Second workshop on COmbining COnstraint solving with MIning and LEarning

(CoCoMiLe)

co-located with AAAI 2013

http://4c.ucc.ie/cocomile/





Organizers: Tias Guns (K.U. Leuven, BE), Lars Kotthoff (University College Cork, IE), Barry O'Sullivan (University College Cork, IE), Andrea Passerini (University of Trento, IT).



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Overview:

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The field of constraint solving has traditionally evolved quite independently from those of machine learning and data mining. In recent years, interest has been growing on the connections between these fields, and the potential advantages of their integration. Integration can work in two ways -- on one hand, various types of constraint solvers can be included in machine learning and data mining algorithms, for example to provide a uniform and effective way to characterize the desired solutions; on the other hand, machine learning can help in addressing constraint satisfaction problems, both at the level of search, by improving search or integrating intelligent meta-heuristics, as well as at the level of modelling, for example by learning constraints or interactively supporting a decision maker.



While promising initial results have been achieved in such directions, many options are unexplored and further research is needed in order to establish a systematic approach to this integration. The best way to reach the full potential of such integrations is in a multi-disciplinary way. This workshop is the second instalment after a successful start co-located with ECAI 2012.



The main purpose of this workshop is to provide an open environment where researchers in machine learning, data mining and constraint solving can exchange ideas and discuss on promising approaches, crucial issues, open problems and interesting formalizations of new tasks.

To encourage this, we will allow three different types of submissions: 1) original contributions (unpublished work), 2) relevant contributions recently submitted or published elsewhere (only oral) and 3) vision statements, works in progress and short overviews.



The following is a non-exclusive list of possible topics:

- data mining/machine learning using constraint solving techniques

- learning with constraints

- constraint-based languages for data mining/machine learning

- preference learning for constraint solving

- automated constraint modeling and solving

- constraint acquisition

- interactive constraint solving

- solver portfolio optimisation

- machine learning in search

- integrating learning and search

- automated parameter optimization / algorithm configuration





In addition to the received contributions, the workshop will include invited talks from prominent researchers working in the intersection between constraint technology, machine learning and data mining. The workshop is planned to end with a broad discussion on the most relevant open problems and research directions.





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Submission:

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We accept the following three types of submissions (using AAAI format, see http://www.aaai.org/Publications/Templates/AuthorKit.zip):

Original novel and unpublished work (max. 6 pages);

An extended abstract of work-in-progress or position statements about future directions, possibilities and limitations (max. 2 pages);

Manuscripts that have recently been accepted for publication or appeared within the last 6 months in a peer-reviewed journal or which are currently under review (only oral, no page limit or format constraints)



Authors should take care that the submitted works are written at the level of the general AI audience, and not geared towards data mining or constraint solving expert specifically.



Submissions will be peer-reviewed by the program committee. All accepted submissions will be published as a AAAI technical report.





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Important Dates:

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3 April 2013: submission deadline (after notification for AAAI and IJCAI)

19 April 2013: acceptance notification

9 May 2013: camera-ready version due

14-15 July 2013: AAAI workshop program





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Contacts:

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Tias Guns (tias.guns@cs.kuleuven.be)

Lars Kotthoff (larsko@4.ucc.ie)

Barry O'Sullivan (b.osullivan@cs.ucc.ie)

Andrea Passerini (passerini@disi.unitn.it)