Constraint programming and constrained optimization (CPO) as well as machine learning and data mining (MLDM) are well-established research fields within computer science. They have contributed techniques that are routinely applied in real-life scientific and industrial applications.

In recent years awareness has grown that MLDM and CPO are closely related and need to be studied in relationship to each other. An important driving force is here the emergence of large amounts of data in industry and science. Both MLDM and CPO face the challenge of how to maximally exploit data to improve production processes, direct customer behavior, and gain scientific understanding. Only a well-balanced combination of data analysis and constraint optimization can be expected to succeed in this.

Data offers opportunities to constraint optimization in several ways. Currently, practitioners of constraint programming have to formulate explicitly the constraints that underly their application. Data may help here in making modeling decisions, and making better models. This raises the question as to whether it is possible to (semi)-automatically learn constraints, optimization criteria, and their formulations from data and experience.

At the same time, awareness has also grown that constraints and optimization are essential when mining and learning. The MLDM community has been using constraints and optimization to formalize mining and learning problems. Examples are the specification of desirable properties of patterns to be mined or clusters to be learned in constraint-based mining and learning.

Both the MLDM and the CPO community are now faced with the challenge of creating optimization technologies that apply to a wider range of tasks, while also taking into account large amounts of data.

The CPO (and in general the artificial intelligence) community could contribute solvers for broad ranges of constraint-satisfaction and optimization tasks to resolve this challenge. These are studied in in the area of constraint programming and constrained optimization. Machine learning and data mining could benefit from these developments as the goals of CPO and MLDM and constraint-based mining and learning overlap: it is only that CPO targets any type of constraint satisfaction and optimization problem, whereas MLDM specifically targets particular types of such problems.

The MLDM community, on the other hand, could contribute its experience in dealing with large amounts of data, and could contribute its experience in making sense out of data. Insights in how to model data, either using probabilistic models or using rule-based systems, and in how effective search algorithms are currently working, could be useful to the CPO community as its aims to add such models in optimization tasks.

This Dagstuhl seminar will bring the MLDM and CPO communities together to study these challenges. It investigates, on the one hand, how standard CPO techniques can be used in MLDM, and on the other hand, how MLDM can contribute to CPO.

In 2011, a successful Dagstuhl seminar (on "Constraint Programming meets Machine Learning and Data Mining") already brought together these two communities. It succeeded in bringing together key researchers in the field and realized a growing awareness of their potential integration. This seminar aims to consolidate these interests and further investigate the potential. It focuses on two new dimensions. First, while the 2011 Dagstuhl seminar focused on constraint satisfaction, the follow-on seminar will focus more strongly on constrained optimization. Secondly, its focus is on data. How can we effectively use data in CPO? How can we integrate data in CPO if we use CPO for data mining and machine learning? This seminar aims to further our understanding of integrating data, constraints and optimization.

Topics

• Learning constraints from data
• Optimizing solvers based on data
• Using data in CPO solvers for data mining
• Using data in CPO solvers for machine learning
• Integrating data, solvers, mining and learning

• Hendrik Blockeel (KU Leuven, BE) [dblp]
• Jean-François Boulicaut (INSA - Lyon, FR) [dblp]
• Ken Brown (University College Cork, IE) [dblp]
• Bruno Crémilleux (Caen University, FR) [dblp]
• James Cussens (University of York, GB) [dblp]
• Krzysztof Czarnecki (University of Waterloo, CA) [dblp]
• Thi-Bich-Hanh Dao (University of Orleans, FR) [dblp]
• Ian Davidson (University of California - Davis, US) [dblp]
• Luc De Raedt (KU Leuven, BE) [dblp]
• Marc Denecker (KU Leuven, BE) [dblp]
• Yves Deville (University of Louvain, BE) [dblp]
• Alan Frisch (University of York, GB) [dblp]
• Randy Goebel (University of Alberta, CA) [dblp]
• Valerio Grossi (University of Pisa, IT) [dblp]
• Tias Guns (KU Leuven, BE) [dblp]
• Holger H. Hoos (University of British Columbia - Vancouver, CA) [dblp]
• Frank Hutter (Universität Freiburg, DE) [dblp]
• Kristian Kersting (TU Dortmund, DE) [dblp]
• Lars Kotthoff (University College Cork, IE) [dblp]
• Pauli Miettinen (MPI für Informatik - Saarbrücken, DE) [dblp]
• Mirco Nanni (ISTI-CNR - Pisa, IT) [dblp]
• Benjamin Negrevergne (KU Leuven, BE) [dblp]
• Siegfried Nijssen (KU Leuven, BE) [dblp]
• Barry O'Sullivan (University College Cork, IE) [dblp]
• Andrea Passerini (University of Trento, IT) [dblp]
• Francesca Rossi (University of Padova, IT) [dblp]
• Lakhdar Sais (CNRS - Lens, FR) [dblp]
• Vijay A. Saraswat (IBM TJ Watson Research Center - Yorktown Heights, US) [dblp]
• Michele Sebag (University of Paris South XI, FR) [dblp]
• Arno Siebes (Utrecht University, NL) [dblp]
• Guido Tack (Monash University - Caulfield, AU) [dblp]
• Yuzuru Tanaka (Hokkaido University, JP) [dblp]
• Joaquin Vanschoren (TU Eindhoven, NL) [dblp]
• Christel Vrain (University of Orleans, FR) [dblp]
• Toby Walsh (NICTA - Sydney, AU) [dblp]