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About 2,661 papers for “Inductive programming”

Inductive Programming

Pierre Flener, Ute Schmid · Encyclopedia of Machine Learning and Data Mining · 2017

Cited by 1

Incremental Inductive Programming

Robert Henderson · 2008

Inductive Programming (IP) refers to the automated generation of computer programs from incomplete specifications, such as input-output examples. We introduce a new technique called Incremental Inductive Programming (IIP), which combines IP with transfer learning, as a way to get IP to scale up to more complex problems. Using a simple, brute-force search based IP system implemented for the purpose, we perform a set of experiments comparing the performance of IIP versus standard non-incremental IP. IIP

Cited by 0

Inductive Logic Programming

Riguzzi, Fabrizio, Bellodi, Elena, Zese, Riccardo · Lecture notes in computer science · 2018

Cited by 4Open access

Inductive Logic Programming

Bloomsbury Academic eBooks · 2003

Inductive logic programming is the subfield of machine learning that uses first order logic to represent hypotheses and data. Because first order logic is expressive and declarative, inductive logic programming specfically targets problems involving structured data and background knowledge. Inductive logic programming tackles a wide variety of problems in machine learning, including classification, regression, clustering, and reinforcement learning, often using “upgrades” of existing propositional machine learning systems, It relies on logic for knowledge representation and reasoning purposes. Notions of coverage, generality, and operators for traversing the space of hypotheses are grounded in logic, see also the logic of generality. Inductive logic programming systems have been applied to important applications in bioand chemo-informatics, natural language processing, and web mining.

Cited by 4

Inductive Logic Programming

Stephen H. Muggleton, Alireza Tamaddoni-Nezhad · Lecture notes in computer science · 2024

Cited by 1Open access

Inductive Logic Programming

Verbeke, Mathias, David A. Bohan, Van Asch, Vincent et al. · Lecture notes in computer science · 2012

Cited by 5Open access

Inductive Logic Programming

Elena Bellodi, Francesca A. Lisi, Riccardo Zese · Lecture notes in computer science · 2023

Cited by 14Open access

Inductive logic programming

Wrobel, S. · Publikationsdatenbank der Fraunhofer-Gesellschaft (Fraunhofer-Gesellschaft) · 1996

S.153-189

Cited by 0

Inductive Constraint Programming

Michèle Sebag, Céline Rouveirol, Jean-François Puget · 2007

Inductive Logic Programming (ILP) is a subfield of both Machine Learning and Logic Programming (LP). It uses the latter as a representation language for the hypotheses to be learned. However, the application of ILP to problems involving numbers has shown the need for some background knowledge encoding the basic numerical relations such as "less than". Our thesis is that a radically different approach should be preferred: since Constraint Logic Programming (CLP) contains the extensions of LP developed in the past 10 years for handling numerical variables, one should rather choose CLP as the representation language of hypotheses. This paper is devoted to learning discriminant constrained clauses from positive and negative examples expressed as constrained clauses. A first step, termed small induction, gives a computational characterization of discriminant constrained clauses which is sufficient to classify further instances of the problem domain. A second step, termed exhaustiv...

Cited by 0

Inductive logic programming

Stephen Muggleton · ACM SIGART Bulletin · 1994

Inductive Logic Programming (ILP) is a research area which investigates the construction of first-order definite clause theories from examples and background knowledge. ILP systems have been applied successfully in a number of real-world domains. These include the learning of structure-activity rules for drug design, finite-element mesh design rules, rules for primary-secondary prediction of protein structure and fault diagnosis rules for satellites. There is a well established tradition of learning-in-the-limit results in ILP. Recently some results within Valiant's PAC-learning framework have also been demonstrated for ILP systems. In this paper it is argued that algorithms can be directly derived from the formal specifications of ILP. This provides a common basis for Inverse Resolution, Explanation-Based Learning, Abduction and Relative Least General Generalisation. A new general-purpose, efficient approach to predicate invention is demonstrated. ILP is underconstrained by its logical specification. Therefore a brief overview of extra-logical constraints used in ILP systems is given. Some present limitations and research directions for the field are identified.

Cited by 70Open access

Inductive Metalogic Programming

Andreas Hamfelt, Jørgen Fischer Nilsson · 1994

We propose a metalogic programming method for efficient induction of a fairly large class of list-handling logic programs delineated through restrictions on the hypothesis language. These restrictions take the form of predefined program recursion schemes (higher order "clichés") from which the hypotheses programs in the induction process are derived by plugging in either simple, nonrecursive clause programs or invented predicates. Metalogic programming is applied for handling clauses as first class data objects in connection with program schemes, and moreover for obtaining a flexible control scheme during the induction, avoiding a blindfold generate-and-test against the given program examples. A metalogic program induction testbed has been constructed and has successfully been applied to some induction problems discussed in the literature.

Cited by 0

Programming with Inductive and Co-Inductive Types

John Greiner · 1992

We look at programming with inductive and co-inductive datatypes, which are inspired theoretically by initial algebras and final co-algebras, respectively. A predicative calculus which incorporates these datatypes as primitive constructs is presented. This calculus allows reduction sequences which are significantly more efficient for two dual classes of common programs than do previous calculi using similar primitives. Several techniques for programming in this calculus are illustrated with numerous examples. A short survey of related work is also included.

Cited by 19

Stochastic Inductive Logic Programming

Ivan Bratko, Matevz Kovacic, Matevz Kovacic · 1994

Machine learning is an important part of artificial intelligence and its applications. Learning from instances is one of the most active areas within machine learning. Initial successes in the induction of propositional theories have been followed by algorithms that construct hypotheses in the form of (a subset of) the first order relational concepts. Such learning is called Inductive Logic Programming (ILP). This thesis deals with two key problems of machine learning of concepts from instances: hypothesis justification and hypothesis construction which are also a vital part of the form of reasoning called inductive inference. The purpose of concept formation is information compression (a hypothesis describes or "explains" given learning instances). Generally, there is not a single hypothesis that explains the given instances of the target concept. One criterion for hypothesis justification is based on the Minimum description length (MDL) principle which states that a hypothesis which...

Cited by 0