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  • Inductive logic programming (ILP) is a subfield of machine learning which uses first-order logic as a uniform representation for examples, background knowledge and hypotheses (Muggleton and De Raedt, 1994). In this paper we deal with a so called template consistency problem, which is one of essential tasks in ILP (Gottlob et al 1999). In particular, given learning examples and template T, we are looking for a substitution s making Ts consistent with the examples using methods from the field of constraint satisfaction.
  • Inductive logic programming (ILP) is a subfield of machine learning which uses first-order logic as a uniform representation for examples, background knowledge and hypotheses (Muggleton and De Raedt, 1994). In this paper we deal with a so called template consistency problem, which is one of essential tasks in ILP (Gottlob et al 1999). In particular, given learning examples and template T, we are looking for a substitution s making Ts consistent with the examples using methods from the field of constraint satisfaction. (en)
Title
  • Using Constraint Satisfaction for Learning Hypotheses in Inductive Logic Programming
  • Using Constraint Satisfaction for Learning Hypotheses in Inductive Logic Programming (en)
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  • Using Constraint Satisfaction for Learning Hypotheses in Inductive Logic Programming
  • Using Constraint Satisfaction for Learning Hypotheses in Inductive Logic Programming (en)
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  • RIV/68407700:21230/10:00171165!RIV11-GA0-21230___
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  • P(1M0545), P(GA201/08/0509), Z(MSM0021620838)
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  • 294772
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  • RIV/68407700:21230/10:00171165
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  • Inductive logic programming; constraint satisfaction (en)
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  • [196B9358632E]
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  • Daytona Beach, Florida
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  • Menlo Park, California
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  • Proceedings of the Twenty-Third International Florida Artificial Intelligence Research Society Conference
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  • Kuželka, Ondřej
  • Železný, Filip
  • Barták, R.
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  • AAAI Press
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  • 978-1-57735-447-5
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  • 21230
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