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- W4231314381 abstract "Constraint programming offers a declarative approach to solving problems modeled as constraint satisfaction problems (CSPs). However, the precise specification of a set of constraints is sometimes not available, but may have to be learned, for instance, from a set of examples of its solutions and non-solutions. In general, one may wish to learn generalized CSPs involving classical, fuzzy, weighted or probabilistic constraints, for example. This paper introduces a unifying framework for CSP learning. The framework is generic in that it can be instantiated to obtain specific formulations for learning classical, fuzzy, weighted or probabilistic CSPs. In particular, a new formulation for classical CSP learning, which minimizes the number of examples violated by candidate CSPs, is obtained by instantiating the framework. This formulation is equivalent to a simple pseudo-boolean optimization problem, thus being efficiently solvable using many optimization tools." @default.
- W4231314381 created "2022-05-12" @default.
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- W4231314381 date "2007-10-01" @default.
- W4231314381 modified "2023-09-25" @default.
- W4231314381 title "Semiring-Based Constraint Acquisition" @default.
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- W4231314381 doi "https://doi.org/10.1109/ictai.2007.160" @default.
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