Combining Constraint Solving with Mining and Learning
Papers from the 2013 AAAI Workshop
Tias Guns, Lars Kotthoff, Barry O'Sullivan, Andrea Passerini, Workshop Cochairs
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 multidisciplinary way. This workshop is the second instalment after a successful start colocated with the 2012 European Conference on AI.
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 To encourage this, we will allow three different types of submissions: (1) original contributions (unpublished work), (2) relevant contributions recently submitted or published elsewhere (only oral) and (3) vision statements, works in progress and short overviews.