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From: birnbaum@fido.ils.nwu.edu (Lawrence Birnbaum)
To: mod-ki@unido.informatik.uni-dortmund.de
Date: Tue, 18 Dec 90 12:02:01 CST
Subject: <mod-ki 0620> ML91 Final Call for Papers
Keywords: Workshops, Maschinelles Lernen

mod-ki message 0620            COPYRIGHT 1991 Hans-Werner Hein, Dortmund
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          THE EIGHTH INTERNATIONAL WORKSHOP ON MACHINE LEARNING
 
                           CALL FOR PAPERS

On behalf of the organizing committee, and the individual workshop
committees, we are pleased to announce submission details for the eight
workshop tracks that will constitute ML91, the Eighth International
Workshop on Machine Learning, to be held at Northwestern University,
Evanston, Illinois, USA, June 27-29, 1991.  The eight workshops are:

        o Automated Knowledge Acquisition
        o Computational Models of Human Learning
        o Constructive Induction
        o Learning from Theory and Data
        o Learning in Intelligent Information Retrieval
        o Learning Reaction Strategies
        o Learning Relations
        o Machine Learning in Engineering Automation

Please note that submissions must be made to the workshops individually,
at the addresses given below, by March 1, 1991.  The Proceedings of ML91
will be published by Morgan Kaufmann.  Questions concerning individual
workshops should be directed to members of the workshop committees. All
other questions should be directed to the program co-chairs at
ml91@ils.nwu.edu. Details concerning the individual workshops follow.
 
        Larry Birnbaum
        Gregg Collins
 
        Northwestern University
        The Institute for the Learning Sciences
        1890 Maple Avenue
        Evanston, IL 60201
        phone (708) 491-3500

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                       AUTOMATED KNOWLEDGE ACQUISITION
 
Research in automated knowledge acquisition shares the primary objective
of machine learning research: building effective knowledge bases.
However, while machine learning focuses on autonomous "knowledge
discovery," automated knowledge acquisition focuses on interactive
knowledge elicitation and formulation. Consequently, research in
automated knowledge acquisition typically stresses different issues,
including how to ask good questions, how to learn from problem-solving
episodes, and how to represent the knowledge that experts can provide. 
In addition to the task of classification, which is widely studied in
machine learning, automated knowledge acquisition studies a variety of
performance tasks such as diagnosis, monitoring, configuration, and
design.  In doing so, research in automated knowledge acquisition is
exploring a rich space of task-specific knowledge representations and
problem solving methods.

Recently, the automated knowledge acquisition community has proposed
hybrid systems that combine machine learning techniques with interactive
tools for developing knowledge-based systems.  Induction tools in expert
system shells are being used increasingly as knowledge acquisition front
ends, to seed knowledge engineering activities and to facilitate
maintenance.  The possibilities of synergistic human-machine learning
systems are only beginning to be explored.

This workshop will examine topics that span autonomous and interactive
knowledge acquisition approaches, with the aim of productive
cross-fertilization of the automated knowledge acquisition and machine
learning communities.
 
Submissions to the automated knowledge acquisition track should address
basic problems relevant to the construction of knowledge-based systems
using automated techniques that take advantage of human input or
human-generated knowledge sources and provide computational leverage in
producing operational knowledge.

Possible topics include:

o Integrating autonomous learning and focused interaction with an
  expert.
o Learning by asking good questions and integrating an expert's
  responses into a growing knowledge base.
o Using existing knowledge to assist in further knowledge acquisition.
o Acquiring, representing, and using generic task knowledge.
o Analyzing knowledge bases for validity, consistency, completeness,
  and efficiency then providing recommendations and support for revision.
o Automated assistance for theory / model formation and discovery.
o Novel techniques for knowledge acquisition, such as explanation,
  analogy, reduction, case-based reasoning, model-based reasoning,
  and natural language understanding.
o Principles for designing human-machine systems that integrate the
  complimentary computational and cognitive abilities of programs and
  users.

Submissions on other topics relating automated knowledge acquisition and
autonomous learning are also welcome. Each submission should specify the
basic problem addressed, the application task, and the technique for
addressing the problem.
 
WORKSHOP COMMITTEE
 
Ray Bareiss (Northwestern Univ.)
Bruce Buchanan (Univ. of Pittsburg)
Tom Gruber (Stanford Univ.)
Sandy Marcus (Boeing)
Bruce Porter (Univ. of Texas)
David Wilkins (Univ. of Illinois)
 
SUBMISSION DETAILS
 
Papers should be approximately 4000 words in length. Authors should
submit six copies, by March 1, 1991, to:
 
Ray Bareiss
Northwestern University
The Institute for the Learning Sciences
1890 Maple Avenue
Evanston, IL 60201
phone (708) 491-3500

Formats and deadlines for camera-ready copy will be communicated upon
acceptance.

------------------------------------------------------------------------
 
                    COMPUTATIONAL MODELS OF HUMAN LEARNING
 
Details concerning this workshop will be forthcoming as soon as possible.
 
------------------------------------------------------------------------
 
                           CONSTRUCTIVE INDUCTION
 
Selection of an appropriate representation is critical to the success of
most learning systems.  In difficult learning problems (e.g., protein
folding, word pronunciation, relation learning), considerable human
effort is often required to identify the basic terms of the
representation language. Constructive induction offers a partial solution 
to this problem by automatically introducing new terms into the
representation as needed. Automatically constructing new terms is
difficult because the environment or teacher usually provides only
indirect feedback, thus raising the issue of credit assignment. However,
as learning systems face tasks of greater autonomy and complexity,
effective methods for constructive induction are becoming increasingly
important.

The objective of this workshop is to provide a forum for the interchange
of ideas among researchers actively working on constructive induction
issues. It is intended to identify commonalities and differences among
various existing and emerging approaches such as knowledge-based term
construction, relation learning, theory revision in analytic systems,
learning of hidden-units in multi-layer neural networks, rule-creation
in classifier systems, inverse resolution, and qualitative-law discovery.

Submissions are encouraged in the following topic areas:

      o Empirical approaches and the use of inductive biases
      o Use of domain knowledge in the construction and evaluation of new 
        terms
      o Construction of or from relational predicates
      o Theory revision in analytic-learning systems
      o Unsupervised learning and credit assignment in constructive
        induction
      o Interpreting hidden units as constructed features
      o Constructive induction in human learning
      o Techniques for handling noise and uncertainty
      o Experimental studies of constructive induction systems
      o Theoretical proofs, frameworks, and comparative analyses
      o Comparison of techniques from empirical learning, analytical
        learning,
        classifier systems, and neural networks
 
WORKSHOP COMMITTEE
 
Organizing Committee:                   Program Committee:
 
Christopher Matheus (GTE Laboratories)  Chuck Anderson (Colorado State)
George Drastal (Siemens Corp.)          Gunar Liepins (Oak Ridge Nat.Lab)
Larry Rendell (Univ. of Illinois)       Douglas Medin (Univ. of Michigan)
                                        Paul Utgoff (Univ. of
                                        Massachusetts)
 
SUBMISSION DETAILS

Papers should be a maximum of 4000 words in length.  Authors should
include a cover page with authors' names, addresses, phone numbers,
electronic mail addresses, paper title, and a 300 (maximum) word
abstract. Do not indicate or allude to authorship anywhere within the
paper. Send six copies of paper submissions, by March 1, 1991, to:

Christopher Matheus
GTE Laboratories
40 Sylvan Road, MS-45
Waltham MA 02254
(matheus@gte.com)
 
Formats and deadlines for camera-ready copy will be communicated upon
acceptance.
 
------------------------------------------------------------------------
 
                   LEARNING FROM THEORY AND DATA
 
Research in machine learning has primarily focused on either (1)
inductively generalizing a large collection of training data (empirical
learning) or (2) using a few examples to guide transformation of existing 
knowledge into a more usable form (explanation-based learning). Recently
there has been growing interest in combining these two approaches to
learning in order to overcome their individual weaknesses.  Preexisting
knowledge can be used to focus inductive learning and to reduce the
amount of training data needed. Conversely, inductive learning techniques
can be used to correct imperfections in a system's theory of the task at
hand (commonly called "domain theories").

This workshop will discuss techniques for reconciling imperfect domain
theories with collected data.  Most systems that learn from theory and
data can be viewed from the perspective of both data-driven learning (how
preexisting knowledge biases empirical learning) and theory-driven
learning (how empirical data can compensate for imperfect theories).  A
primary goal of the workshop will be to explore the relationship between
these two complementary viewpoints. Papers are solicited on the following 
(and related) topics:

o Techniques for inductively refining domain theories and knowledge
  bases.
o Approaches that use domain theories to initialize an incremental,
  inductive-learning algorithm.
o Theory-driven design and analysis of scientific experiments.
o Systems that tightly couple data-driven and theory-driven learning
  as complementary techniques.
o Empirical studies, on real-world problems, of approaches
  to learning from theory and data.
o Theoretical analyses of the value of preexisting knowledge in inductive
  learning.
o Psychological experiments that investigate the relative roles
  of prior knowledge and direct experience.

WORKSHOP COMMITTEE

Haym Hirsh (Rutgers Univ.), hirsh@cs.rutgers.edu
Ray Mooney (Univ. of Texas), mooney@cs.utexas.edu
Jude Shavlik (Univ. of Wisconsin), shavlik@cs.wisc.edu
 
SUBMISSION DETAILS

Papers should be single-spaced and printed using 12-point type. Authors
must restrict their papers to 4000 words. Papers accepted for general
presentation will be allocated 25 minutes during the workshop and four
pages in the proceedings published by Morgan Kaufmann. There will also be 
a posters session; due to the small number of proceedings pages allocated 
to each workshop, poster papers will not appear in the Morgan Kaufmann
proceedings. Instead, they will be allotted five pages in an informal
proceedings distributed at this particular workshop only. Please indicate 
your preference for general or poster presentation. Also include your
mailing and e-mail addresses, as well as a short list of keywords.

People wishing to discuss their research at the workshop should submit
four (4) copies of a paper, by March 1, 1991, to:

        Jude Shavlik
        Computer Sciences Department
        University of Wisconsin
        1210 W. Dayton Street
        Madison, WI  53706

Formats and deadlines for camera-ready copy will be communicated upon
acceptance.

------------------------------------------------------------------------
 
            LEARNING IN INTELLIGENT INFORMATION RETRIEVAL

The intent of this workshop is to bring together researchers from the
Information Retrieval (IR) and Machine Learning (ML) communities to
explore areas of common interest. Interested researchers are encouraged
to submit papers and proposals for panel discussions.

The main focus will be on issues relating learning to the intelligent
retrieval of textual data.  Such issues include, for example:

 o Descriptive features, clustering, category formation, and
   indexing vocabularies in the domain of queries and documents.
          + Problems of very large, sparse feature sets.
          + Large, structured indexing vocabularies.
          + Clustering for supervised learning.
          + Connectionist cluster learning.
          + Content theories of indexing, similarity, and relevance.

 o Learning from failures and explanations:
          + Dealing with high proportions of negative examples.
          + Explaining failures and successes.
          + Incremental query formulation, incremental concept learning.
          + Exploiting feedback.
          + Dealing with near-misses.
 
 o Learning from and about humans:
          + Intelligent apprentice systems.
          + Acquiring and using knowledge about user needs and goals.
          + Learning new search strategies for differing user needs.
          + Learning to classify via user interaction.

 o Information Retrieval as a testbed for Machine Learning.

 o Particularities of linguistically-derived features.

WORKSHOP COMMITTEE
 
Christopher Owens (Univ. of Chicago), owens@gargoyle.uchicago.edu
David D. Lewis (Univ. of Chicago), lewis@cs.umass.edu
Nicholas Belkin (Rutgers Univ.)
W. Bruce Croft (Univ. of Massachusetts)
Lawrence Hunter (National Library of Medicine)
David Waltz (Thinking Machines Corporation)
 
SUBMISSION DETAILS

Authors should submit 6 copies of their papers. Preference will be given
to papers that sharply focus on a single issue at the intersection of
Information Retrieval and Machine Learning, and that support specific
claims with concrete examples and/or experimental data. To be printed in
the proceedings, papers must not exceed 4 double-column pages
(approximately 4000 words).

Researchers who wish to propose a panel discussion should submit 6 copies
of a proposal consisting of a brief (one page) description of the
proposed topic, followed by a list of the proposed participants and a
brief (one to two paragraph) summary of each participant's relevant work.

Both papers and panel proposals should be received by March 1, 1991, at
the following address:

Christopher Owens
Department of Computer Science
The University of Chicago
1100 East 58th Street
Chicago, IL 60637
Phone: (312) 702-2505

Formats and deadlines for camera-ready copy will be communicated upon
acceptance.

------------------------------------------------------------------------
 
                       LEARNING REACTION STRATEGIES

The computational complexity of classical planning and the need for
real-time response in many applications has led many in AI to focus on
reactive systems, that is, systems that can quickly map situations to
actions without extensive deliberation. Efforts to hand code such
systems  have made it clear that when agents must interact with complex
environments the reactive mapping cannot be fully specified in advance,
but must be adaptable to the agent's particular environment.

Systems that learn reaction strategies from external input in a complex
domain have become an important new focus within the machine learning
community. Techniques used to learn strategies include (but are not
limited to):

        o reinforcement learning
        o using advice and instructions during execution
        o genetic algorithms, including classifier systems
        o compilation learning driven by interaction with the world
        o sensorimotor learning
        o learning world models suitable for conversion into reactions
        o learning appropriate perceptual strategies
 
WORKSHOP COMMITTEE
 
Leslie Kaelbling (Teleos), leslie@teleos.com
Charles Martin (Univ. of Chicago), martin@cs.uchicago.edu
Rich Sutton (GTE), rich@gte.com
Jim Firby (Univ. of Chicago), firby@cs.uchicago.edu
Reid Simmons (CMU), reid.simmons@cs.cmu.edu
Steve Whitehead (Univ. of Rochester), white@cs.rochester.edu
 
SUBMISSION DETAILS
 
Papers must be kept to four two-column pages (approximately 4000 words)
for inclusion in the proceedings. Preference will be given to submissions 
with a single, sharp focus.  Papers must be received by March 1, 1990.

Send 3 copies of the paper to:
 
Charles Martin
Department of Computer Science
University of Chicago
1100 East 58th Street
Chicago, IL 60637
 
Formats and deadlines for camera-ready copy will be communicated upon
acceptance.

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                          LEARNING RELATIONS
 
In the past few years, there have been a number of developments in
empirical learning systems that learn from relational data. Many
applications (e.g. planning, design, programming languages, molecular
structures, database systems, qualitative physical systems) are naturally 
represented in this format. Relations have also been the common language
of many advanced learning styles such as analogy, learning plans and
problem solving. This workshop is intended as a forum for those
researchers doing relational learning to address common issues such as:

Representation: Is the choice of representation a relational language, a
grammar, a plan or explanation, an uncertain or probabilistic variant, or
second order logic? How is the choice extended or restricted for the
purposes of expressiveness or efficiency? How are relational structure
mapped into neural architectures?

Principles: What are the underlying principles guiding the system? For
instance: similarity measures to find analogies between relational
structures such as plans, "minimum encoding" and other approaches to
hypothesis evaluation, the employment of additional knowledge used to
constrain hypothesis generation, mechanisms for retrieval or adapation
of prior plans or explanations.

Theory: What theories have supported the development of the system? For
instance, computational complexity theory, algebraic semantics, Bayesian
and decision theory, psychological learning theories, etc.

Implementation: What indexing, hashing, or programming methodologies have
been used to improve performance and why? For instance, optimizing the
performance for commonly encountered problems (ala CYC).

The committee is soliciting papers that fall into one of three
categories: Theoretical papers are encouraged that define a new
theoretical framework, prove results concerning programs which carry our
constructive or relational learning, or compare theoretical issues in
various frameworks. Implementation papers are encouraged that provide
sufficient details to allow reimplementation of learning algorithms, and
discuss the key time/space complexity details motivating the design.
Experimentation papers are encouraged that compare methods or address
hard learning problems, with appropriate results and supporting
statistics.

WORKSHOP COMMITTEE
 
Wray Buntine (RIACS and NASA Ames Research Center), wray@ptolemy.arc.nasa.gov
Stephen Muggleton (Turing Institute), steve@turing.ac.uk
Michael Pazzani (Univ. of California, Irvine), pazzani@ics.uci.edu
Ross Quinlan (Univ. of Sydney), quinlan@cs.su.oz.au
 
SUBMISSION DETAILS

Those wishing to present papers at the workshop should submit a paper or
an extended abstract, single-spaced on US letter or A4 paper, with a
maximum length of 4000 words. Those wishing to attend but not present
papers should send a 1 page description of their prior work and current
research interests.

Three copies should be sent to arrive by March 1, 1991 to:

Michael Pazzani
ICS Department
University of California
Irvine, CA 92717  USA
 
Formats and deadlines for camera-ready copy will be communicated upon
acceptance.
 
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               MACHINE LEARNING IN ENGINEERING AUTOMATION

Engineering domains present unique challenges to learning systems, such
as handling continuous quantities, mathematical formulas, large problem
spaces, incorporating engineering knowledge, and the need for user-system 
interaction. This session concerns using empirical, explanation-based,
case-based, analogical, and connectionist learning techniques to solve
engineering problems such as design, planning, monitoring, control,
diagnosis, and analysis. Papers should describe new or modified machine
learning systems that are demonstrated with real engineering problems and 
overcome limitations of previous systems.

Papers should satisfy one or more of the following criteria:

o Present new learning techniques for engineering problems.
o Present a detailed case study which illustrates shortcomings
  preventing application of current machine learning technology to
  engineering problems.
o Present a novel application of existing machine learning techniques
  to an engineering problem indicating promising areas for applying
  machine learning techniques to engineering problems.

Machine learning programs being used by engineers must meet complex
requirements. Engineers are accustomed to working with statistical
programs and expect learning systems to handle noise and imprecision in
a reasonable fashion. Engineers often prefer rules and classifications
of events that are more general than characteristic descriptions and
more specific than discriminant descriptions. Engineers have considerable 
domain expertise and want systems that enable application of this
knowledge to the learning task.

This session is intended to bring together machine learning researchers
interested in real-world engineering problems and engineering researchers
interested in solving problems using machine learning technology.

We welcome submissions including but not limited to discussions of
machine learning applied to the following areas:
 
        o manufacturing automation
        o design automation
        o automated process planning
        o production management
        o robotic and vision applications
        o automated monitoring, diagnosis, and control
        o engineering analysis
 
WORKSHOP COMMITTEE
 
Bradley Whitehall (Univ. of Illinois)
Steve Chien (JPL)
Tom Dietterich (Oregon State Univ.)
Richard Doyle (JPL)
Brian Falkenhainer (Xerox PARC)
James Garrett (CMU)
Stephen Lu (Univ. of Illinois)
 
SUBMISSION DETAILS

Submission format will be similar to AAAI-91: 12 point font,
single-spaced, text and figure area 5.5" x 7.5" per page, and a maximum
length of 4000 words. The cover page should include the title of the
paper, names and addresses of all the authors, a list of keywords
describing the paper, and a short (less than 200 words) abstract. Only
hard-copy submissions will be accepted (i.e., no fax or email
submissions).

Four (4) copies of submitted papers should be sent to:
 
Dr. Bradley Whitehall
Knowledge-Based Engineering Systems Research Laboratory
Department of Mechanical and Industrial Engineering
University of Illinois at Urbana-Champaign
1206 West Green Street
Urbana, IL 61801
ml-eng@kbesrl.me.uiuc.edu
 
Formats and deadlines for camera-ready copy will be communicated upon
acceptance.
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