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Meta-interpretive learning: application to grammatical inference

MACHINE LEARNING 2014年第1期94卷 25-49页

作者： Muggleton, Stephen H. Lin, Dianhuan Pahlavi, Niels Tamaddoni-Nezhad, Alireza Univ London Imperial Coll Sci Technol & Med Dept Comp London England

Despite early interest Predicate Invention has lately been under-explored within ILP. We develop a framework in which predicate invention and recursive generalisations are implemented using abduction with respect to a meta-interpreter. The approach is based on a previously unexplored case of Inverse Entailment for Grammatical Inference of Regular languages. Every abduced grammar H is represented by a conjunction of existentially quantified atomic formulae. Thus Anot signH is a universally quantified clause representing a denial. The hypothesis space of solutions for Anot signH can be ordered by theta-subsumption. We show that the representation can be mapped to a fragment of Higher-Order Datalog in which atomic formulae in H are projections of first-order definite clause grammar rules and the existentially quantified variables are projections of first-order predicate symbols. This allows predicate invention to be effected by the introduction of first-order variables. Previous work by Inoue and Furukawa used abduction and meta-level reasoning to invent predicates representing propositions. By contrast, the present paper uses abduction with a meta-interpretive framework to invent relations. We describe the implementations of Meta-interpretive Learning (MIL) using two different declarative representations: Prolog and Answer Set programming (ASP). We compare these implementations against a state-of-the-art ILP system MC-TopLog using the dataset of learning Regular and Context-Free grammars as well learning a simplified natural language grammar and a grammatical description of a staircase. Experiments indicate that on randomly chosen grammars, the two implementations have significantly higher accuracies than MC-TopLog. In terms of running time, Metagol is overall fastest in these tasks. Experiments indicate that the Prolog implementation is competitive with the ASP one due to its ability to encode a strong procedural bias. We demonstrate that MIL can be applied to learni

关键词： inductive logic programming Meta-interpretative learning Predicate invention Recursion Grammatical inference

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Disjunctive Abduction

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NEW GENERATION COMPUTING 2019年第2期37卷 219-243页

作者： Inoue, Katsumi Sakama, Chiaki Natl Inst Informat Tokyo Japan Grad Univ Adv Studies SOKENDAI Dept Informat Tokyo Japan Tokyo Inst Technol Sch Comp Dept Comp Sci Tokyo Japan Wakayama Univ Dept Comp & Commun Sci Wakayama Japan

Given an observation or a goal, abduction infers candidate hypotheses to explain the observation or to achieve the goal. In this paper, we consider disjunctive abduction, in which disjunctions play important roles in abduction. Machine-oriented intelligent systems can utilize disjunctive abduction in many ways. For example, consider the case in which multiple explanations exist for an observation. We may need to select the best explanation among them, but often it is difficult to choose only one given insufficient information. In that case, we can leave the decision indefinite by considering a disjunctive explanation, which is a disjunction of possible alternative explanations. Disjunctive explanations can offer a consistent way to theory changes when they are incorporated or assimilated into the current knowledge base. The assimilated knowledge base preserves the intended semantics from the collection of all possible updated knowledge bases. A merit of disjunctive explanations is that we only need one current knowledge base at a time, still keeping every possible change in a single state. This method is extended to accomplish an update when there are multiple ways to remove old unnecessary hypotheses from the knowledge base. The proposed framework is well applicable to view updates in disjunctive databases. We also show that disjunctive abduction can be considered in inductive logic programming.

关键词： Abduction logic programming Knowledge assimilation inductive logic programming

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Learning logic programs with structured background knowledge

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ARTIFICIAL INTELLIGENCE 2001年第1-2期128卷 31-97页

作者： Horváth, T Turán, C GMD AiS Inst Autonomous Intelligent Syst German Natl Res Ctr Informat Technol D-53754 St Augustin Germany Univ Illinois Dept Math Stat & Comp Sci Chicago IL 60607 USA Hungarian Acad Sci Res Grp Artificial Intelligence Szeged Hungary

The efficient learnability of restricted classes of logic programs is studied in the PAC framework of computational learning theory, We develop the product homomorphism method, which gives polynomial PAC learning algorithms for a nonrecursive Horn clause with function-free ground background knowledge, if the background knowledge satisfies some structural properties. The method is based on a characterization of the concept that corresponds to the relative least general generalization of a set of positive examples with respect to the background knowledge. The characterization is formulated in terms of products and homomorphisms. In the applications this characterization is turned into an explicit combinatorial description, which is then translated into the language of nonrecursive Horn clauses, We show that a nonrecursive Horn clause is polynomially PAC-learnable if there is a single binary background predicate and the ground atoms in the background knowledge form a forest. If the ground atoms in the background knowledge form a disjoint union of cycles then the situation is different, as the shortest consistent hypothesis may have exponential size. In this case polynomial PAC-learnability holds if a different representation language is used. We also consider the complexity of hypothesis finding for multiple clauses in some restricted cases. (C) 2001 Elsevier Science B,V. All rights reserved.

关键词： computational learning theory PAC learning concept learning inductive logic programming VC-dimension

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An empirical study of on-line models for relational data streams

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MACHINE LEARNING 2017年第2期106卷 243-276页

作者： Srinivasan, Ashwin Bain, Michael BITS Pilani Dept Comp Sci & Informat Syst KK Birla Goa Campus Sancoale Goa India UNSW Sch Comp Sci & Engn Sydney NSW 2052 Australia

To date, inductive logic programming (ILP) systems have largely assumed that all data needed for learning have been provided at the onset of model construction. Increasingly, for application areas like telecommunications, astronomy, text processing, financial markets and biology, machine-generated data are being generated continuously and on a vast scale. We see at least four kinds of problems that this presents for ILP: (1) it may not be possible to store all of the data, even in secondary memory;(2) even if it were possible to store the data, it may be impractical to construct an acceptable model using partitioning techniques that repeatedly perform expensive coverage or subsumption-tests on the data;(3) models constructed at some point may become less effective, or even invalid, as more data become available (exemplified by the "drift" problem when identifying concepts);and (4) the representation of the data instances may need to change as more data become available (a kind of "language drift" problem). In this paper, we investigate the adoption of a stream-based on-line learning approach to relational data. Specifically, we examine the representation of relational data in both an infinite-attribute setting, and in the usual fixed-attribute setting, and develop implementations that use ILP engines in combination with on-line model-constructors. The behaviour of each program is investigated using a set of controlled experiments, and performance in practical settings is demonstrated by constructing complete theories for some of the largest biochemical datasets examined by ILP systems to date, including one with a million examples;to the best of our knowledge, the first time this has been empirically demonstrated with ILP on a real-world data set.

关键词： inductive logic programming Data streams Online learning

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Relational data factorization

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MACHINE LEARNING 2017年第12期106卷 1867-1904页

作者： Paramonov, Sergey van Leeuwen, Matthijs De Raedt, Luc Katholieke Univ Leuven Machine Learning Dept Comp Sci Leuven Belgium Leiden Univ Leiden Inst Adv Comp Sci Leiden Netherlands

Motivated by an analogy with matrix factorization, we introduce the problem of factorizing relational data. In matrix factorization, one is given a matrix and has to factorize it as a product of other matrices. In relational data factorization, the task is to factorize a given relation as a conjunctive query over other relations, i.e., as a combination of natural join operations. Given a conjunctive query and the input relation, the problem is to compute the extensions of the relations used in the query. Thus, relational data factorization is a relational analog of matrix factorization;it is also a form of inverse querying as one has to compute the relations in the query from the result of the query. The result of relational data factorization is neither necessarily unique nor required to be a lossless decomposition of the original relation. Therefore, constraints can be imposed on the desired factorization and a scoring function is used to determine its quality (often similarity to the original data). Relational data factorization is thus a constraint satisfaction and optimization problem. We show how answer set programming can be used for solving relational data factorization problems.

关键词： Answer set programming inductive logic programming Pattern mining Relational data Factorization Data mining Declarative modeling

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First order regression

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MACHINE LEARNING 1997年第2-3期26卷 147-176页

作者： Karalic, A Bratko, I UNIV LJUBLJANA FAC ELECT & COMP SCILJUBLJANASLOVENIA

We present a new approach, called First Order Regression (FOR), to handling numerical information in inductive logic programming (ILP). FOR is a combination of ILP and numerical regression. First-order logic descriptions are induced to carve out those subspaces that are amenable to numerical regression among real-valued variables. The program FORS is an implementation of this idea, where numerical regression is focused on a distinguished continuous argument of the target predicate. We show that this can be viewed as a generalisation of the usual ILP problem. Applications of FORS On several real-world data sets are described: the prediction of mutagenicity of chemicals, the modelling of liquid dynamics in a surge tank, predicting the roughness in steel grinding, finite element mesh design, and operator's skill reconstruction in electric discharge machining. A comparison of FORS' performance with previous results in these domains indicates that FORS is an effective tool for ILP applications that involve numerical data.

关键词： machine learning inductive logic programming regression real-valued variables first-order logic applications of machine learning

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L-wrappers: concepts, properties and construction - A declarative approach to data extraction from web sources

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SOFT COMPUTING 2007年第8期11卷 753-772页

作者： Badica, Costin Badica, Amelia Popescu, Elvira Abraham, Ajith Univ Craiova Software Engn Dept Craiova 200440 Romania Univ Craiova Business Informat Syst Dept Craiova 200585 Romania Chung Ang Univ Sch Comp Sci & Engn IITA Professorship Program Seoul 156756 South Korea

In this paper, we propose a novel class of wrappers (logic wrappers) inspired by the logic prog- ramming paradigm. The developed logic wrappers (L-wrapper) have declarative semantics, and therefore: (i) their specification is decoupled from their implementation and (ii) they can be generated using inductive logic programming. We also define a convenient way for mapping L-wrappers to XSLT for efficient processing using available XSLT processing engines.

关键词： logic wrappers information retrieval web intelligence inductive logic programming

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Learning fuzzy rules with their implication operators

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DATA & KNOWLEDGE ENGINEERING 2007年第1期60卷 71-89页

作者： Serrurier, Mathieu Dubois, Didier Prade, Henri Sudkamp, Thomas Univ Toulouse 3 IRIT CNRS F-31062 Toulouse France Wright State Univ Dept Comp Sci Dayton OH 45385 USA

Fuzzy predicates have been incorporated into machine learning and data mining to extend the types of data relationships that can be represented, to facilitate the interpretation of rules in linguistic terms, and to avoid unnatural boundaries in partitioning attribute domains. The confidence of an association is classically measured by the co-occurrence of attributes in tuples in the database. The semantics of fuzzy rules, however, is not co-occurrence but rather graduality or certainty and is determined by the implication operator that defines the rule. In this paper we present a learning algorithm, based on inductive logic programming, that simultaneously learns the semantics and evaluates the validity of fuzzy rules. The learning algorithm selects the implication that maximizes rule confidence while trying to be as informative as possible. The use of inductive logic programming increases the expressive power of fuzzy rules while maintaining their linguistic interpretability. (c) 2006 Elsevier B.V. All rights reserved.

关键词： inductive logic programming fuzzy rules implication operators

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Structure and expression of the insulin-like peptide receptor from amphioxus

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MOLECULAR ENDOCRINOLOGY 1996年第7期10卷 857-866页

作者： Pashmforoush, M Chan, SJ Stainer, DF UNIV CHICAGO HOWARD HUGHES MED INST CHICAGO IL 60637 USA UNIV CHICAGO DEPT BIOCHEM & MOLEC BIOL CHICAGO IL 60637 USA

Insulin and the insulin-like growth factors IGF-I and IGF-II are found in all vertebrates, and these anabolic peptides share primary and tertiary structural features which suggest that they have evolved from a common ancestral gene. We have proposed that an insulin-like peptide (ILP) cDNA recently cloned from the protochodate amphioxus may represent the ancestral gene in that the deduced sequence of ILP contains features of both insulin and IGF, and it evidently represents a hybrid insulin/lGF molecule. To expand this hypothesis we have cloned the cDNA that encodes the cognate receptor from amphioxus. Primary sequence comparisons show that the ILP receptor is a member of the insulin receptor family, which in mammals includes the insulin receptor (IR), type I IGF receptor (IGF-IR), and IR-related receptor (IRR). In overall amino acid sequence, the ILP receptor is 48.6% identical to the human (h)IR, 47.3% identical to hIGF-IR, and 43.7% identical to hIRR, and this contrasts with the finding that hIR and hIGF-IR share 57.6% identity. Using degenerate oligonucleotide primers, we show by RT-PCR that amphioxus contains only a single member of the insulin receptor gene family. To complement the sequence comparison, we expressed the ILP receptor protein by transfecting the cDNA into 293 cells. Autophosphorylation of the expressed ILP receptor was half-maximally stimulated by a synthetic ILP analog, (B1-Thr)ILP, at a concentration of about 5 x 10(-7) M. Interestingly, autophosphorylation of the ILP receptor was also stimulated by incubation with either mammalian insulin or IGF-I, although equally high concentrations (10(-5) M) of each were required. Based on these results, we propose that, analogously to the ILP gene, the ancestral ILP receptor gene also duplicated and diverged to generate the IR and IGF-IR genes during the evolutionary transition from protochordates to vertebrates. Our results also indicate that the amphioxus ILP receptor contains the basic structural deter

关键词： Growth factor-i Factor cdna Determinants Sequence Superfamily Evolution Family Ligand 1 5-a Acid inductive logic programming Fibrinogen Evolution Receptor Peptide Receptor Sequences Determinants Insulin ligands Oligonucleotide Primers superfamily evolution Lancelets family acids

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Adaptive complex event processing for harmful situation detection

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EVOLVING SYSTEMS 2012年第3期3卷 167-177页

作者： Coffi, Jean-Rene Marsala, Christophe Museux, Nicolas Thales Res & Technol 1Ave Augustin Fresnel F-91767 Palaiseau France Univ Paris 06 UPMC UMR 7606 LIP6 F-75005 Paris France

In the field of infrastructures' surveillance and protection, it is important to make decisions based on activities occurring in the environment and its local context and conditions. In this paper we use an active rule based event processing architecture in order to make sense of situations from the combination of different signals received by the rule engine. However obtaining some high level information automatically is not without risks, especially in sensitive environments, and detection mistakes can happen for various reasons: the signal's source can be defective, whether it is human-miss-interpretation of the signal-or computed-material malfunction;the aggregation rules can be wrong syntaxically, for example when a rule will never be triggered or a situation never detected;the interpretation given to the combination of signals does not correspond to the reality on the field-because the knowledge of the rule designer is subjective or because the environment evolves over-time-the rules are therefore incorrect semantically. In this paper, a new approach is proposed to avoid the third kind of error sources. We present a hybrid machine learning technique adapted to the complexity of the rules' representation, in order to create a system more conform to reality. The proposed approach uses a combination of an Association Rule Mining algorithm and inductive logic programming for rule induction. Empirical studies on simulated datasets demonstrate how our method can contribute to sensible systems such as the security of a public or semi-public place.

关键词： Complex event processing inductive logic programming Association Rule Mining Incremental Learning

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