approach: modes and methods of coupling, task sharing between various components of a hybrid system, knowledge representation and sharing. * both: commonsense reasoning, natural language processing, analogical reasoning, and more generally applications of unified and hybrid models. Day 2: D. Cognitive Aspects: * Cognitive plausibility and relations to other AI paradigms * In cognitive modeling, why should we integrate connectionist and symbolic processing? * Is there a clear cognitive rationale for such integration? (we may need to examine in detail some typical areas, such as commonsense reasoning, and natural language processing) * Is there psychological and/or biological evidence for existing models? If so, what is it? E. Open research issues: * Can we now propose a common terminology with precise definitions for both approaches to connectionist-symbolic integration and for the location on the continuum? * How far can unified systems go? Can unified models be supplemented by hybrid models? Can hybrid models be supplanted by unified models? * Limitations and barriers faced by both approaches * What breakthroughs are needed for both approaches? * Is it possible to synthesize various existing models? Workshop format _ panel discussions _ mini-group discussions: participants will break into groups of 7/8 to discuss a given theme; group leaders will then form a panel to report on group discussions and attempt a synthesis with audience participation _ interactive talks: this is a novel type of oral presentation we will experiment with. Instead of a classical presentation, the speaker will present a problem or issue and give a brief statement of his personal stand (5 min) to launch discussions which he will then moderate and conclude. _ classical slide talks followed by Q/A and discussions. Workshop Co-chairs: Frederic Alexandre, Crin-Cnrs/Inria-Lorraine Ron Sun, The University of Alabama Organizing Committee: John Barnden, New Mexico State University Steve Gallant, Belmont Research Inc. Larry Medsker, American University Christian Pellegrini, University of Geneva Noel Sharkey, Sheffield University Program Committee: Lawrence Bookman (Sun Laboratory, USA) Michael Dyer (UCLA, USA) Wolfgang Ertel (FRW, Germany) LiMin Fu (University of Florida, USA) Jose Gonzalez-Cristobal (UPM, Spain) Ruben Gonzalez-Rubio (University of Sherbrooke, Canada) Jean-Paul Haton (Crin-Inria, France) Melanie Hilario (University of Geneva, Switzerland) Abderrahim Labbi (IMAG, France) Ronald Yager (Iona College, USA) Schedule: _ The submission deadline for participants is February 1, 1995. _ The authors and potential participants will be notified the acceptance decision by March 15, 1995. _ The camera-ready copies of working notes papers will be due on April 15, 1995 Submission: _ If you wish to present a talk, specify the preferred type of presentation (classical or interactive talk) and submit 5 copies of an extended abstract (within the limit of 5-7 pages) to: Ron Sun Department of Computer Science The University of Alabama Tuscaloosa, AL 35487 rsun@cs.ua.edu (205) 348-6363 _ If you only wish to attend the workshop, send 5 copies of a short (no more than one page) description of your interest to the same address above. _ Please be sure to include your e-mail address in all submissions. ------------------------------ From: Benoit Julien Date: Thu, 22 Dec 94 10:58:35 EST Subject: ML in Engineering - Call for participation CALL FOR PARTICIPATION *** Workshop on Machine Learning in Engineering *** International Joint Conference on Artificial Intelligence 1995 IJCAI-95 Montreal, Quebec, Canada August 19-25, 1995 WORKSHOP OBJECTIVES The last ten years have seen a significant increase in the development of knowledge-based systems for engineering applications. As in other domains, the success of knowledge-based approaches in engineering depends critically on the quality of the knowledge acquisition process. Computer-aided engineering system developers in the early nineties quickly recognized the potentials offered by emerging machine learning techniques. As machine learning moves from "toy" problems to "real" engineering applications, a concerted R&D effort becomes essential to identify and overcome critical engineering knowledge acquisition bottlenecks. In that perspective, this workshop will bring together researchers applying or developing machine learning techniques for various engineering disciplines in order to establish important commonalities and differences in engineering learning problems. This forum will permit the definition of basic engineering learning tasks and their relationships with appropriate machine learning strategies. By presenting the state-of-the-art in machine learning applications to engineering, this event should also bridge many gaps between machine learning theory and engineering practice. TOPICS OF INTEREST All researchers and practitioners actively applying or developing machine learning techniques to engineering problems are encouraged to submit papers for this workshop. Topics of interest include, but are not limited to, the following: * Case studies Case studies of application of machine learning in engineering, with analysis of successes and failures. Examples of application topics: - Knowledge mining of engineering databases; - Engineering learning apprentice systems; - Semi-automated engineering knowledge acquisition; - Constructive induction in engineering; - Engineering knowledge discovery systems; - Engineering model acquisition and refinement; - Learning from sensory data. * Comparative studies Comparative studies of machine learning techniques solving similar engineering learning tasks; * Overviews Overviews of the state-of-the-art of machine learning in engineering; * Position papers on key issues Position papers discussing and proposing methodologies for solving important engineering learning issues. Examples of key issues: - Prior knowledge in engineering learning problems; - Tracking engineering concept drifts (dynamic knowledge); - Mapping of generic engineering tasks with learning techniques; - Multistrategy learning for engineering problems; - Machine learning for engineering data analysis; - Learning from very small or very large training sets; - Learning from noisy and incomplete information; - Integration of machine learning and knowledge acquisition. Papers describing strictly case studies of manual knowledge acquisition and maintenance are discouraged. This workshop does not cover applications of subsymbolic learning techniques such a neural networks and genetic algorithms. SUBMISSIONS All papers submitted should not exceed 15 pages. The organizers intend to publish a selection of the accepted papers as a book or a special issue of a journal. The authors should take this into account while preparing their papers. In order to encourage the submission of work in progress reports, 5 pages extended abstracts will also be accepted for submission. However, the accepted extended abstracts will not be considered for later publication. Copies of the workshop proceedings containing all accepted papers and extended abstracts will be prepared and made available by IJCAI at the workshop. Each paper and extended abstract should provide a clear description of the engineering task and the learning problem so that other participants not familiar with the application can easily understands the key characteristics and objectives of the research. The papers should also define all technical terms and make explicit the research methodology and the underlying characteristics and assumptions of the learning problem(s) and technique(s). The authors should also discuss important future issues as well as implications and possible extensions of their work to other engineering domains. Each submitted paper and extended abstract will be reviewed by at least three members of an international program committee and will be judged on significance, originality, and clarity. Papers submitted simultaneously to other conferences or journals must state so on the title page. Those who would like to attend the workshop without giving a presentation should send a 1 page description of relevant research interests with a short list of selected publications. Please send general inquiries to julien@crim.ca. DEADLINES Four (4) hard copies of the papers or extended abstracts must be received by the workshop organiser by February 17, 1995. Alternatively, electronic submissions in postscript are encouraged. FAX submissions are not accepted. Notification of acceptance or rejection will be sent to the first (or designated) author with the reviewers comments by March 24, 1995. Final camera-ready papers and extended abstracts should arrive by April 21, 1995. This one-day workshop will be held between Saturday 19 August and Monday 21 August 1995 inclusive. PAPER FORMAT Submissions must be clearly legible, with good quality print. Papers and extended abstracts are respectively limited to 15 and 5 pages including title page, bibliography, tables and figures. Papers must be printed on 8.5 x 11 inch paper or A4 paper using 12 point type (10 characters per inch) with a 1 inch margins and no more than 40 lines per page. The title page must include the names, postal and electronic (e-mail) addresses and phone and FAX numbers of all authors together with an abstract (200 words maximum) and a list of key words. The first key words should specify the engineering domain (e.g., electrical, civil, mechanical, industrial, chemical, environmental, metalurgy, mining), the engineering generic task (e.g., classification, scheduling, control, maintenance, planning, design), and the machine learning technique(s) used (e.g., case-based learning, conceptual clustering, explanation-based learning, rule induction, inductive predicate logic). Papers without this format will not be reviewed. To save paper and postage costs please use double-sided printing or, preferably, send a postcript file via internet to the workshop organizer. WORKSHOP FORMAT The format of the workshop will be paper sessions with discussion at the end of each session. The day will be divided in four (4) thematic sessions of an hour and a half each. A commentator from the program committee will be assigned for each presentation so as to initiate and supervised the discussions. The workshop will conclude with a panel discussion. The panel discussions will be instrumental in establishing guidelines for future integrations and collaborations and a research agenda for the next five years based on the key multidisciplinary issues identified. The number of participants to the workshop is limited to 40. All workshop participants are expected to register for the main IJCAI conference and to pay an additional fee ($US 50) for the workshop. WORKSHOP CHAIRS Benoit Julien (workshop organiser) Centre de recherche informatique de Montreal (CRIM) 1801, McGill College avenue, Suite 800 Montreal (Quebec) H3A 2N4 Canada phone: 1-514-398-5862 fax: 1-514-398-1244 e-mail: julien@crim.ca Steven J. Fenves Department of Civil Engineering Carnegie Mellon University Pittsburgh, PA, 15213 United States phone: 1-412-268-2944 fax: 1-412-268-7813 e-mail: fenves@ce.cmu.edu Tomasz Arciszewski Systems Engineering Department School of Information Technology and Engineering George Mason University Fairfax, VA, 22030 United States phone: 1-703-993-1513 fax: 1-703-993-1706 e-mail: tarcisze@aic.gmu.edu ------------------------------ Date: Mon, 19 Dec 94 12:27:26 PST From: Russell Anderson Subject: Biological and Evolutionary Information Processing BioSystems Vol. 34, Nos.1-3, pp.1-276 (1995) Special Issue of BioSystems: "Biological and Evolutionary Information Processing --- A Festschrift for Hans J. Bremermann" Editor: Russell W. Anderson _____________________________________________________ CONTENTS Hans J. Bremermann: A pioneer in mathematical biology R.W. Anderson and M. Conrad pp. 1-10. Elements of Systematic Search in Animal Behavior and Model Simulations W. Alt pp. 11-26. Partitioning nonlinearities in the response of honey bee olfactory receptor neurons to binary odors W.M. Getz and R. P. Akers pp.27-40. Return to equilibrium - or - Why we may be losing information from our physiologic experiments S. Zietz pp. 41-46. Network thermodynamics revisited E.R. Lewis pp. 47-64. Intercellular signaling in neuronal-glial networks. M.S. Cooper pp. 65-86. On the maternal transmission of immunity: A `molecular attention' hypothesis R.W. Anderson pp. 87-105 Characterization of prediction in the primate visual smooth pursuit system D.C. Deno, W.F. Crandall, K. Sherman and E.L. Keller pp.107-128. Parameter identification of a neurological control model for pathological head movements of cerebllar patients C.F. Ramos, W.H. Zangemeister and J. Dee pp.129-141. The role of dehydro-Alanine in the design of peptides S. Bhatnagar, G. Subba-Rao and T.P. Singh pp.143-148. Constantly 'awake" brain and Bremermann's question R.K. Mishra pp. 149-160. Evolutionary credit apportionment and its application to time- dependent neural processing R. Smalz and M. Conrad pp.161-172. Computing with dynamic attractors in neural networks M.W. Hirsch and B. Baird pp.173-195. A shape representation for computer vision based on differential topology A.P. Blicher pp. 197-224. A precondition prover for analogy W.W. Bledsoe pp. 225-247. Wavelet variations on the Shannon sampling theorem H. Bray, K. McCormick, R.O. Wells and X. Zhou pp. 249-257. Analytic representation of compactly supported wavelets H.L. Resnikoff pp. 259-272 Russell W. Anderson 2415 College Ave #33 Berkeley, CA 94704 (510) 848-1576 rwa@milo.berkeley.edu ------------------------------ End of ML-LIST (Digest format) ****************************************