******* Our apologies if you received this announcement multiple
times ********
CALL FOR
PAPERS
StrucK-09: The IJCAI-09 Workshop on Learning
Structural Knowledge from Observations
Pasadena,
California, USA
July 12,
2009,
http://www.cs.umd.edu/users/ukuter/struck09/
to be held in
conjunction with
The 21st International Joint Conference on
Artificial Intelligence (IJCAI-09)
** Workshop Description:
Human cognition organizes knowledge in different complexity levels:
higher-level knowledge is formed by first acquiring simple concepts,
which are then combined to learn complex ones. As a result, many
cognitive architectures use structural models to represent relations
between knowledge of different complexity. Structural modeling has led
to a number of representation and reasoning formalisms including
frames, schemas, abstractions, hierarchical task networks (HTNs), and
goal graphs among others. These formalisms have in common the use of
certain kinds of constructs (e.g., objects, goals, skills, and tasks)
that represent knowledge of varying degrees of complexity and that are
connected through structural relations.
In recent years, we have observed increasing interest towards the
problem of learning such structural knowledge from observations. These
observations range from traces generated by an automated planner to
video feeds from a robot performing some actions. Researchers have
been addressing instances of this problem from different perspectives
in a variety of research communities, among others including
-- Machine Learning (including inductive logic programming (ILP))
-- Automated Planning
-- Case-Based Reasoning
-- Cognitive Science
We believe that the time is ripe to get together researchers from
these and other communities that are looking into instances of this
problem and share ideas and perspectives in a common forum. Potential
focus topics include but are not limited to:
- Cognitive architectures and learning techniques such as ILP,
explanation-based learning (EBL), abstraction, generalization, and
teleoreactive logic programs
- Formalisms for goal-directed behavior, including hierarchical task
networks, skill hierarchies, goal networks,
and annotated goal hierarchies
- Learning behavior from observations over time
- Observations ranging from fully to partially observable inputs and
from annotated to un-annotated action traces
- Trade-offs between task performance and structural learning
- Learning meta-level knowledge (i.e., how to choose among different
reasoning/problem-solving functionalities, how to manage the trade-
offs between task performance and learning)
- Probabilistic and other extensions to structural knowledge to
represent uncertainty
- Representing and learning continuous information
- Interacting with the external environment during structural learning
(i.e., information-gathering, execution, etc)
- Learning structural information/data flow from observations
** Important Dates
Paper Submission: March 6, 2009
Notifications of Acceptance/Rejection: April 17, 2009
Camera-Ready Papers: May 8, 2009
Announcement of the Workshop Program: May 22, 2009
Workshop Date: July 12, 2009
** Paper submission instructions:
Paper submissions must be formatted in IJCAI style (see: http://ijcai-09.org/fcfp.html
for instructions and to download file templates). We solicit paper
ranging from 2 pages (extended abstracts) to 6 pages (full papers).
The submission procedure(s) will be announced at the workshop Web
site; please visit http://www.cs.umd.edu/users/ukuter/struck09/.
** Paper presentation:
All accepted papers will be asked to present a poster; selected
participants will be invited to give 15 minute overviews unless they
express a preference not to.
** Workshop Program:
The workshop will interleave short presentations, a poster session,
two or more discussion groups, and a joint open discussion. The
workshop is aimed to identifying and discussing specific questions
that are still open and/or that are still prone to further
understanding and research in order to develop efficient and
intelligent systems. To achieve this objective, we plan organize break-
out working groups during the course of the workshop. Each break-out
group will focus on one specific topic in Learning Structural
Knowledge from Observations. The participants of each group will be
identified from on the workshop participants and their interests/
willingness. Similarly, the final set of questions will be determined
based on the accepted papers and abstracts and the audience of the
workshop.
Please see the workshop Web site (http://www.cs.umd.edu/users/ukuter/struck09/
) for the announcements and updates on the workshop program.
** Program Committee
- Ralph Bergmann (University of Trier, Germany)
- Daniel Borrajo (Universidad Carlos III de Madrid, Spain)
- Adi Botea (NICTA, Australia)
- Mark Burstein (BBN Technologies, USA)
- Maria Fox (University of Strathclyde, UK)
- Tolga Konik (ISLE, Stanford University, USA)
- Ugur Kuter (University of Maryland, College Park, USA)
- John Levine (University of Strathclyde, UK)
- Clayton T. Morrison (University of Arizona, USA)
- Hector Munoz-Avila (Lehigh University, USA)
- Karen Myers (SRI International, USA)
- Tim Oates (University of Maryland, Baltimore County, USA)
- Enric Plaza (Artificial Intelligence Research Institute, Spain)
- Manuela M. Veloso (Carnegie-Mellon University, USA)
- Fusun Yaman (BBN Technologies, USA)
- Qiang Yang (University of Hong Kong, China)
** Organizing Committee:
Ugur Kuter (University of Maryland, College Park, USA)
Hector Munoz-Avila (Lehigh University, USA)
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========================================================================
Dr. Ugur Kuter
Assistant Research Scientist
Institute for Advanced Computer Studies
University of Maryland, College Park
Maryland 20742 USA
[email protected]
phone: +1 (301) 405-5933, fax: +1 (301) 405-6707
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