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The inductive learning and logic programming sides of ILP

  • From inductive machine learning, ILP inherits its goal: to develop tools and techniques to
    • Induce hypotheses from observations (examples)
    • Synthesise new knowledge from experience
  • By using computational logic as the representational mechanism for hypotheses and observations, ILP can overcome the two main limitations of classical machine learning techniques:
    • The use of a limited knowledge representation formalism (essentially a propositional logic)
    • Difficulties in using substantial background knowledge in the learning process

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