***** Many apologies for multiple postings *****
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CALL FOR PAPERS
Workshop on ARTIFICIAL INTELLIGENCE PLANNING and LEARNING (AIPL-07)
Providence, Rhode Island, September 22, 2007
(in conjunction with the International Conference on Automated
Planning and Scheduling (ICAPS-07))
Workshop Web Site: http://www.cs.umd.edu/users/ukuter/icaps07aipl/
Great strides have been made in automated AI Planning in recent
years, including very efficient planning techniques that use
controlled search with domain-specific and/or domain-independent
heuristics, constraint-satisfaction techniques for reasoning with
time and resources, and model-checking based planning algorithms. The
effectiveness of these techniques has been demonstrated in the past
International Planning Competitions, and to some extent, in real-
world applications such as navigation tasks, space operations,
railway control, and rescue-evacuation tasks.
One challenge for most planning systems is that they require a domain
expert to provide some sort of "planning knowledge" to the system. In
many realistic planning problems, however, such planning knowledge
may not be completely available; this is partly because it is very
hard to compile such knowledge due to the complexities in the
domains, e.g., evacuation and rescue operations, and it is partly
because there is no expert to provide it, e.g., space operations. In
these complex domains, a planning system that can learn such
knowledge to develop ways on how to operate in the world holds great
promise to be successful.
There has been always an interest in research on developing new
techniques in the intersection of AI Planning and Learning. Recently,
the two communities have started to move towards each other in
several venues. The biggest evidence for this trend is the recent
projects initiated by the major funding agencies throughout the
world; e.g., the DARPA Grand Challenge, the Integrated Learning and
Transfer Learning programs at DARPA, and "Dynamic Planning,
Optimization, and Learning" (DPOL) project at NICTA, Australia.
The aim of this workshop is to bring researchers together from the AI
planning and machine learning communities. The tentative topics that
will be covered in the workshop include, but are not limited to:
* Theory of mixing inductive and deductive approaches to planning
* Learning to search (e.g., learning search heuristics).
* Learning for games (e.g., learning game evaluators at end games).
* Generalizing plans across similar domains.
* Function approximation in planning.
* Learning models for planning.
* (Relational) Reinforcement learning and planning.
* Planning heuristics for exploration in reinforcement learning.
* Optimization based approaches to planning.
* Applications of planning and learning (e.g., methods applied in
the past and present DARPA Grand Challenges)
INVITED SPEAKER:
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Pat Langley, Arizona State University, USA
IMPORTANT DATES:
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Paper Submissions: June 15, 2007
Notification of Acceptance/Rejection: July 13, 2007
Camera-Ready Paper Submissions: July 27, 2007
WORKSHOP FORMAT:
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The workshop will take one full day, starting at 8:30am and ending at
5:30pm. We solicit for paper submissions that range from 2 pages to 6
pages in standard AAAI format - please see the formatting
instructions at <http://www.aaai.org/Publications/Author/author.php>.
All appropriate submissions meeting a minimum standard will be asked
to present a poster; selected participants will be invited to give 15
minute overviews unless they express a preference not to. There will
also be time allocated during the workshop for a panel session. We
especially encourage student paper submissions and attendance.
Please see the workshop web site: http://www.cs.umd.edu/users/ukuter/
icaps07aipl/
We will be posting there detailed information on the paper submission
procedures and workshop program.
ORGANIZING COMMITTEE:
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Ugur Kuter, University of Maryland, College Park, USA
Douglas Aberdeen, NICTA, Australia
Olivier Buffet, LAAS, Toulouse, France
Peter Stone, The University of Texas at Austin, USA
PROGRAM COMMITTEE:
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Douglas Aberdeen, NICTA, Australia
Olivier Buffet, LAAS, Toulouse, France
Alan Fern, Oregon State University, USA
Hector Geffner, Universitat Pompeu Fabra, Spain
Robert Givan, Purdue University, USA
Subbaro Kambhampati, Arizona State University, USA
Ugur Kuter, University of Maryland at College Park, USA
Hector Munoz-Avila, Lehigh University, USA
Peter Stone, The University of Texas at Austin, USA
Sylvie Thiebaux, NICTA, Australia
Qiang Yang, Hong Kong University of Science and Technology, Hong Kong
Rong Zhou, PARC, USA
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