Inverting implication with small training sets

  • David W. Aha
  • Stephane Lapointe
  • Charles X. Ling
  • Stan Matwin
Regular Papers
Part of the Lecture Notes in Computer Science book series (LNCS, volume 784)

Abstract

We present an algorithm for inducing recursive clauses using inverse implication (rather than inverse resolution) as the underlying generalization method. Our approach applies to a class of logic programs similar to the class of primitive recursive functions. Induction is performed using a small number of positive examples that need not be along the same resolution path. Our algorithm, implemented in a system named CRUSTACEAN, locates matched lists of generating terms that determine the pattern of decomposition exhibited in the (target) recursive clause. Our theoretical analysis defines the class of logic programs for which our approach is complete, described in terms characteristic of other ILP approaches. Our current implementation is considerably faster than previously reported. We present evidence demonstrating that, given randomly selected inputs, increasing the number of positive examples increases accuracy and reduces the number of outputs. We relate our approach to similar recent work on inducing recursive clauses.

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References

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Copyright information

© Springer-Verlag 1994

Authors and Affiliations

  • David W. Aha
    • 1
  • Stephane Lapointe
    • 2
  • Charles X. Ling
    • 3
  • Stan Matwin
    • 4
  1. 1.Naval Research LaboratoryNaval Center for Applied Research in Artificial Intelligence, Code 5514Washington, DCUSA
  2. 2.Defence Research Establishment ValcartierDREVCourceletteCanada
  3. 3.Department of Computer ScienceUniversity of Western OntarioLondonCanada
  4. 4.Department of Computer ScienceUniversity of OttawaCanada

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