Foundations of Inductive Logic Programming
Nienhuys-Cheng, Shan-Hwei.
Foundations of Inductive Logic Programming [electronic resource] / by Shan-Hwei Nienhuys-Cheng, Ronald de Wolf. - 1st ed. 1997. - XVIII, 410 p. online resource. - Lecture Notes in Artificial Intelligence, 1228 2945-9141 ; . - Lecture Notes in Artificial Intelligence, 1228 .
Propositional 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.
Inductive 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.
9783540690498
10.1007/3-540-62927-0 doi
Software engineering.
Artificial intelligence.
Machine theory.
Computer programming.
Software Engineering.
Artificial Intelligence.
Formal Languages and Automata Theory.
Programming Techniques.
QA76.758
005.1
Foundations of Inductive Logic Programming [electronic resource] / by Shan-Hwei Nienhuys-Cheng, Ronald de Wolf. - 1st ed. 1997. - XVIII, 410 p. online resource. - Lecture Notes in Artificial Intelligence, 1228 2945-9141 ; . - Lecture Notes in Artificial Intelligence, 1228 .
Propositional 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.
Inductive 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.
9783540690498
10.1007/3-540-62927-0 doi
Software engineering.
Artificial intelligence.
Machine theory.
Computer programming.
Software Engineering.
Artificial Intelligence.
Formal Languages and Automata Theory.
Programming Techniques.
QA76.758
005.1