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Applications of inductive logic programming
Communications of the ACM, 1995Techniques 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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Cautious induction in inductive logic programming
1997Many 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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Possibilistic Inductive Logic Programming
2005Learning 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.
M. Serrurier, Henri Prade
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Inductive Equivalence of Logic Programs
2005This 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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A Perspective on Inductive Logic Programming
1999The state-of-the-art in inductive logic programming is surveyed by analyzing the approach taken by this field over the past 8 years. The analysis investigates the roles of 1) logic programming and machine learning, 2) theory, techniques and applications, and 3) various technical problems addressed within inductive logic programming.
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Special issue on inductive logic programming
New Generation Computing, 1995The articles of this volume will be reviewed individually in CompuScience (database).
Stephen Muggleton +2 more
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Logic Programming and Co-inductive Definitions
2000This 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
2001Inductive 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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Induction of Constraint Logic Programs
1996Inductive 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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