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About 2,661 papers for “Inductive programming”

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

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

Automatic Inductive Programming Tutorial

Aler, Ricardo · e-Archivo (Carlos III University of Madrid) · 2010

Tutorial de 4 horas de duración, aceptado e impartido en: International Conference in Machine Learning ICML 2006.

Cited by 0Open access

Probabilistic Inductive Logic Programming

24th European Conference on Artificial Intelligence 2020, Riguzzi, Fabrizio · Underline Science Inc. · 2021

The combination of logic and probability is very useful for modeling domains with complex and uncertain relationships among entities. Machine learning approaches based on such combinations have recently achieved important results, originating the fields of Statistical Relational Learning, Probabilistic Inductive Logic Programming and, more generally, Statistical Relational Artificial Intelligence. The tutorial will concentrate on Probabilistic Logic Programming, a form of Probabilistic Programming that is receiving an increasing attention for its ability to combine powerful knowledge representation with Turing completeness. This tutorial will introduce probabilistic logic programming and overview the main systems for learning models in these formalisms both in terms of parameters and of structure. The tutorial includes a significant hands-on experience with the systems ProbLog2 and cplint using the web applications https://dtai.cs.kuleuven.be/problog/ and http://cplint.eu that the attendants can access with their notebooks via wifi.

Cited by 0Open access

Inductive Definitions in Constraint Programming

Rehan Abdul, Aziz Peter, J. Stuckey et al. · 2013

Constraint programming (CP) and answer set programming (ASP) are two declarative paradigms used to solve combinatorial problems. Many modern solvers for both these paradigms rely on partial or complete Boolean representations of the problem to exploit the extremely efficient techniques that have been developed for solving propositional satisfiability problems. This convergence on a common representation makes it possible to incorporate useful features of CP into ASP and vice versa. There has been significant effort in recent years to integrate CP into ASP, primarily to overcome the grounding bottleneck in traditional ASP solvers that exists due to their inability to handle integer variables efficiently. On the other hand, ASP solvers are more efficient than CP systems on problems that involve inductive definitions, such as reachability in a graph. Besides efficiency, ASP syntax is more natural and closer to the mathematical definitions of such concepts. In this paper, we describe an approach that adds support for answer set rules to a CP system, namely the lazy clause generation solver chuffed. This integration also naturally avoids the grounding bottleneck of ASP since constraint solvers natively support finite domain variables. We demonstrate the usefulness of our approach by comparing our new system against two competitors: the state-of-the-art ASP solver clasp, and clingcon, a system that extends clasp with CP capabilities.

Cited by 0

Contributions to Inductive Logic Programming

Afstudeerscriptie Bestuurlijke Informatica, R. M. De Wolf, What Is Inductive Logic Programming · 1996

Contents Preface iii 1 What is Inductive Logic Programming? 1 1.1 The importance of learning : : : : : : : : : : : : : : : : : : : : : 1 1.2 Inductive learning : : : : : : : : : : : : : : : : : : : : : : : : : : 2 1.3 The problem setting for ILP : : : : : : : : : : : : : : : : : : : : : 4 1.4 Other problem settings : : : : : : : : : : : : : : : : : : : : : : : : 9 1.5 A brief history of the field : : : : : : : : : : : : : : : : : : : : : : 10 1.6 An outline of the thesis : : : : : : : : : : : : : : : : : : : : : : : 12 2 The Subsumption Theorem for Several Forms of Resolution 13 2.1 Introduction : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : 13 2.2 Preliminaries : : : : : : : : : : : : : : : : : : : : : : : : : : : : : 16 2.3 The Subsumption Theorem : : : : : : : : : : : : : : : : : : : : : 18 2.3

Cited by 0

Towards inductive support logic programming

J.F. Baldwin, T. P. Martin · 2002

Support logic programming and its practical implementation (Fril) integrates probabilistic and fuzzy uncertainty into logic programming using mass assignments. This paper presents a snapshot of current research, aimed at combining the best aspects of inductive logic programming with the uncertainty representation of Fril to create a sophisticated and novel approach to knowledge discovery. An example is given showing how a supported Fril rule can be extracted from uncertain Fril relations.

Cited by 2

Inductive definitions in constraint programming

Rehan Aziz, Peter J. Stuckey, Zoltán Somogyi · 2013

Constraint programming (CP) and answer set programming (ASP) are two declarative paradigms used to solve combinatorial problems. Many modern solvers for both these paradigms rely on partial or complete Boolean representations of the problem to exploit the extremely efficient techniques that have been developed for solving propositional satisfiability problems. This convergence on a common representation makes it possible to incorporate useful features of CP into ASP and vice versa. There has been significant effort in recent years to integrate CP into ASP, primarily to overcome the grounding bottleneck in traditional ASP solvers that exists due to their inability to handle integer variables efficiently. On the other hand, ASP solvers are more efficient than CP systems on problems that involve inductive definitions, such as reachability in a graph. Besides efficiency, ASP syntax is more natural and closer to the mathematical definitions of such concepts. In this paper, we describe an approach that adds support for answer set rules to a CP system, namely the lazy clause generation solver chuffed. This integration also naturally avoids the grounding bottleneck of ASP since constraint solvers natively support finite domain variables. We demonstrate the usefulness of our approach by comparing our new system against two competitors: the state-of-the-art ASP solver clasp, and clingcon, a system that extends clasp with CP capabilities.

Cited by 1

Inductive bias and genetic programming

Peter A. Whigham · 1995

Many engineering problems may be described as a search for one near optimal description amongst many possibilities, given certain constraints. Search techniques such as genetic programming, seem appropriate to represent many problems. The paper describes a grammatically based learning technique based upon the genetic programming paradigm, that allows declarative biasing and modifies the bias as the evolution proceeds. The use of bias allows complex problems to be represented and searched efficiently.

Cited by 148

Bayesian Inductive Logic Programming Abstract

Stephen Muggleton · 2015

Inductive Logic Progrdng (ILP) involves the construction of first-order definite clause theories from examples and background knowledge, Un-like both traditional Machine Learning and Com-putational Learning Theory, ILP is based on lock-step development of Theory, Implementations and Applications. ILP systems have successful appli-cations in the learning of structure-activity rules for drug design, semantic grammars rules, finite element mesh design rules and rules for prediction of protein structure and mutagenic molecules. The strong applications in ILP can be contrasted with relatively weak PAC-learning results (even highly-restricted forms of logic programs are known to be prediction-hard). It has been recently argued that the mismatch is due to distributional assump-tions made in application domains. These assump-tions can be modelled as a Bayesiau prior prob-ability representing subjective degrees of belief. Other authors have argued for the use of Bayesian prior distributions for reasons different to those here, though this has not lead to a new model of polynomial-time learnability. Incorporation of Bayesian prior distributions over time-bounded hy-potheses in PAC leads to a new model called U-Learnability. It is argued that U-learnability is more appropriate than PAC for Universal (Tur-ing computable) languages. Time-bounded logic programs have been shown to be polynomially U-learnable under certain distributions. The use of time-bounded hypotheses enforces decidability and allows a unified characterisation of speed-up learning and inductive learning. U-learnability has as special cases PAC and Natarajan’s model of speed-up learning. Permission to copy without fee all or part of this material is granted provided that the copies are not made or distributed for direct commercial advantage, the ACM copyright notice and the title of the publication and its date appear, and notice is given

Cited by 0