Co-located with AAAI-13 in Bellevue, Washington, USA
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 the 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.
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 promising approaches, crucial issues, open problems and interesting formalizations of new tasks. To encourage this, we will allow three different types of submissions:
The following is a non-exclusive list of topics of interest:
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.