Received: from ics.uci.edu by paris.ics.uci.edu id aa09484; 10 Apr 94 21:32 PDT Received: from ics.uci.edu by q2.ics.uci.edu id aa19395; 10 Apr 94 21:32 PDT To: ML-LIST: ; Subject: Machine Learning List: Vol. 6 No. 11 Reply-to: ml@ics.uci.edu Date: Sun, 10 Apr 1994 21:19:20 -0700 From: Michael Pazzani Message-ID: <9404102132.aa19395@q2.ics.uci.edu> Machine Learning List: Vol. 6 No. 11 Sunday, April 10, 1994 Contents: on-line information for ML94 and COLT94 Postdoc position announcement Workshop on Learning and Descriptional Complexity CFP ML94 Workshopon Molecular Biology The 13th World Computer Congress - IFIP Congress 94 Informatica paper available New Machine Learning Volume IJCAI-95 COMPMED 94 FINAL SCHEDULE SBIA94 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: ---------------------------------------------------------------------- Date: Fri, 1 Apr 94 20:10:28 EST From: Haym Hirsh Subject: on-line information for ML94 and COLT94 Information for this summer's Machine Learning (ML94) and Computational Learning Theory (COLT94) conferences is now available on-line. Users of anonymous ftp can find the information on www.cs.rutgers.edu in the directory "/pub/learning94". Users of www information servers such as mosaic can find the information at "http://www.cs.rutgers.edu/pub/learning94/learning94.html". Please send comments or questions to ml94@cs.rutgers.edu. Please note that the early registration deadline is May 27, and (for those planning on staying at the nearby Hyatt rather than in dorms), conference room rates are only guaranteed until June 10. Finally, the conferences coincide this year with World Cup soccer matches being held at Giants Stadium in East Rutherford, New Jersey. These games are expected to be the largest sporting event ever held in the New York metropolitan area, and we therefore strongly encourage conference attendees to make travel arrangements as early as possible. Haym ------------------------------ From: "Dr. Dennis Bahler" Subject: Postdoc position announcement Date: Mon, 4 Apr 94 13:24:19 EDT POSTDOCTORAL POSITION NATIONAL INSTITUTE OF ENVIRONMENTAL HEALTH SCIENCES The National Institute of Environmental Health Sciences in Research Triangle Park, North Carolina has an opening for a postdoctoral research position in computer science. The person in this position will join an existing research team studying the application of methods from artificial intelligence to the prediction of risks from exposure to chemical agents. Experience with inductive learning methods, decision trees and neural networks is beneficial. Minority candidates and women are encouraged to apply. Appointees must be U.S. citizens or permanent U.S. residents. Curriculum Vitae and three letters of reference should be sent to: Dr. Christopher J. Portier Laboratory of Quantitative and Computational Biology National Institute of Environmental Health Sciences PO Box 12233 Mail Drop A3-06 Research Triangle Park, North Carolina 27709 Curriculum vitaes will be accepted via e-mail to portier@niehs.nih.gov. Reference letters can be sent by e-mail and followed by hard copy. Candidates will be interviewed after April 21, 1994. Applications will be accepted until a candidate is chosen. ------------------------------ Date: Wed, 6 Apr 94 16:17:08 EDT From: Edwin Pednault Subject: Workshop on Learning and Descriptional Complexity Workshop on Applications of Descriptional Complexity to Inductive, Statistical, and Visual Inference Sunday, July 10, 1994 Rutgers University New Brunswick, New Jersey Held in Conjunction with the Eleventh International Conference on Machine Learning (ML94, July 11-13, 1994) and the Seventh Annual Conference on Computational Learning Theory (COLT94, July 12-15, 1994). Interest in the minimum description-length (MDL) principle is increasing in the machine learning and computational learning theory communities. One reason is that MDL provides a basis for inductive learning in the presence of noise and other forms of uncertainty. Another reason is that it enables one to combine and compare different kinds of data models within a single unified framework, allowing a wide range of inductive-inference problems to be addressed. Interest in the MDL principle is not restricted to the learning community. Inductive-inference problems arise in one form or another in many disciplines, including information theory, statistics, computer vision, and signal processing. In each of these disciplines, inductive-inference problems have been successfully pursued using the MDL principle and related descriptional complexity measures, such as stochastic complexity, predictive MDL, and algorithmic probability. The purpose of this workshop is two fold: (1) to provide an opportunity to researchers in all disciplines involved with descriptional complexity to meet and share results; and (2) to foster greater interaction between the descriptional complexity community and the machine learning and computational learning theory communities, enabling each group to benefit from the results and insights of the others. To meet these objectives, the format of the workshop is designed to maximize opportunities for interaction among participants. In addition, a tutorial on descriptional complexity will be held prior to the workshop to encourage broad participation. The tutorial and workshop may be attended together or individually. The topics of the workshop will include, but will not be limited to, - Applications of descriptional complexity to all forms of inductive inference, including those in statistics, machine learning, computer vision, pattern recognition, and signal processing. - Rates of convergence, error bounds, distortion bounds, and other convergence and accuracy results. - New descriptional complexity measures for inductive learning. - Specializations and approximations of complexity measures that take advantage of problem-specific constraints. - Representational techniques, search techniques, and other application and implementation related issues. - Theoretical and empirical comparisons between different descriptional complexity measures, and with other learning techniques. WORKSHOP FORMAT The workshop will be held on Sunday, July 10, 1994. Attendance will be open. However, those who wish to attend should contact the organizers prior to the workshop at the address below. To maximize the opportunity for interaction, the workshop will consist primarily of poster presentations, with a few selected talks and a moderated wrap-up discussion. Posters will be the primary medium for presentation. This medium was chosen because it encourages close interaction between participants, and because many more posters can be accommodated than talks. Both factors should encourage productive interaction across a wide range of topics despite the constraints of a one-day workshop. Depending on the number and quality of the submissions, arrangements may be made to publish a book of papers after the workshop under the auspices of the International Federation for Information Processing Working Group 14.2 on Descriptional Complexity. SUBMISSIONS Posters will be accepted on the basis of extended abstracts that should not exceed 3000 words, excluding references (i.e., about six pages of text, single spaced). Separate one-page summaries should accompany the submitted abstracts. The summary pages of accepted abstracts will be distributed to all interested participants prior to the workshop, and should be written accordingly. Summaries longer than one page will have only their first page distributed. Six copies of each extended abstract and two copies of each summary page must be received at the address below by May 18, 1994. Acceptance decisions will be made by June 10, 1994. Copies of the summary pages of accepted abstracts will be mailed to all those who submit abstracts and to those who contact the organizers before the decision date. Because we expect the audience to be diverse, clarity of presentation will be a criterion in the review process. Contributions and key insights should be clearly conveyed with a wide audience in mind. Authors whose submissions are accepted will be expected to provide the organizers with full-length papers or revised versions of their extended abstracts when they arrive at the workshop. These papers and abstracts will be used for the publisher's review. Authors may wish to bring additional copies to distribute at the workshop. IMPORTANT DATES May 18 Extended abstracts due June 10 Acceptance decisions made, summary pages distributed July 10 Workshop PROGRAM COMMITTEE Ed Pednault (Chair), AT&T Bell Laboratories. Andrew Barron, Yale University. Ron Book, University of California, Santa Barbara. Tom Cover, Stanford University. Juris Hartmanis, Cornell University. Shuichi Itoh, University of Electro-Communications. Jorma Rissanen, IBM Almaden Research Center. Paul Vitanyi, CWI and University of Amsterdam. Detlef Wotschke, University of Frankfurt. Kenji Yamanishi, NEC Corporation. CONTACT ADDRESS Ed Pednault AT&T Bell Laboratories, 4G-318 101 Crawfords Corner Road Holmdel, NJ 07733-3030 email: epdp@research.att.com tel: 908-949-1074 ----------------------------------------------------------------------- Tutorial on Descriptional Complexity and Inductive Learning One of the earliest theories of inductive inference was first formulated by Solomonoff in the late fifties and early sixties. It was expanded in subsequent and, in some cases, independent work by Solomonoff, Kolmogorov, Chaitin, Wallace, Rissanen, and others. The theory received its first citation in the AI literature even before its official publication. It provides a basis for learning both deterministic and probabilistic target concepts, and it establishes bounds on what is computationally learnable in the limit. Over time, this theory found its way into several fields, including probability theory and theoretical computer science. In probability theory, it provides a precise mathematical definition for the notion of a random sample sequence. In theoretical computer science, it is being used among other things to prove lower bounds on the computational complexity of problems, to analyze average-case behavior of algorithms, and to explore the relationship between the succinctness of a representation and the computational complexity of algorithms that employ that representation. Interest in the theory diminished in artificial intelligence in the mid to late sixties because of the inherent intractability of the theory in its most general form. However, research in the seventies and early eighties led to several tractable specializations developed expressly for inductive inference. These specializations in turn led to applications in many disciplines, including information theory, statistics, machine learning, computer vision, and signal processing. The body of theory as it now stands has developed well beyond its origins in inductive inference, encompassing algorithmic probability, Kolmogorov complexity, algorithmic information theory, generalized Kolmogorov complexity, minimum message-length inference, the minimum description-length (MDL) principle, stochastic complexity, predictive MDL, and related concepts. It is being referred to collectively as descriptional complexity to reflect this evolution. This tutorial will provide an introduction to the principal concepts and results of descriptional complexity as they apply to inductive inference. The practical application of these results will be illustrated through case studies drawn from statistics, machine learning, and computer vision. No prior background will be assumed in the presentation other than a passing familiarity with probability theory and the theory of computation. Attendees should expect to gain a sound conceptual understanding of descriptional complexity and its main results. The tutorial will be held on Sunday, July 10, 1994. ------------------------------ Date: Wed, 6 Apr 94 18:31:54 EDT From: "Michiel (Mick" Subject: CFP ML94 Workshop on Molecular Biology Computational Molecular Biology and Machine Learning Workshop Machine Learning Conference 1994 Program Committee: Michiel Noordewier (Rutgers University) Lindley Darden (Rockefeller University) Description and Focus This workshop will focus on the application of methods from machine learning to the promising problem area of molecular biology. A goal is to consolidate a machine learning faction in the emerging field of computational biology. One problem area is identified as genetic sequence search and analysis, and protein structure prediction. Biological sequences have become a ready source of sample data for machine learning approaches to classification. Recently such sequences have also provided problems for sophisticated pattern recognition paradigms, including those borrowed from computational linguistics, Bayesian methods, and artificial neural networks. This workshop will bring together workers using such diverse approaches, and will focus on the rich set of problems presented by the recent availability of extensive biological sequence information. Another area of applicability of ML techniques to molecular biology is in the application of computational discovery methods. Such methods are employed for forming, ranking, evaluating, and improving hypotheses. Learning strategies using analogies or homologies among molecules or processes from different organisms or species are also of interest. The format of the workshop will be the presentation of short papers followed by panel discussions. Submission Requirements Persons wishing to attend the workshop should submit three copies of a 1-2 page research summary including a list of relevant publications, along with a phone number and an electronic mail address. Persons wishing to make presentations at the workshop should submit three copies of a short paper (no more than 10 pages) or extended abstract, in addition to the research summary. All submissions must be received by May 1, 1994. Notification of acceptance or rejection will be mailed to applicants by May 15, 1994. A set of working notes will be distributed at the workshop. Camera ready copies of papers accepted for inclusion in the working notes of the workshop will be due on June, 15, 1994. The timetable is as follows: Abstracts, papers, etc due to chair 1 May Decisions made, submitters get feedback 15 May Final working-note submissions rcv'd by chair 15 June workshop date 10 July, 1994 ------------------------------ Subject: The 13th World Computer Congress - IFIP Congress 94 Date: Wed, 06 Apr 94 14:59:42 +0200 From: morik@gmdzi.gmd.de The 13th World Computer Congress - IFIP Congress 94 August 28 - September 2, 1994 CCH Congress Centrum Hamburg, Germany The theme of the Congress is Computer and Communications Evolution -The Driving Forces. The Congress will focus on developments that will begin to have impact by the end of the 20th century, during the five years following the Congress. It will be a true 'Congress' where the participants will actually develop a message. On Tuesday, August 30, a workshop on Machine Learning - A New Technology and Its Applications will be held. The program is: Machine Learning and Knowledge Acquisition - Yves Kodratoff MOBAL - A Modular Tool for Making Applications Adaptive - Stefan Wrobel A Real-World Application in Trouble-Shooting - Lorenza Saitta Machine Learning in VLSI Design - Juergen Herrmann Exploiting Machine Learning for Design Tasks - Tim Parsons (not yet acknowledged) Machine Learning in Robot Navigation - Katharina Morik Machine Learning in Robot Assembly - Attilio Giordana The workshop is included in the general Congress. For participation, please contact the Conference Secretariat IFIP 94 P.O.Box 30 24 80 Hamburg 36, Germany Tel.: +49 49 45 69 22 42 Fax: +49 40 35 69 23 43 or e-mail to Prof. Kaiser (Univ. Hamburg) kaiser@informatik.uni-hamburg.de ------------------------------ Date: Mon, 4 Apr 94 17:13:56 EDT From: gordon@aic.nrl.navy.mil Subject: Informatica paper available The following paper was published in a special issue of Informatica journal edited by George Tecuci (and which George announced in a previous issue of ml_list). We are making copies available for anyone who has difficulty getting copies of Informatica. "A multistrategy learning scheme for agent knowledge acquisition" Diana Gordon Devika Subramanian Abstract: The problem of designing and refining task-level strategies in an embedded multiagent setting is an important unsolved question. To address this problem, we have developed a multistrategy system that combines two learning methods: compilation of high-level advice provided by a human, and incremental refinement by a genetic algorithm. The first method generates seed rules for finer-grained refinements by the genetic algorithm. Our multistrategy learning system is evaluated on two complex simulated domains as well as with a Nomad 200 robot. This paper is a reprint of our MSL93 paper - with some improvements. We are planning to make our compiler code available very soon. Please send requests for reprints of our paper (hardcopy or PostScript) or our (prolog) code to gordon@aic.nrl.navy.mil or devika@cs.cornell.edu. Diana Gordon Devika Subramanian ------------------------------ Date: Thu, 7 Apr 1994 16:47:27 -0400 (EDT) From: Stephen Hanson Subject: New Machine Learning Volume This is a new volume just published that may be of interest to you: COMPUTATIONAL LEARNING THEORY and NATURAL LEARNING SYSTEMS Constraints and Prospects MIT/BRADFORD 1994. Editors, S. Hanson, G. Drastal, R. Rivest Table of Contents FOUNDATIONS Daniel Osherson, Massachusetts Institute of Technology, Michael Stob, Calvin College, and Scott Weinstein, University of Pennsylvania. {em Logic and Learning} Ranan Banerji, Saint Joseph's University. {em Learning Theoretical Terms} Stephen Judd, Siemens Corporate Research, {em How Network Complexity is Affected by Node Function Sets} Diane Cook, University of Illinois. {em Defining the Limits of Analogical Planning} REPRESENTATION and BIAS Larry Rendell and Raj Seshu, University of Illinois. {em Learning Hard Concepts Through Constructive Induction: Framework and Rationale} Harish Ragavan and Larry Rendell, University of Illinois. {em The Utility of Domain Knowledge for Learning Disjunctive Concepts} George Drastal, Siemens Corporate Research. {em Learning in an Abstraction Space} Raj Seshu, University of Denver. {em Binary Decision Trees and an ``Average-Case'' Model for Concept Learning: Implications for Feature Construction and the Study of Bias} Richard Maclin and Jude Shavlik, University of Wisconsin, Madison. {em Refining Algorithms with Knowledge-Based Neural Networks: Improving the Chou-Fasman Algorithm for Protein Folding} SAMPLING PROBLEMS Michael Kearns and Robert Schapire, Massachusetts Institute of Technology. {em Efficient Distribution-free Learning of Probabilistic Concepts} Marek Karpinski and Thorsten Werther, University of Bonn. {em VC Dimension and Sampling Complexity of Learning Sparse Polynomials and Rational Functions} Haym Hirsh and William Cohen, Rutgers University. {em Learning from Data with Bounded Inconsistency:Theoretical and Experimental Results} Wolfgang Maass and Gyorgy Turan, University of Illinois. {em How Fast Can a Threshold Gate Learn?} Eric Baum, NEC Research Institute. {em When are k-Nearest Neighbor and Back Propagation Accurate for Feasible Sized Sets of Examples?} EXPERIMENTAL Ross Quinlan, University of Sydney. {em Comparing Connectionist and Symbolic Learning Methods} Andreas Weigend and David Rumelhart, Stanford University. {em Weight-Elimination and Effective Network Size} Ronald Rivest and Yiqun Yin, Massachusetts Institute of Technology. {em Simulation Results for a New Two-Armed Bandit Heuristic} Susan Epstein, Hunter College. {em Hard Questions About Easy Tasks: Issues From Learning to Play Games} Lorien Pratt, Rutgers University. {em Experiments on the Transfer of Knowledge between Neural Networks} Stephen J. Hanson, Ph.D. Head, Learning Systems Department SIEMENS Research 755 College Rd. East Princeton, NJ 08540 ------------------------------ Date: Fri, 1 Apr 94 11:18:58 PST From: Rick Skalsky Subject: IJCAI-95 CALL FOR PARTICIPATION: IJCAI-95 IJCAI-95 will take place at the Palais de Congres, Montreal, August 20-25 1995. The biennial IJCAI conferences are the major forums for the international scientific exchange and presentation of AI research. The Conference Technical Program will include workshops, tutorials, panels and invited talks, as well as tracks for paper and videotape presentations. PAPER TRACK: SUBMISSION REQUIREMENTS AND GUIDELINES Topics of Interest Submissions are invited on substantial, original, and previously unpublished research in all aspects of AI, including, but not limited to: * Architectures and languages for AI (e.g. parallel hardware and software for building AI systems) * Artistic, entertainment and multimedia applications. * Automated reasoning (e.g. theorem proving, abduction, automatic programming, search, context management and truth maintenance systems, constraint satisfaction, satisfiability checking) * Cognitive modeling (e.g. user models, memory models) * Connectionist and PDP models * Distributed AI, autonomous agents, multi-agent systems and real-time issues. * Intelligent teaching systems * Knowledge Engineering and Principles of AI applications (e.g. for design, manufacturing control, grand challenge applications) * Knowledge representation (e.g. logics for knowledge, action, belief and intention, nonmonotonic formalisms, complexity analysis, languages and systems for representing knowledge) * Learning, knowledge acquisition and case-based reasoning * Logic programming (e.g. semantics, deductive databases, relationships to AI knowledge representation) * Natural language (e.g. syntax, semantics, discourse, speech recognition and understanding, natural language front ends, generation systems, information extraction and retrieval) * Philosophical foundations * Planning and reasoning about action (including the relation between planning and control) * Qualitative reasoning and naive physics (e.g. temporal and spatial reasoning, model-based reasoning, diagnosis) * Reasoning under uncertainty (including fuzzy logic and fuzzy control) * Robotic and artificial life systems (e.g. unmanned vehicles, vision/manipulation systems) * Social, economic and legal implications * Vision (e.g. color, shape, stereo, motion, object recognition, active vision, model-based vision, vision architectures and hardware, biological modeling). Timetable Submissions must be received by 6th January 1995. Submissions received after that date will be returned unopened. Authors should note that ordinary mail can sometimes be considerably delayed, especially over the new year period, and should take this into account when timing their submissions. Notification of receipt will be mailed to the first author (or designated author) soon after receipt. Notification of acceptance or rejection: successful authors will be notified on or before 20th March 1995. Unsuccessful authors will be notified by 27th March 1995. Notification will be sent to the first author (or designated author). Camera ready copies of the final versions of accepted papers must be received by the publisher in the USA by 24th April 1995. Note that at least one author of each accepted paper is required to attend the conference to present the work. General Authors should submit six (6) copies of their papers in hard copy form. All paper submissions should be to the following address. Electronic or fax submissions cannot be accepted. IJCAI-95 Paper Submissions, American Association for Artificial Intelligence, 445, Burgess Drive, Menlo Park, CA. 94025, USA. (telephone (415) 328-3123, email ijcai@aaai.org). Appearance and Length Papers should be printed on 8.5'' x 11'' or A4 sized paper. They must be a maximum of 15 pages long, each page having no more than 43 lines, lines being at most 140mm long and with 12 point type. Title, abstract, figures and references must be included within this length limit. Papers breaking these rules will not be considered for presentation at the conference. Letter quality print is required. (Normally, dot-matrix printout will be unacceptable unless truly of letter quality. Exceptions will be made for submissions from countries where high quality printers are not widely available.) Title Page Each copy of the paper must include a title page, separate from the body of the paper. This should contain: * Title of the paper * Full names, postal addresses, phone numbers, fax numbers and email addresses (where these exist) of all authors. The first postal address should be one that is suitable for delivery of items by courier service * An abstract of 100-200 words * A set of keywords giving the area/subarea of the paper and describing the topic of the paper. This information, together with the title of the paper, will be the main information used in allocating reviewers. * The following declaration: ``This paper has not already been accepted by and is not currently under review for a journal or another conference. Nor will it be submitted for such during IJCAI's review period.'' Policy on Multiple Submissions IJCAI will not accept any paper which, at the time of submission, is under review for a journal or another conference. Authors are also expected not to submit their papers elsewhere during IJCAI's review period. These restrictions apply only to journals and conferences, not to workshops and similar specialized presentations with a limited audience. Review Criteria Papers will be subject to peer review, but this review will not be ``blind'' (that is, the reviewers will be aware of the names of the authors). Selection criteria include accuracy and originality of ideas, clarity and significance of results and the quality of the presentation. The decision of the Program Committee, taking into consideration the individual reviews, will be final and cannot be appealed. Papers selected will be scheduled for presentation and will be printed in the proceedings. Authors of accepted papers, or their representatives, are expected to present their papers at the conference. Distinguished Paper Awards The Program Committee will distinguish one or more papers of exceptional quality for special awards. This decision will in no way depend on whether the authors choose to enhance their paper with a video presentation. Other Calls Calls for tutorial and workshop proposals and video presentations for IJCAI-95 will be issued shortly. For questions or comments, (415) 328-3123, email ijcai@aaai.org ------------------------------ Date: Tue, 5 Apr 1994 14:44:31 +0600 From: mwitten@chpc.utexas.edu Subject: COMPMED 94 FINAL SCHEDULE FINAL PROGRAM ANNOUNCEMENT FIRST WORLD CONGRESS ON COMPUTATIONAL MEDICINE AND PUBLIC HEALTH 24-28 April 1994 Hyatt on the Lake, Austin, Texas The final program for the First World Congress On Computational Medicine and Public Health has now been set. Over 200 speakers will be presenting work in a variety of applications areas related to medicine and public health. Registration is still open for attendees. Registration details and/or a copy of the schedule at a glance, schedule-in-detail may be requested by sending an email request to compmed94@chpc.utexas.edu or by calling 512-471-2472 or by faxing 512-471-2445 There is no ftp form of the conference schedule due to the size of the file. We will be happy to fax/send a copy to anyone who requests it. The conference proceedings will appear as a series of volumes published by World Scientific. If you are interested in possibly submitting a paper for the proceedings, please contact mwitten@chpc.utexas.edu or call 512-471-2457 The overwhelming response to this congress has already justified having a second world congress in the future. The tentative schedule is to have it in 3 years. If you are interested in participating at the 2nd World Congress On Computational Medicine and Public Health, please contact Dr. Matthew Witten Congress Chair mwitten@chpc.utexas.edu ------------------------------ Date: Wed, 6 Apr 94 11:20:46 WST From: Simposio Brasileiro de Inteligencia Artificial 94 Subject: SBIA94 XI Brazilian Symposium on Artificial Intelligence SBIA '94 October 17th - 21th 1994, Fortaleza CALL FOR PAPERS +++++++++++++++ SBIA'94 is the eleventh National Brazilian Conference on Artificial Intelligence, to be held this year in Fortaleza, located in the sunny northeastern coast of Brazil. The conference is intended to promote research and scientific interchange, by putting together researchers, students and practitioners. Papers on all current areas and aspects of AI are welcome. The conference program includes, but is not restricted to, the following topics: AI Architectures, AI in Design, Artificial Life, Automated Reasoning, Belief Revision, Cognitive Modeling, Common Sense Reasoning, Control Systems, Decision Theory, Distributed AI, Expert Systems, Game Playing, Geometric Reasoning, Intelligent Tutoring Systems, Knowledge Acquisition, Knowledge Representation, Learning Environments, Logic Programming, Machine Learning, Machine Translation, Mathematical Foundations, Natural Language Processing, Neural Networks, Non-classical Logics in AI, Non-monotonic Reasoning, Pattern Recognition, Perception, Philosophical Foundations, Planning, Problem Solving, Qualitative Reasoning, Robotics, Scientific Discovery, Speech Understanding, Theorem Proving, User Interfaces, Virtual Reality, Vision. INSTRUCTIONS FOR SUBMISSION Authors are requested to submit 5 copies of a complete paper written in English in hardcopy format, with at most 14 pages. Papers should be printed on A4 sized paper, with 12 point type. Copies should be sent, not later than May 27, 1994, to the Program Chairperson: Prof. Tarcisio Pequeno, SBIA94 LIA/Departamento de Computacao/UFC Campus do Pici, Bloco 910 60455-760 Fortaleza CE BRASIL e-mail: tarcisio@lia1.ufc.br IMPORTANT DATES Deadline for submission: May.27.1994 Notification to the authors: by July.8.1994 Final Version: July.29.1994 Inquires regarding the program or local arrangements should be directed to: e-mail: sbia94@lia1.ufc.br fax: +55.85.223-1333 Sponsored by The Brazilian Computer Society ------------------------------ End of ML-LIST (Digest format) ****************************************