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Applications of inductive logic programming

Communications of the ACM, 1995
Techniques of machine learning have been successfully applied to various problems [1, 12]. Most of these applications rely on attribute-based learning, exemplified by the induction of decision trees as in the program C4.5 [20]. Broadly speaking, attribute-based learning also includes such approaches to learning as neural networks and nearest neighbor ...
Stephen Muggleton, Ivan Bratko
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Inductive logic programming and learnability

ACM SIGART Bulletin, 1994
The paper gives an overview of theoretical results in the rapidly growing field of inductive logic programming (ILP). The ILP learning situation (generality model, background knowledge, examples, hypotheses) is formally characterized and various restrictions of it are discussed in the light of their impact on learnability.
Jörg-Uwe Kietz, Sašo Džeroski
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Grammar Induction as Substructural Inductive Logic Programming

2000
In this chapter we describe an approach to grammar induction based on categorial grammars: the EMILE algorithm. Categorial grammars are equivalent to context-free grammars. They were introduced by Ajduciewicz and formalised by Lambek. Technically they can be seen as a variant of the propositional calculus without structural rules.
Erik de Haas, Pieter Adriaans
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Cautious induction in inductive logic programming

1997
Many top-down Inductive Logic Programming systems use a greedy, covering approach to construct hypotheses. This paper presents an alternative, cautious approach, known as cautious induction. We conjecture that cautious induction can allow better hypotheses to be found, with respect to some hypothesis quality criteria.
Simon Anthony, Alan M. Frisch
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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.
Katsumi Inoue, Chiaki Sakama
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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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An Introduction to Inductive Logic Programming

2001
Inductive logic programming (ILP) is concerned with the development of techniques and tools for relational data mining. Besides the ability to deal with data stored in multiple tables, ILP systems are usually able to take into account generally valid background (domain) knowledge in the form of a logic program.
Nada Lavrač, Sašo Džeroski
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Nonmonotomic Inductive Logic Programming [PDF]

, 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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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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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.
Christel Vrain, Lionel Martin
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