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Inference of abduction theories for handling incompleteness in first-order learning

KNOWLEDGE AND INFORMATION SYSTEMS 2007年第2期11卷 217-242页

作者： Esposito, F. Ferilli, S. Basile, T. M. A. Di Mauro, N. Univ Bari Dept Comp Sci I-70121 Bari Italy

In real-life domains, learning systems often have to deal with various kinds of imperfections in data such as noise, incompleteness and inexactness. This problem seriously affects the knowledge discovery process, specifically in the case of traditional Machine Learning approaches that exploit simple or constrained knowledge representations and are based on single inference mechanisms. Indeed, this limits their capability of discovering fundamental knowledge in those situations. In order to broaden the investigation and the applicability of machine learning schemes in such particular situations, it is necessary to move on to more expressive representations which require more complex inference mechanisms. However, the applicability of such new and complex inference mechanisms, such as abductive reasoning, strongly relies on a deep background knowledge about the specific application domain. This work aims at automatically discovering the meta-knowledge needed to abduction inference strategy to complete the incoming information in order to handle cases of missing knowledge.

关键词： incomplete knowledge inductive logic programming abduction

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Task selection for the multiscalar architecture

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JOURNAL OF PARALLEL AND DISTRIBUTED COMPUTING 1999年第2期58卷 132-158页

作者： Vijaykumar, TN Sohi, GS Purdue Univ Sch Elect & Comp Engn W Lafayette IN 47907 USA Univ Wisconsin Dept Comp Sci Madison WI 53706 USA

The multiscalar architecture advocates a distributed processor organization and task-level speculation to exploit high degrees of instruction level parallelism (ILP) in sequential programs without impeding improvements in clock speeds. The main goal of this paper is to understand the key implications of the architectural features of distributed processor organization and task-level speculation for compiler task selection from the point of view of performance. We identify the fundamental performance issues to be: control flow speculation, data communication, data dependence speculation, load imbalance, and task overhead. We show that these issues are intimately related to a few key characteristics of tasks: task size, intertask control flow, and intertask data dependence. We describe compiler heuristics to select tasks with favorable characteristics. We report experimental results to show that the heuristics are successful in boosting overall performance by establishing larger ILP windows. We also present a breakdown of execution times to show that register wait, load imbalance, control flow squash, and conventional pipeline losses are significant for almost all the SPEC95 benchmarks. (C) 1999 Academic Press.

关键词： compilers Breakdown ARCHITECTURE Building construction Timing devices data dependency Religious Missions inductive logic programming sequential program Heuristics execution time architectural styles task selection unbalanced load

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Learning closed Horn expressions

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INFORMATION AND COMPUTATION 2002年第1期178卷 214-240页

作者： Arias, M Khardon, R Tufts Univ Medford MA 02155 USA

The paper studies the learnability of Horn expressions within the framework of learning from entailment, where the goal is to exactly identify some pre-fixed and unknown expression by making queries to membership and equivalence oracles. It is shown that a class that includes both range restricted Horn expressions (where terms in the conclusion also appear in the condition of a Horn clause) and constrained Horn expressions (where terms in the condition also appear in the conclusion of a Horn clause) is learnable. This extends previous results by showing that a larger class is learnable with better complexity bounds. A further improvement in the number of queries is obtained when considering the class of Horn expressions with inequalities on all syntactically distinct terms. (C) 2002 Elsevier Science (USA).

关键词： computational teaming theory inductive logic programming Horn expressions algorithms queries

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Learning programs by learning from failures

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MACHINE LEARNING 2021年第4期110卷 801-856页

作者： Cropper, Andrew Morel, Rolf Univ Oxford Oxford England

We describe an inductive logic programming (ILP) approach called learning from failures. In this approach, an ILP system (the learner) decomposes the learning problem into three separate stages: generate, test, and constrain. In the generate stage, the learner generates a hypothesis (a logic program) that satisfies a set of hypothesis constraints (constraints on the syntactic form of hypotheses). In the test stage, the learner tests the hypothesis against training examples. A hypothesis fails when it does not entail all the positive examples or entails a negative example. If a hypothesis fails, then, in the constrain stage, the learner learns constraints from the failed hypothesis to prune the hypothesis space, i.e. to constrain subsequent hypothesis generation. For instance, if a hypothesis is too general (entails a negative example), the constraints prune generalisations of the hypothesis. If a hypothesis is too specific (does not entail all the positive examples), the constraints prune specialisations of the hypothesis. This loop repeats until either (i) the learner finds a hypothesis that entails all the positive and none of the negative examples, or (ii) there are no more hypotheses to test. We introduce Popper, an ILP system that implements this approach by combining answer set programming and Prolog. Popper supports infinite problem domains, reasoning about lists and numbers, learning textually minimal programs, and learning recursive programs. Our experimental results on three domains (toy game problems, robot strategies, and list transformations) show that (i) constraints drastically improve learning performance, and (ii) Popper can outperform existing ILP systems, both in terms of predictive accuracies and learning times.

关键词： inductive logic programming Program synthesis Program induction

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Separate-and-conquer rule learning

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ARTIFICIAL INTELLIGENCE REVIEW 1999年第1期13卷 3-54页

作者： Fürnkranz, J Austrian Res Inst Artificial Intelligence A-1010 Vienna Austria

This paper is a survey of inductive rule learning algorithms that use a separate-and-conquer strategy. This strategy can be traced back to the AQ learning system and still enjoys popularity as can be seen from its frequent use in inductive logic programming systems. We will put this wide variety of algorithms into a single framework and analyze them along three different dimensions, namely their search, language and overfitting avoidance biases.

关键词： covering inductive logic programming inductive rule learning

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Relational machine learning for electronic health record-driven phenotyping

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JOURNAL OF BIOMEDICAL INFORMATICS 2014年 52卷 260-270页

作者： Peissig, Peggy L. Costa, Vitor Santos Caldwell, Michael D. Rottscheit, Carla Berg, Richard L. Mendonca, Eneida A. Page, David Marshfield Clin Res Fdn Biomed Informat Res Ctr Marshfield WI 54449 USA Univ Porto DCC FCUP P-4100 Oporto Portugal Univ Porto Dept Ciencia Comp CRACS INESC TEC P-4100 Oporto Portugal Marshfield Clin Fdn Med Res & Educ Dept Surg Marshfield WI USA Univ Wisconsin Dept Biostat & Med Informat Madison WI 53706 USA Univ Wisconsin Dept Pediat Madison WI 53706 USA Univ Wisconsin Dept Comp Sci Madison WI 53706 USA

Objective: Electronic health records (EHR) offer medical and pharmacogenomics research unprecedented opportunities to identify and classify patients at risk. EHRs are collections of highly inter-dependent records that include biological, anatomical, physiological, and behavioral observations. They comprise a patient's clinical phenome, where each patient has thousands of date-stamped records distributed across many relational tables. Development of EHR computer-based phenotyping algorithms require time and medical insight from clinical experts, who most often can only review a small patient subset representative of the total EHR records, to identify phenotype features. In this research we evaluate whether relational machine learning (ML) using inductive logic programming (ILP) can contribute to addressing these issues as a viable approach for EHR-based phenotyping. Methods: Two relational learning ILP approaches and three well-known WEKA (Waikato Environment for Knowledge Analysis) implementations of non-relational approaches (PART, J48, and JRIP) were used to develop models for nine phenotypes. International Classification of Diseases, Ninth Revision (ICD-9) coded EHR data were used to select training cohorts for the development of each phenotypic model. Accuracy, precision, recall, F-Measure, and Area Under the Receiver Operating Characteristic (AUROC) curve statistics were measured for each phenotypic model based on independent manually verified test cohorts. A two-sided binomial distribution test (sign test) compared the five ML approaches across phenotypes for statistical significance. Results: We developed an approach to automatically label training examples using ICD-9 diagnosis codes for the ML approaches being evaluated. Nine phenotypic models for each ML approach were evaluated, resulting in better overall model performance in AUROC using ILP when compared to PART (p = 0.039), J48 (p = 0.003) and JRIP (p = 0.003). Discussion: ILP has the potential to impro

关键词： Machine learning Electronic health record inductive logic programming Phenotyping Relational machine learning

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Evolutionary concept learning in first order logic: An overview

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AI COMMUNICATIONS 2006年第1期19卷 13-33页

作者： Divina, F Tilburg Univ Computat Linguist & AI Sect NL-5000 LE Tilburg Netherlands

This paper presents an overview of evolutionary approaches to inductive logic programming (ILP). After a short description of the two popular ILP systems FOIL and Progol, we focus on methods based on evolutionary algorithms (EAs). Six systems are described and compared by means of the following aspects: search strategy, representation, hypothesis evaluation, search operators and biases adopted for limiting the hypothesis space. We discuss possible advantages and drawbacks related to the specific features of the systems along these aspects. Issues concerning the relative performance and efficiency of the systems are addressed.

关键词： Evolutionary computation inductive Concept Learning First Order logic inductive logic programming

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A logic-based Computational Method for the Automated Induction of Fuzzy Ontology Axioms

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FUNDAMENTA INFORMATICAE 2013年第4期124卷 503-519页

作者： Lisi, Francesca A. Straccia, Umberto Univ Bari Aldo Moro Dipartimento Informat Bari Italy ISTI CNR Pisa Italy

Fuzzy Description logics (DLs) are logics that allow to deal with structured vague knowledge. Although a relatively important amount of work has been carried out in the last years concerning the use of fuzzy DLs as ontology languages, the problem of automatically managing the evolution of fuzzy ontologies has received very little attention so far. We describe here a logic-based computational method for the automated induction of fuzzy ontology axioms which follows the machine learning approach of inductive logic programming. The potential usefulness of the method is illustrated by means of an example taken from the tourism application domain.

关键词： inductive logic programming Fuzzy Description logics Ontologies

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Learning any memory-less discrete semantics for dynamical systems represented by logic programs

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MACHINE LEARNING 2022年第10期111卷 3593-3670页

作者： Ribeiro, Tony Folschette, Maxime Magnin, Morgan Inoue, Katsumi Univ Nantes LS2N CNRS Cent Nantes F-44000 Nantes France Natl Inst Informat Chiyoda Ku 2-1-2 Hitotsubashi Tokyo 1018430 Japan Univ Lille Cent Lille CNRS UMR CRIStAL 9189 F-59000 Lille France

Learning from interpretation transition (LFIT) automatically constructs a model of the dynamics of a system from the observation of its state transitions. So far the systems that LFIT handled were mainly restricted to synchronous deterministic dynamics. However, other dynamics exist in the field of logical modeling, in particular the asynchronous semantics which is widely used to model biological systems. In this paper, we propose a modeling of discrete memory-less multi-valued dynamic systems as logic programs in which a rule represents what can occur rather than what will occur. This modeling allows us to represent non-determinism and to propose an extension of LFIT to learn regardless of the update schemes, allowing to capture a large range of semantics. We also propose a second algorithm which is able to learn a whole system dynamics, including its semantics, in the form of a single propositional logic program with constraints. We show through theoretical results the correctness of our approaches. Practical evaluation is performed on benchmarks from biological literature.

关键词： inductive logic programming Dynamic systems logical modeling Dynamic semantics

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Methodology to derive context adaptable architectures for FPGAs

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IET COMPUTERS AND DIGITAL TECHNIQUES 2009年第1期3卷 124-141页

作者： Phillips, J. Sudarsanam, A. Samala, H. Kallam, R. Carver, J. Dasu, A. Utah State Univ Dept Elect & Comp Engn Logan UT 84321 USA

The configurable nature of field-programmable gate arrays (FPGAs) has allowed designers to take advantage of various data flow characteristics in application kernels to create custom architecture implementations, by optimising instruction level paralleism (ILP) and pipelining at the register transfer level. However, not all applications are composed of pure data flow kernels. Intermingling of control and data flows in applications offers more interesting challenges in creating custom architectures. The authors present one possible way to take advantage of correlations that may be present among data flow graphs (DFGs) embedded in control flow graphs. In certain cases, where there is sufficient correlation and ILP, the proposed context adaptable architecture (CAA) design methodology results in an interesting and useful custom architecture for such embedded DFGs. Certain other application characteristics may demand the use of alternative methodologies such as partial and dynamic reconfiguration (PDR) and a mixture of PDR and common sub-graph methods (PDR-CSG). The authors present a rigorous analysis, combined with some benchmarking efforts to showcase the differences, advantages and disadvantages of the CAA methodology with other methodologies. The authors also present an analysis of how the core underlying algorithm in our methodology compares with other published algorithms and the differences in resulting designs on an FPGA for a sample set of test cases.

关键词： ARCHITECTURE Building construction CASING INSPECTION Clean Air Act Pipeline processing AURAL ATRESIA, CONGENITAL Customs inductive logic programming control flow diagram methodology Field programmable gate arrays data flow EASE OF APPLICATION PDR gene Customization

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