We use a lexicographical preference order on the problem space to combine solution synthesis with conflict learning. Given two preferred solutions of two subproblems, we can either combine them to a solution of the whole problem or learn a ‘fat’ conflict which cuts off a whole subtree. The approach makes conflict learning more pervasive for Constraint Programming as it well exploits efficient support finding and compact representations of Craig interpolants.
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