Call for Participation: Workshop on Exploration

[David Cohn]

I am helping organize the following one-day workshop during the
post-NIPS workshops in Vail, Colorado, on December 3, 1993. We would
like to hear from people interested in participating in the workshop,
either formally, as a presenter, or informally, as an attendee. Even
if you will not be able to attend, if you have work which you feel is
relevant, and would like to see discussed, please contact me at the
email address below.

Given the limited time available, we will not be able to present
*every* approach, but we hope to cover a broad range of approaches,
both in formal presentations, and in informal discussion,

Many thanks in advance,

-David Cohn (cohn at psyche.mit.edu)

====================== begin workshop announcement =====================

         Robot Learning II: Exploration and Continuous Domains
                        A NIPS '93 Workshop
 
                            David Cohn
               Dept. of Brain and Cognitive Sciences
               Massachusetts Institute of Technology
                       Cambridge, MA 02138
                       cohn at psyche.mit.edu
 
The goal of this one-day workshop will be to provide a forum for
researchers active in the area of robot learning and related fields.
Due to the limited time available, we will focus on two major issues:
efficient exploration of a learner's state space, and learning in
continuous domains.
 
Robot learning is characterized by sensor noise, control error,
dynamically changing environments and the opportunity for learning by
experimentation.  A number of approaches, such as Q-learning, have
shown great practical utility learning under these difficult
conditions.  However, these approaches have only been proven to
converge to a solution if all states of a system are visited
infinitely often.  What has yet to be determined is whether we can
efficiently explore a state space so that we can learn without having
to visit every state an infinite number of times, and how we are to
address problems on continuous domains, where there are effectively an
infinite number of states to be visited.
 
This workshop is intended to serve as a followup to last year's
post-NIPS workshop on robot learning. The two problems to be addressed
this year were identified as two (of the many) crucial issues facing
the field.
 
The morning session of the workshop will consist of short
presentations discussing theoretical approaches to exploration and to
learning in continuous domains, followed by general discussion guided
by a moderator. The afternoon session will center on practical and/or
heuristic approaches to these problems in the same format.  As time
permits, we may also attempt to create an updated "Where do we go from
here?"  list, like that drawn up in last year's workshop.
 
The targeted audience for the workshop are those researchers who are
interested in robot learning, exploration, or active learning in
general.  We expect to draw an eclectic audience, so every attempt
will be made to ensure that presentations are accessible to people
without any specific background in the field.