Hybrid SRL with Optimization Modulo Theories

@article{Teso2014HybridSW,
  title={Hybrid SRL with Optimization Modulo Theories},
  author={Stefano Teso and Roberto Sebastiani and Andrea Passerini},
  journal={CoRR},
  year={2014},
  volume={abs/1402.4354}
}
Generally speaking, the goal of constructive learning could be seen as, given an example set of structured objects, to generate novel objects with similar properties. From a statistical-relational learning (SRL) viewpoint, the task can be interpreted as a constraint satisfaction problem, i.e. the generated objects must obey a set of soft constraints, whose weights are estimated from the data. Traditional SRL approaches rely on (finite) First-Order Logic (FOL) as a description language, and on… CONTINUE READING
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