Research in domain-independent planning aims to produce model-based solvers
able to address different classes of problems. Great progress has been made
in the field, leading to successful techniques such as domain-independent
heuristics, landmarks analysis or useful compilations of planning tasks.
Nevertheless, despite the generality and elegance of current planners, their
performance is conditioned by the amount of expert knowledge invested in the
specification of their models.

Planners using domain-specific knowledge tend to outperform general domain-
independent planners. However, specifying good parameter configurations or
effective search guidance knowledge is a challenging task. So is the task of
creating input problem formulations that are correct, complete, and allow an
efficient search process. In real world applications, planning actions may
result in multiple outcomes, the perception of the state of the environment
may be partial and/or the goals of the planning task may not be completely
defined.

Machine learning is a useful tool to enhance the performance of planners and to
address the model acquisition bottleneck. This workshop aims to provide a forum
for discussing issues surrounding the use of learning techniques in planning,
continuing the lineage of the events held at ICAPS in 2007, 2009 and 2011. The
topics that will be covered include, but are not limited to:

* Offline and online approaches to learning planning search guidance
* Approaches to learning planning models and to planning with the learned models
* Representation of learned knowledge - control rules, heuristics, macro-actions,
hierarchies,...
* Automatic configuration of portfolio-based and auto-tuned planners
* Machine learning for activity/plan/goal recognition
* The impact of problems sets on what can be learned
* Future challenges and roadmap for the Learning Part of the IPC

We invite contributions from researchers who have considered the application of
learning to planning. We also welcome theoretical contributions considering the
expressive power and/or limitations of various forms of learned knowledge.


Paper submission and review:
----------------------------
Paper submissions should be made through the workshop EasyChair web site
https://www.easychair.org/conferences/?conf=icapswpal2013

Paper submission is in PDF only. Please format submissions in AAAI style.
Refer to the author instructions on the AAAI web site for detailed formatting
instructions and LaTeX style files (http://www.aaai.org/Publications/Author/author.php).
Final papers will be in the same format, keep them to at most 8+1 pages long (meaning
8 pages plus 1 extra page containing only references). Please note that all submitted
papers will be peer-reviewed, and that low-quality or off-topic papers will not be accepted.
Also note that all workshop participants must register for the main ICAPS 2013 conference
and that at least one author of each accepted paper must attend the workshop.


Important Dates:
----------------
* Paper Submission: March 29th
* Notification: April 19th
* Camera-Ready Submission: April 30th
* Workshop: June 10-11th


Organizers:
-----------
* Sergio Jiménez, Universidad Carlos III de Madrid, SPAIN
* Adi Botea, IBM Research, IRELAND
* Erez Karpas, Technion, ISRAEL


Program Committee:
------------------
* Amanda Coles, King's College London, UK
* Andrew Coles, King's College London, UK
* Alan Fern, Oregon State University, USA
* Raquel Fuentetaja, Universidad Carlos III de Madrid, SPAIN
* Alfonso Gerevini, University of Brescia, ITALY
* Robert Givan, Purdue University, USA
* Shaul Markovitch, Technion – Israel Institute of Technology, ISRAEL
* Lee Mccluskey, University of Huddersfield, UK
* Ioannis Refanidis, University of Macedonia, GREECE
* Tomás de la Rosa, Universidad Carlos III de Madrid, SPAIN
* Scott Sanner, NICTA and ANU, AUSTRALIA
* Ivan Serina, Free University of Bozen-Bolzano, ITALY
* Prasad Tadepalli, Oregon State University, USA
* Sungwook Yoon, PARC, USA