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A Model Seeker: Extracting Global Constraint Models From Positive Examples
"... Abstract. We describe a system which generates finite domain constraint models from positive example solutions, for highly structured problems. The system is based on the global constraint catalog, providing the library of constraints that can be used in modeling, and the Constraint Seeker tool, whi ..."
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Abstract. We describe a system which generates finite domain constraint models from positive example solutions, for highly structured problems. The system is based on the global constraint catalog, providing the library of constraints that can be used in modeling, and the Constraint Seeker tool, which finds a ranked list of matching constraints given one or more sample call patterns. We have tested the modeler with 230 examples, ranging from 4 to 6,500 variables, using between 1 and 7,000 samples. These examples come from a variety of domains, including puzzles, sportsscheduling, packing & placement, and design theory. When comparing against manually specified “canonical ” models for the examples, we achieve a hit rate of 50%, processing the complete benchmark set in less than one hour on a laptop. Surprisingly, in many cases the system finds usable candidate lists even when working with a single, positive example. 1
Using the Global Constraint Seeker for Learning Structured Constraint Models: a First Attempt
"... Abstract. Considering problems that have a strong internal structure, this paper shows how to generate constraint models from a set of positive, flat samples (i.e., solutions) without knowing a priori neither the constraint candidates, nor the way variables are shared within constraints. We describe ..."
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Abstract. Considering problems that have a strong internal structure, this paper shows how to generate constraint models from a set of positive, flat samples (i.e., solutions) without knowing a priori neither the constraint candidates, nor the way variables are shared within constraints. We describe two key contributions to building such a model generator: (1) First, learning is modeled as a bicriteria optimization problem over ranked constraint candidates returned by the Constraint Seeker, where we optimize both the compactness of the model, and the rank (or appropriateness) of the selected constraints. (2) Second, filtering out irrelevant candidate models is achieved by using meta data of the global constraint catalog that describe links between constraints. Some initial experiments on a proofofconcept implementation show promising results. 1 Scope and Hypothesis Global constraints were initially introduced [3] in order to more efficiently handle the filtering associated with some recurring structured constraint networks [1]. An inherent disadvantage of the approach is that the introduction of global constraints does not