Hardness Results for Learning First-Order Representations and Programming by Demonstration

@article{Cohen1998HardnessRF,
  title={Hardness Results for Learning First-Order Representations and Programming by Demonstration},
  author={William W. Cohen},
  journal={Machine Learning},
  year={1998},
  volume={30},
  pages={57-87}
}
Learning from “structured examples” is necessary in a number of settings, including inductive logic programming. Here we analyze a simple learning problem in which examples have non-trivial structure: specifically, a learning problem in which concepts are strings over a fixed alphabet, examples are deterministic finite automata (DFAs), and a string represents the set of all DFAs that accept it. We show that solving this “dual” DFA learning problem is hard, under cryptographic assumptions. This… CONTINUE READING

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