Industry and society are increasingly automating processes, which requires solving combinatorial optimisation problems. To find not just optimal solutions, but also 'desirable' solutions for the end user, it is increasingly important to offer AI tools that automatically learn from the user and the environment and that support the constraint modelling in interpretable ways.
In this talk I will provide an overview of three different ways in which AI can augment the modeling part of combinatorial optimisation. This includes learning from the user (preference learning in VRP), learning from the environment (end-to-end decision focussed learning) and explanation generation, that sit at the intersection of learning and reasoning. As part of this work, we are building a modern constraint programming language called CPMpy(http://cpmpy.readthedocs.io) that eases integration of multiple constraint solving paradigms with machine learning and other scientific python libraries. I will shortly highlight its possibilities beyond the above cases, as well as our larger vision of conversational human-aware technology for optimisation.
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