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About 1,503 papers for “Inductive logic programming”

Inductive Logic Programming

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

Cited by 4Open 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 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

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

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

Constraint Inductive Logic Programming

Michèle Sébag, C. Rouveirol · 1996

. This paper is concerned with learning from positive and negative examples expressed in first-order logic with numerical constants. The presented approach is based on the cooperation of Inductive Logic Programming (ILP) and Constraint Logic Programming (CLP), and proceeds as follows: ffl A discriminant induction problem is shown to be equivalent to a Constraint Satisfaction Problem (CSP): all constrained clauses covering positive examples and rejecting negative examples can be trivially derived from the solutions of this CSP. ffl Solving this CSP then allows to build the G set of solutions in terms of Version Spaces; this resolution can be delegated to a constraint solver. ffl This CSP provides a tractable computational characterization of G, which is sufficient to classify further examples and offers simple countingbased heuristics to resist noisy data. In this hybrid ILP-CLP approach, CLP performs most of the search involved in inductive learning; the advantage is to benefit fro...

Cited by 17

INDUCTIVE EQUATIONAL LOGIC PROGRAMMING

McDonald, Arthur A · Digital Commons - URI (University of Rhode Island) · 2018

Inductive Logic Programming (ILP) is an area of research that is at the intersection of Machine Learning and Logic Programming. An ILP system uses positive and negative facts (examples) and optional background knowledge to induce a logic program that 1) accurately describes the facts and 2) successfully predicts the outcome of unseen examples.\nThis thesis introduces a new ILP algorithm implemented in Equational Logic that takes a hybrid approach to induction, using bottom-up generalization combined with inverse narrowing to create recursive equations.\nWe also introduce a framework for the induction of conditional equations from positive ground examples.

Cited by 0Open access

INDUCTIVE EQUATIONAL LOGIC PROGRAMMING

A. B. McDonald · 2018

Inductive Logic Programming (ILP) is an area of research that is at the intersection of Machine Learning and Logic Programming. An ILP system uses positive and negative facts (examples) and optional background knowledge to induce a logic program that 1) accurately describes the facts and 2) successfully predicts the outcome of unseen examples. This thesis introduces a new ILP algorithm implemented in Equational Logic that takes a hybrid approach to induction, using bottom-up generalization combined with inverse narrowing to create recursive equations. We also introduce a framework for the induction of conditional equations from positive ground examples.

Cited by 0Open access