Results 81 to 90 of about 120 (110)

Possibilistic Inductive Logic Programming

2005
Learning rules with exceptions may be of interest, especially if the exceptions are not important in some sense. Standard Inductive Logic Programming (ILP) algorithms and classical first order logic are not well-suited for managing rules with exceptions.
Mathieu Serrurier, Henri Prade
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Inductive Equivalence of Logic Programs

2005
This paper studies equivalence issues in inductive logic programming. A background theory B1 is inductively equivalent to another background theory B2 if B1 and B2 induce the same hypotheses for any given set of examples. Inductive equivalence is useful to compare inductive capabilities among agents having different background theories.
Chiaki Sakama, Katsumi Inoue
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Induction of Constraint Logic Programs

1996
Inductive Logic Programming is mainly concerned with the problem of learning concept definitions from positive and negative examples of these concepts and background knowledge. Because of complexity problems, the underlying first order language is often restricted to variables, predicates and constants.
Lionel Martin, Christel Vrain
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Inductive logic programming beyond logical implication

1996
This paper discusses the generalization of definite Horn programs beyond the ordering of logical implication. Since the seminal paper on generalization of clauses based on θ subsumption, there are various extensions in this area. Especially in inductive logic programming(ILP), people are using various methods that approximate logical implication, such ...
Jianguo Lu, Jun Arima
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Probabilistic Inductive Logic Programming on the Web

2017
Probabilistic Inductive Logic Programming (PILP) is gaining attention for its capability of modeling complex domains containing uncertain relationships among entities. Among PILP systems, cplint provides inference and learning algorithms competitive with the state of the art. Besides parameter learning, cplint provides one of the few structure learning
RIGUZZI, Fabrizio, ZESE, Riccardo, COTA, Giuseppe   +2 more
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Challenges for Inductive Logic Programming

1999
Inductive logic programming (ILP) is a research area that has its roots in inductive machine learning and logic programming. Computational logic has significantly influenced machine learning through the field of inductive logic programming (ILP) which is concerned with the induction of logic programs from examples and background knowledge.
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Approaches to inductive logic programming

1992
Inductive Logic Programming (ILP) is concerned with construction of logic programs from examples. It shares many concerns of Machine Learning (ML), but is committed to logic. As logic can help to provide a basis for elaborating such a methodology for learning, the area of ILP has attracted a wide attention of many researchers.
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Nonmonotomic Inductive Logic Programming

2001
Nonmonotonic logic programming (NMLP) and inductive logic programming (ILP) are two important extensions of logic programming. The former aims at representing incomplete knowledge and reasoning with commonsense, while the latter targets the problem of inductive construction of a general theory from examples and background knowledge.
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Support Vector Inductive Logic Programming

2005
In this paper we explore a topic which is at the intersection of two areas of Machine Learning: namely Support Vector Machines (SVMs) and Inductive Logic Programming (ILP). We propose a general method for constructing kernels for Support Vector Inductive Logic Programming (SVILP).
Stephen H. Muggleton, Huma Lodhi, Ata Amini, Michael J. E. Sternberg   +3 more
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Logic Programming and Co-inductive Definitions

2000
This paper aims to define a complete semantics for a class of non-terminating logic programs. Standard approaches to deal with this problem consist in concentrating on programs where infinite derivations can be seen as computing, in the limit, some ”infinite object”. This is usually done by extending the domain of computation with infinite elements and
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