Combining Constraint Solving with Business Rules and Machine Learning – CoCoMiLe 2013

The integration of different decision making techniques finally is finding its home under the roof of the Decision Management movement. I am glad that an integrated Constraint Programming (CP), Business Rules (BR), and Machine Learning (ML) approach is gaining in popularity as well. An interesting workshop “CoCoMiLe 2013 – COmbining COnstraint solving with MIning and LEarning” will be held on July 14 in Bellevue, WA, USA. It is organized by my former colleagues from  This event will be co-located with AAAI-13. Here is what its summary says:

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.

Very interesting! I am not sure if I’d be able to attend the workshop, but probably now is a good time to refresh some of my old thoughts about the topic. Here is a possible integration scheme for a real-time search of an optimal decision:


This scheme is taken from an old presentation made together with Prof. Gene Freuder in 2008. Typical examples of real-world applications include travel package reservation or field service scheduling. See also – last 5 slides. Since then we applied a similar approach at a major government agency to diagnose non-compliance activities. I expect to see more CP+BR+ML examples in the nearest future.

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