Workshop paper submission deadline moved to July 7
For new dates see here!
Motivation and General Description
The task of constructing composite systems, that is systems composed of more than one part, can be seen as interdisciplinary area which builds on expertise in different domains. The aim of this workshop is to explore the possibilities of constructing such systems with the aid of Machine Learning and exploiting the know-how of Data Mining. One way of producing composite systems is by inducing the constituents and then by putting the individual parts together.
For instance, a text extraction system may be composed of various subsystems, some oriented towards tagging, morphosyntactic analysis or word sense disambiguation. This may be followed by selection of informative attributes and finally generation of the system for the extraction of the relevant information. Machine Learning techniques may be employed in various stages of this process.
The problem of constructing complex systems can thus be seen as a problem of planning to resolve multiple (possibly interacting) tasks. So, one important issue that needs to be addressed is how these multiple learning processes can be coordinated. Each task is resolved using certain ordering of operations. Meta-learning can be useful in this process. It can help us to retrieve previous solutions conceived in the past and re-use them in new settings.
The aim of the workshop is to explore the possibilities of this new area, offer a forum for exchanging ideas and experience concerning the state-of-the art, permit to bring in knowledge gathered in different but related and relevant areas and outline new directions for research.
Lines of research covered by the workshop - Topics of Interest
Of particular interest are methods and proposals that address the following issues:
- Planning to construct composite systems,
- Exploitation of ontologies of tasks and methods,
- Representation of learning goals and states in learning,
- Control and coordination of learning processes,
- Recovering / adapting sequences of DM operations,
- Meta-learning and exploitation of meta-knowledge,
- Layered learning,
- Multi-task learning,
- Transfer learning,
- Multi-predicate learning (and other relevant ILP methods),
- Combining induction and abduction,
- Multi-strategy learning,
- Learning to learn.
Other areas may be covered, provided they are relevant towards the overall aims of the workshop.
The workshop will include at least one invited talk, scientific presentations. Interaction among the participants will be encouraged through a panel discussion.
Larry Hunter, Univ. of Colorado at Denver and Health Sciences Center:
Historical Overview of the area “Planning to Learn”
- Workshop Call for Papers: April 30th, 2007
- Workshop paper submission deadline: moved to July 7
(the original deadline had been June 30)
- Workshop paper acceptance notification: July 28st, 2007
- Workshop paper camera-ready deadline: August 3rd, 2007
- Start of the conference: September 17, 2007
- Provisional Workshop day: September 17, 2007
- The language of the workshop is English.
- Papers should be in PDF format (and in exceptional circumstances in postscript); papers will not be accepted in any other format.
- Papers should be at most 10 pages long.
- All papers should be formatted in the style of the Springer Publications format for Lecture Notes in Computer Science (LNCS).
See http://www.springer.de/comp/lncs/authors.html for detailed instructions of how to prepare your submission.
- All papers should include the names of the authors, their affiliations, their e-mail addresses, and an abstract on their first page.
- All paper need to be submitted at: http://www.easychair.org/PlanLearn07/
- 20 Years of Planning to Learn (Invited talk), Lawrence Hunter
- Meta-Learning Rule Learning Heuristics, Frederik Janssen and Johannes Fürnkranz
- Towards Intelligent Assistance for a Data Mining Process, Avi Bernstein
- An Iterative Process for Building Learning Curves and Predicting Relative Performance of Classifiers, Rui Leite and Pavel Brazdil
- Towards Automating Goal-driven Learning, Maarten van Someren
- Learning to Evaluate Conditional Partial Plans, Slawomir Nowaczyk and Jacek Malec
- Evolutionary Learning with Cross-Class Knowledge Reuse for Handwritten Character Recognition Wojciech Jaskowski, Krzysztof Krawiec, and Bartosz Wieloch
- Designing Complex Systems: Role of Learning and Domain Specific Meta-Knowledge, Pavel Brazdil
- Pavel Brazdil, LIACC, University of Porto, Portugal, Email: pbrazdil [at] liacc.up.pt
- Abraham Bernstein, University of Zurich, Switzerland. Email: click here
- Contact both workshop chairs
- Abraham Bernstein, University of Zurich, Switzerland
- Pavel Brazdil, LIACC, University of Porto, Portugal
- Christophe Giraud-Carrier, Brigham Young University, USA
- Peter Flach, University of Bristol, Great Britain
- Larry Hunter, University of Colorado at Denver and Health Sciences Center, USA
- Rui Leite, LIACC, University of Porto, Portugal
- Katharina Morik, University of Dortmund, Germany
- Oliver Ray, University of Bristol, Great Britain
- Ashwin Ram, Georgia Tech, USA
- Luc de Raedt, University of Leuven, Belgium
- Michele Sebag, LRI, University of Paris-sud, France
- Carlos Soares, LIACC, University of Porto, Portugal
- Maarten van Someren, University of Amsterdam, The Netherlands
- Ricardo Vilalta, University of Houston, USA.
This information can be found at: http://www.ifi.uzh.ch/ddis/planlearn07.html