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010   $a3-540-69049-2
024 7 $a10.1007/3-540-62927-0
035   $a(CKB)1000000000234637
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035   $a(PQKBTitleCode)TC0000323262
035   $a(PQKBWorkID)10300111
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035   $a(DE-He213)978-3-540-69049-8
035   $a(PPN)155200240
035   $a(EXLCZ)991000000000234637
100   $a20121227d1997      u|             0
101 0 $aeng
135   $aurnn|008mamaa
181   $ctxt
182   $cc
183   $acr
200 10$aFoundations of Inductive Logic Programming$b[electronic resource] /$fby Shan-Hwei Nienhuys-Cheng, Ronald de Wolf
205   $a1st ed. 1997.
210  1$aBerlin, Heidelberg :$cSpringer Berlin Heidelberg :$cImprint: Springer,$d1997.
215   $a1 online resource (XVIII, 410 p.) 
225 1 $aLecture Notes in Artificial Intelligence ;$v1228
300   $aBibliographic Level Mode of Issuance: Monograph
311   $a3-540-62927-0 
327   $aPropositional logic -- First-order logic -- Normal forms and Herbrand models -- Resolution -- Subsumption theorem and refutation completeness -- Linear and input resolution -- SLD-resolution -- SLDNF-resolution -- What is inductive logic programming? -- The framework for model inference -- Inverse resolution -- Unfolding -- The lattice and cover structure of atoms -- The subsumption order -- The implication order -- Background knowledge -- Refinement operators -- PAC learning -- Further topics.
330   $aInductive Logic Programming is a young and rapidly growing field combining machine learning and logic programming. This self-contained tutorial is the first theoretical introduction to ILP; it provides the reader with a rigorous and sufficiently broad basis for future research in the area. In the first part, a thorough treatment of first-order logic, resolution-based theorem proving, and logic programming is given. The second part introduces the main concepts of ILP and systematically develops the most important results on model inference, inverse resolution, unfolding, refinement operators, least generalizations, and ways to deal with background knowledge. Furthermore, the authors give an overview of PAC learning results in ILP and of some of the most relevant implemented systems.
410  0$aLecture Notes in Artificial Intelligence ;$v1228
606   $aSoftware engineering
606   $aArtificial intelligence
606   $aMathematical logic
606   $aComputer programming
606   $aSoftware Engineering/Programming and Operating Systems$3https://scigraph.springernature.com/ontologies/product-market-codes/I14002
606   $aArtificial Intelligence$3https://scigraph.springernature.com/ontologies/product-market-codes/I21000
606   $aMathematical Logic and Formal Languages$3https://scigraph.springernature.com/ontologies/product-market-codes/I16048
606   $aProgramming Techniques$3https://scigraph.springernature.com/ontologies/product-market-codes/I14010
615  0$aSoftware engineering.
615  0$aArtificial intelligence.
615  0$aMathematical logic.
615  0$aComputer programming.
615 14$aSoftware Engineering/Programming and Operating Systems.
615 24$aArtificial Intelligence.
615 24$aMathematical Logic and Formal Languages.
615 24$aProgramming Techniques.
676   $a005.1/15
700   $aNienhuys-Cheng$b Shan-Hwei$4aut$4http://id.loc.gov/vocabulary/relators/aut$0743205
702   $aWolf$b Ronald de$4aut$4http://id.loc.gov/vocabulary/relators/aut
906   $aBOOK
912   $a9910144922803321
996   $aFoundations of inductive logic programming$91478054
997   $aUNINA