No subject

extremely large search spaces when viewed in terms of their basic input
features. Examples include learning useful behavior for a robot that
receives a continuous stream of video input, or learning to play the game
of Go. For such problems, an unbiased search is infeasible, and a bias must
be employed that focuses the search within the input space so that the size
of the problem is effectively reduced. Letting representations develop as
part of learning may be viewed as a way of establishing such a bias.

Submissions are encouraged on issues including, but not limited to:

  * How can large search spaces be reduced by introducing a helpful bias?
    Existing approaches relevant to this include bias learning and learning
    to learn.

  * How can related problems become a source for helpful biases? This
    question is studied in multitask learning, sequential learning,
    many-layered learning, and lifelong learning.

  * Architectures for variable representations.

  * How may representations be used, and when searching the space of
    representations, what should their evaluation function be? During the
    revival of neural network research in the mid 1980's, it became clear
    that internal representations can be learned based on a global feedback
    signal. However, while this signal is appropriate as an evaluation for
    a complete system, the representations such systems employ may require
    a different evaluation:

    * assessing modularity: Can a representation be used in multiple
      contexts? Structural vs. functional modularity.

    * assessing value: How useful is the information a representation
      extracts to the construction of solutions? This is a credit
      assignment question, and recent work on establishing stable 
      economies of value may shed new light on this.

  * Statistical techniques for assessing modularity. The modularity of a
    representation relates to a reduced dependency on elements that are not
    part of the representation.

  * Bayesian techniques for learning representations. 

  * The relationship between statistical techniques and other approaches to
    credit assignment.

  * Hierarchy. The size of input spaces than can be handled may be scaled
    up by constructing representations from existing representations,
    leading to a hierarchy of representations.

  * Practical methods for hierarchical Bayesian inference.

  * Extracting symbols from sensors. How can raw sensor information be used
    to extract compact representations or symbols?

  * How may representations and the solutions employing them be developed
    simultaneously? One approach to this question is studied in the
    sub-discipline of evolutionary computation known as co-evolution.

  * Methods for constructive induction.  

  * Development of theoretical terms through, for example, predicate
    invention.

  * Emerging issues in evolutionary and computational biology on the
    importance of change of representation in gene expression.

  * Change of representation that occurs over the lifetime of an embedded
    agent.

WORKSHOP FORMAT

The workshop will be organized so as to maximize interaction, discussion,
and exchange of ideas. The day will start with an invited talk and will be
followed by a series of paper presentations grouped by topic. Each
presentation will be short, e.g. 10 or 15 minutes, with 5 minutes allotted
to questions on the content of the talk. At the end of each group of papers
the presenters will participate in a panel discussion to answer questions
of a more general sort related to the topic and the relationship between
the papers in that group. We will include a panel discussion on emerging
problems in the area of development of representation, and conclude the day
by inviting all participants to join in an open discussion with the goal of
identifying the main themes of the day and establishing a research agenda.

PROGRAM CO-CHAIRS

  Edwin de Jong 
  Computer Science Department 
  Brandeis University MS018
  Waltham, MA 02454-9110 
  1.781.736.3366 
  edwin at cs.brandeis.edu

  Tim Oates 
  CSEE Department 
  University of Maryland Baltimore County
  1000 Hilltop Circle 
  Baltimore, MD 21250 
  1.410.455.3082
  oates at cs.umbc.edu

PROGRAM COMMITTEE

  Jonathan Baxter    (WhizBang! Labs)
  Rich Caruana       (Cornell)
  Rod Grupen         (University of Massachusetts, Amherst)
  Tom Heskes         (University of Nijmegen, The Netherlands)
  Leslie Kaelbling   (MIT)
  Justus Piater      (INRIA Rhone-Alpes, France)
  Jude Shavlik       (University of Wisconsin, Madison) 
  Paul Utgoff        (University of Massachusetts, Amherst)

IMPORTANT DATES

  Deadline for submissions:       April 22
  Notification to participants:   May 10
  Camera ready copy due:          May 31

SUBMISSION INFORMATION

Submissions may be either a full technical paper (up to 8 pages) or a
position statement in the form of an extended abstract (one or two
pages). Electronic submissions (PostScript, PDF, or HTML) are preferred and
should be sent by April 22 to either of the co-chairs (Edwin de Jong at
edwin at cs.brandeis.edu or Tim Oates at oates at cs.umbc.edu). Please format
your submission according to the ICML-2002 formatting guidelines.