Planning and Learning

The 3rd workshop on learning and planning aims to provide a forum for the discussion of issues surrounding the use of learning techniques in planning, continuing the lineage of the events of ICAPS 2007 and 2009. This year, the workshop will be held in parallel to the learning track of the International Planning Competition, and will be a suitable forum to also present the ideas behind the planners running the competition.

Proceedings

The full PAL proceedings are available as pdf. The single papers are also linked in the schedule.

Schedule

This schedule as a PDF file.

The workshop will be held on June 13, 2011 in hall 101–00–026 on the computer science campus.

Accepted Papers

• Matyas Brendel, Marc Schoenauer
  Instance-Based Parameter Tuning and Learning for Evolutionary AI Planning
  (pdf)
• Tomas de la Rosa, Sheila McIlraith
  Learning Domain Control Knowledge for TLPlan and Beyond
  (pdf)
• Chris Fawcett, Malte Helmert, Holger Hoos, Erez Karpas, Gabriele Röger, Jendrik Seipp
  FD-Autotune: Domain-Specific Configuration using Fast Downward
  (pdf)
• Hannaneh Hajishirzi, Eyal Amir
  Planning in Robocup-Soccer Narratives
  (pdf)
• Malte Helmert, Gabriele Röger, Erez Karpas
  Fast Downward Stone Soup: A Baseline for Building Planner Portfolios
  (pdf)
• Srinivas Nedunuri, William R. Cook, Douglas R. Smith
  Cost-Based Learning for Planning
  (pdf)
• Neville Mehta, Prasad Tadepalli, Alan Fern
  Efficient Learning of Action Models for Planning
  (pdf)
• Mauro Vallati, Chris Fawcett, Alfonso E. Gerevini, Holger H. Hoos, Alessandro Saetti
  Generating Fast Domain-Specific Planners by Automatically Configuring a Generic Parameterised Planner
  (pdf)
• Christopher Weber, Daniel Bryce
  Reactive, Proactive, and Passive Learning about Incomplete Actions
  (pdf)
• Richard Cubek, Wolfgang Ertel
  Learning and Application of High-Level Concepts with Conceptual Spaces and PDDL
  (pdf)

Call For Papers

Planning has been defined as the process of thinking before acting, while machine learning has been defined as the process of improving with experience. Although these two areas seem to be quite different, machine learning is actually very useful in all stages of planning, from learning models for planning problems, to learning domain-specific search control, and even online learning during problem solving. This workshop aims to provide a forum for discussing current advances in using learning techniques for all areas of planning.

Automated planners traditionally reason about correct and complete descriptions of planning tasks. These descriptions include models of the actions that can be carried out in the environment together with a specification of the state of the environment and the goals to achieve. In the real-world, actions may result in numerous outcomes, the perception of the state of the environment may be partial and the goals may not be completely defined. Specifying planning tasks from scratch under these conditions becomes complex, even for experts.

Furthermore, despite great progress that had been made in the field of domain independent planning - powerful domain-independent heuristics, useful landmarks analysis or novel propagators for use in a planning-as-CSP framework, to name but a few - hand-crafted domain-specific planners tend to outperform general domain independent planners. The drawback of such guidance is the amount of human effort needed to produce suitable guidance for each domain - the key motivation behind domain-independent approaches.

Machine learning can be used to help with both of these problems. The aim is to eliminate the human bottleneck by automating the process of acquiring domain-specific knowledge (either in the form of a domain model, or as search guidance). In doing so, the system as a whole becomes domain independent once again - the learning system can be used on each domain of interest.

This workshop aims to provide a forum for discussing issues surrounding the use of learning techniques in planning, continuing the lineage of the events of ICAPS 2007 and 2009. The topics that will be covered include, but are not limited to:

• Approaches to learning search guidance
• Approaches to learning of planning models - action modelling, model-lite planning, ...
• Representation of learned knowledge - control rules, heuristics, macro-actions....
• Applying learning to portfolio-based planners
• Hybrid learned-guidance--generic-heuristic search
• Applications of planning and learning
• Learning during planning
• Future Challenges for the IPC Learning Part
• Using machine learning in activity/plan/goal recognition
• The impact of problems sets on what can be learned

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 representation. Additionally, we also welcome descriptions of the systems participating in the learning part of IPC-2011.

Important Dates

• Submission Deadline: March 23, 2011
• Notifications and Technical Program: April 15, 2011
• Workshop Date: June 13th, 2011

Submission Procedure

We ask authors to submit technical papers in PDF format. Papers should be formatted in accordance with the AAAI style template and may be at most 8 pages long, including figures and bibliography. Visit the url http://www.aaai.org/Publications/Author/author.php for formatting instructions.

Please note that all submitted papers will be carefully peer-reviewed by multiple reviewers, and that low-quality or off-topic papers will not be accepted.

Papers can be submitted via e-mail or made available on the Web. In either case, documents should be in gzipped PDF format and be named "author.pdf.gz", using the name of the first author. An e-mail message containing either the file or its URL (e.g. http://..../author.pdf.gz), with subject "Learning and Planning Workshop Submission", should be sent to all members of the organizing committee by the submission deadline.

Organizing Committee

• Sergio Jiménez Celorrio
  Planning and Learning Group
  Computer Science Department
  Universidad Carlos III de Madrid
  Avda de la Universidad, 30
  28911-Leganés, Madrid
  Spain
  sjimenez@inf.uc3m.es
• Erez Karpas
  Faculty of Industrial Engineering and Management
  Technion - Israel Institute of Technology
  Technion City, Haifa 32000
  Israel
  karpase@technion.ac.il
• Subbarao Kambhampati
  Dept. of Computer Science & Engineering
  Fulton School of Engineering
  Arizona State University, Tempe Arizona 85287-5406
  USA
  rao@asu.edu

Program Committee

• Daniel Borrajo, Universidad Carlos III de Madrid
• Alan Fern, Oregon State University
• Alfonso Gerevini, Università degli Studi di Brescia
• Bob Givan, Purdue University
• Hakim Newton, NICTA
• Adele Howe, Colorado State University
• Roni Khardon, Tufts University
• Shaul Markovitch, Technion
• Lee McCluskey, University of Huddersfield
• Ioannis Refanidis, University of Macedonia
• Scott Sanner, NICTA and ANU
• Prasad Tadepalli, Oregon State University
• Jia-Hong Wu
• Sungwook Yoon, PARC
• Shlomo Zilberstein, University of Massachusetts, Amherst