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Machine Learning List Vol. 6 No. 11
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<X>/<N> or N.Z where X and N are
the volume and number of the issue; ID: anonymous PASSWORD: <your mail address>
----------------------------------------------------------------------
Date: Fri, 1 Apr 94 20:10:28 EST
From: Haym Hirsh <ml94@athos.rutgers.edu>
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" <drb@ivan.csc.ncsu.edu>
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 <epdp@big.att.com>
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" <noordewi@cs.rutgers.edu>
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 <jose@learning.siemens.com>
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 <skalsky@aaai.org>
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 <sbia94@taiba.ufc.br>
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)
****************************************
