From: wshen@nanook.mcc.com (Wei-Min Shen)
Subject: CFP: AAAI93 Workshop on Learning Action Models
Organization: MCC, Austin, TX 78759, U.S.A.
Distribution: comp.ai
Date: Mon, 4 Jan 1993 15:49:05 GMT



	The AAAI-93 Workshop on Learning Actions Models 
                          held at the 
       Eleventh National Conference on Artificial Intelligence


DESCRIPTION OF WORKSHOP:

The goal of this workshop is to develop/communicate technologies that
enable active learning systems, based on their own percepts and actions, to
abstract a model from their environment and incorporate the model into
their actions, thereby improving the long-term performance of the system.

Learning action models has been a fundamental problem in fields such as
adaptive control and system identification.  Recent progress in
reinforcement learning and robot learning shows clearly that the learning
of such models is a very important topic within the AI learning community
as well. It affords an opportunity for high-level representations for
reasoning about the environment to interact usefully with the low-level
action model. It seems the time has come for researchers from different
fields to start working together to surmount the gap between the high-level
cognitive models and the robotic hardware.

The workshop is intended to bring several otherwise separated research
groups together to share recent developments. In particular, we encourage
contributed papers in reinforcement learning, adaptive control, robot
learning, learning to predict, and control-oriented learning neural
networks. A list of specific topics are listed below. Survey papers of
selected field are also welcome.


TOPICS:

The topics of the workshop include, but are not limited to, the following:

Model Representation:

The representation of the model can be critical to the success of learning
and effective using of the model. Examples of representations include State
Machines with $Q$-values, Neuron Networks, Linear/nonlinear Functions, and
Logical and Qualitative prediction rules. Questions related to model
representation include: What are the pros and cons of each representation
with regard to learning, generalization, abstraction, approximation, and
prediction?  How do the models scale? Can you model continuous actions?
How can you balance the tradeoff between detail and generality?  And is
representation even one of the critical issues in designing such learning
systems? (Most would say so, but anti-representationism is growing within
AI.)

The Utility of Models:

When is modeling useful? For many (low-level) control tasks, it would be
impossible or very expensive to learn a complete and accurate model, and it
might be easier to learn to control without learning a model in the first
place.  On the other hand, for many (mostly high-level) tasks, a model is
essential. A related question is how to measure the usefulness of a model.
One choice is to be task-specific, the other may be the readiness of
dealing with new tasks.

Balancing Exploration and Planning:

This problem is better known as the explore/exploit tradeoff, and it is
modeled in the statistics and GA communities by k-armed bandit problems.
Action models enable the agent to "mentally" plan its actions for the
goals. However, since the environment and the goals may change, models
cannot always be perfect and must be revised by exploring.  How to balance
these two activities is a challenging problem.

Discovering Hidden States:

Environments may have states that cannot be perceived directly by the
learner.  To learn an accurate and useful action model, these hidden states
may need to be discovered and utilized in the learned model. Still, there
is the likelihood that the action model will be incomplete. To what extent
can an incomplete model be useful in achieving the agent's goals?

Experiment Design and Learning from Experiments:

Besides exploring the environment more or less randomly, how does the agent
design experiments and learn from them? The design of experiments may be
based on the status of the current model, on deficiencies found while using
the model, on changes in the overall system goals, etc. The problem is
closely related to action selection or active learning. How do the negative
theoretical results---e.g., a theorem stating that neither membership
queries nor equivalence queries alone are sufficient to learn the model
effectively---impact on this practical problem?

Reasoning about the Models:

Techniques that can effective apply the models to the goals of the system,
keeping models responsive to sudden changes in the environment.

Comparison of Learning Methods:

There are quite a few existing methods for learning action models.
Comparison of them may yield inspiration for new methods. Questions related
to this topic include: In what type of environment can a learning method
function? How fast does it converge? Can it handle noise or incomplete
state information? Does it support model abstraction? etc.



FORMAT OF WORKSHOP:

The research papers will be organized by topics and presented sequentially.
Panels and discussion sessions for each topic will be organized after
papers have been accepted.


ATTENDANCE:

The workshop will be attended by authors of accepted papers as well as
researchers that are willing to contribute/participate in the discussions.
All submitted papers will be included in the proceedings distributed to the
participants, among which about 8 to 10 papers will be selected for
presentation. The committee is working actively to publish the papers as
citable ``AAAI Press Technical Reports.'' The workshop lasts one day and
the number of attendees will be no more than forty (40). People who are
interested are invited to submit a summary of their research and
publications and on that basis will be invited to attend.


SUBMISSION REQUIREMENT:

Please send four (4) copies of a short paper or an extended abstract of the
research. Neither abstracts nor papers may exceed five (5) pages in length.
Standard LaTex or pure ASCII text may be sent by email. Hard copy and email
must both arrive by the submission deadline.


SUBMISSION DEADLINE:   March 12, 1993.

NOTIFICATION DATE:  April 2, 1992

FINAL DATE FOR PAPERS:

Camera-ready full papers are due April 30, 1992. Beyond this date, they
may not be published in the working notes.


SUBMIT TO:

Wei-Min Shen
Information System Division
Microelectronics and Computer Technology Corporation
3500 West Balcones Center Drive
Austin, TX 78759
TEL 512-338-3295
FAX 512-338-3890
wshen@mcc.com


WORKSHOP COMMITTEE:

Phil Laird
NASA Ames Research Center 
Moffett Field, CA 94035
laird@ptolemy.arc.nasa.gov

Sridhar Mahadevan
IBM T.J. Watson Research Center, Box 704
Yorktown Heights, NY 10598
sridhar@watson.ibm.com

Wei-Min Shen (Chair)
Microelectronics and Computer Technology Corporation
3500 West Balcones Center Drive
Austin, TX 78759
wshen@mcc.com

Richard Sutton
GTE Laboratories Incorporated 
40 Sylvan Rd. 
Waltham MA 02254 
sutton@gte.com

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