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inductive logic programming - 20th international conference, ilp 2010, Revised Papers

20th international conference on inductive logic programming, ilp 2010

ISBN: (纸本)9783642212949

the proceedings contain 28 papers. the topics discussed include: probabilistic relational learning and inductive logic programming at a global scale;practical probabilistic programming;learning multi-class theories in ilp;a numerical refinement operator based on multi-instance learning;not far away from home: a relational distance-based approach to understanding images of houses;approximate inference for logic programs with annotated disjunctions;approximate Bayesian computation for the parameters of PRISM programs;probabilistic rule learning;interactive discriminative mining of chemical fragments;MMRF for proteome annotation applied to human protein disease prediction;multivariate prediction for learning on the semantic web;hypothesizing about causal networks with positive and negative effects by meta-level abduction;BET: an inductive logic programming workbench;and seeing the world through homomorphism: an experimental study on reducibility of examples.

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inductive logic programming - 19th international conference, ilp 2009, Revised Papers

Inductive Logic Programming - 19th International Conference,...

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19th international conference on inductive logic programming, ilp 2009

ISBN: (纸本)364213839X

the proceedings contain 24 papers. the topics discussed include: knowledge-directed theory revision;towards clausal discovery for stream mining;on the relationship between logical Bayesian networks and probabilistic logic programming based on the distribution semantics;induction of relational algebra expressions;a logic-based approach to relation extraction from texts;discovering rules by meta-level abduction;inductive generalization of analytically learned goal hierarchies;nonmonotonic onto-relational learning;cp-logic theory inference with contextual variable elimination and comparison to BDD based inference methods;speeding up inference in statistical relational learning by clustering similar query literals;an inductive logic programming approach to validate hexose binding biochemical knowledge;boosting first-order clauses for large, skewed data sets;and transfer learning via relational templates.

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inductive logic programming - 18th international conference, ilp 2008, Proceedings

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18th international conference on inductive logic programming, ilp 2008

ISBN: (纸本)3540859276

the proceedings contain 20 papers. the topics discussed include: building theories of the world: human and machine learning perspectives;SRL without tears: an ilp perspective;semantic web meets ilp: unconsumated love, or no love lost?;learning expressive models of gene regulation;information overload and FP7 funding opportunities in 2009-10;a model to study phase transition and plateaus in relational learning;top-down induction of relational model trees in multi-instance learning;challenges in relational learning for real-time systems applications;discriminative structure learning of Markov logic networks;an experiment in robot discovery with ilp;using the bottom clause and mode declarations on FOL theory revision from examples;DL-FOIL: concept learning in description logics;feature discovery with type extension trees;and feature construction using theory-guided sampling and randomised search.

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Towards Explainable Weather Forecasting through FastLAS 17th

Towards Explainable Weather Forecasting Through FastLAS

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17th international conference on logic programming and Non-monotonic Reasoning

作者： Dreossi, Talissa Dovier, Agostino Formisano, Andrea Law, Mark Manzato, Agostino Russo, Alessandra Tait, Matthew Univ Udine Udine Italy ILASP Ltd London England ARPA FVG OSMER Udine Italy Imperial Coll London London England

ISBN: (纸本)9783031742088;9783031742095

Weather forecasting is important for saving lives, protecting property, and supporting economic activities. It provides timely warnings for severe weather, improves agricultural planning, and aids in disaster management. Neural networks and deep learning methods can achieve impressive accuracy in weather prediction, but their black-box nature lacks in explainability. To address this limitation, we investigated the potential of FastLAS, an inductive logic programming (ilp) framework, to produce reliable and, more important, explainable weather predictions. FastLAS learns ASP programs whose syntax and structural semantics resemble natural human language, making them easily understandable and interpretable by humans. the supportedness of stable models allows a clear explanation of the predictions. Our empirical evaluation on data from an Italian weather forecasting center shows that our approach is capable of learning predictive models from small dataset (a few samples instead of the thousands needed by neural networks) achieving an accuracy higher than statistical machine learning base lines.

关键词： ilp logic programming AI Explainability

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Computable Relations Mapping with Horn Clauses for inductive Program Synthesis 20th

Computable Relations Mapping with Horn Clauses for Inducti...

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20th Principle and Practice of Data and Knowledge Acquisition Workshop, PKAW 2024, held in conjunction with the 21st Pacific Rim international conference on Artificial Intelligence, PRICAI 2024

作者： Qiu, Taosheng Ichise, Ryutaro National Institute of Informatics Tokyo Japan Tokyo Institute of Technology Tokyo Japan The Graduate University for Advanced Studies SOKENDAI Tokyo Japan

ISBN: (纸本)9789819600250

inductive logic programming (ilp) systems use logic programming languages like Prolog as computational models. Expressivity of these languages is often built on fixed Herbrand vocabulary, thus requires encoding arbitrarily large structures as nested functions. However, most learning systems process structures directly on relations and cannot handle function symbols. To solve this, we propose an alternative problem framework to measure the expressivity of learning systems, by considering any Turing computable mapping between arbitrarily large graphs, namely Computable Relations Mapping (CRM). CRM is a useful framework for its connection to Turing machine and straightforward representation of most computational tasks. then, we propose a minimal CRM-complete fragment of Horn clauses with default negation and function symbols, by constructing programs corresponding to all CRMs. the correctness is given by combining a checked implementation and some proof. Since smaller rules can be more efficiently searched and analyzed, we consider it a potentially better representation and reasoning model for expressive inductive inference systems. © the Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Turing machines

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Learning Multi-class theories in ilp

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20th international conference on inductive logic programming (ilp)

作者： Abudawood, Tarek Flach, Peter A. Univ Bristol Intelligent Syst Lab Bristol BS8 1TH Avon England

ISBN: (纸本)9783642212949;9783642212956

In this paper we investigate the lack of reliability and consistency of those binary rule learners in ilp that employ the one-vs-rest binarisation technique when dealing with multi-class domains. We show that we can learn a simple, consistent and reliable multi-class theory by combining the rules of the multiple one-vs-rest theories into one rule list or set. We experimentally show that our proposed methods produce coherent and accurate rule models from the rules learned by a well known ilp learner Aleph.

关键词： Learning systems

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BET : An inductive logic programming Workbench

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20th international conference on inductive logic programming (ilp)

作者： Kalgi, Srihari Gosar, Chirag Gawde, Prasad Ramakrishnan, Ganesh Gada, Kekin Iyer, Chander Kiran, T. V. S. Srinivasan, Ashwin Indian Inst Technol Dept Comp Sci & Engn Bombay Maharashtra India

ISBN: (纸本)9783642212949;9783642212956

Existing ilp (inductive logic programming) systems are implemented in different languages namely C, Progol, etc. Also, each system has its customized format for the input data. this makes it very tedious and time consuming on the part of a user to utilize such a system for experimental purposes as it demands a thorough understanding of that system and its input specification. In the spirit of Weka [1], we present a relational learning workbench called BET(Background + Examples = theories), implemented in Java. the objective of BET is to shorten the learning curve of users (including novices) and to facilitate speedy development of new relational learning systems as well as quick integration of existing ilp systems. the standardized input format makes it easier to experiment with different relational learning algorithms on a common dataset.

关键词： BET ilp systems Golem Progol FOIL PRISM TILDE

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Approximate Inference for logic Programs with Annotated Disjunctions

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20th international conference on inductive logic programming (ilp)

作者： Bragaglia, Stefano Riguzzi, Fabrizio Univ Ferrara ENDIF Univ Bologna DEIS I-44100 Ferrara Italy

ISBN: (纸本)9783642212949;9783642212956

logic Programs with Annotated Disjunctions (LPADs) are a promising language for Probabilistic inductive logic programming. In order to develop efficient learning systems for LPADs, it is fundamental to have high-performing inference algorithms. the existing approaches take too long or fail for large problems. In this paper we adapt to LPAD the approaches for approximate inference that have been developed for ProbLog, namely k-best and Monte Carlo. k-Best finds a lower bound of the probability of a query by identifying the k most probable explanations while Monte Carlo estimates the probability by smartly sampling the space of programs. the two techniques have been implemented in the cplint suite and have been tested on real and artificial datasets representing graphs. the results show that both algorithms are able to solve larger problems often in less time than the exact algorithm.

关键词： Probabilistic inductive logic programming logic Programs with Annotated Disjunctions ProbLog

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inductive logic programming AS ABDUCTIVE SEARCH

INDUCTIVE LOGIC PROGRAMMING AS ABDUCTIVE SEARCH

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26th international conference on logic programming (ICLP)

作者： Corapi, Domenico Russo, Alessandra Lupu, Emil Imperial Coll London Dept Comp 180 Queens Gate London SW7 2AZ England

ISBN: (纸本)9783939897170

We present a novel approach to non-monotonic ilp and its implementation called TAL (Top-directed Abductive Learning). TAL overcomes some of the completeness problems of ilp systems based on Inverse Entailment and is the first top-down ilp system that allows background theories and hypotheses to be normal logic programs. the approach relies on mapping an ilp problem into an equivalent ALP one. this enables the use of established ALP proof procedures and the specification of richer language bias with integrity constraints. the mapping provides a principled search space for an ilp problem, over which an abductive search is used to compute inductive solutions.

关键词： inductive logic programming Abductive logic programming Non-monotonic Reasoning

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the effect of relational background knowledge on learning of protein three-dimensional fold signatures

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MACHINE LEARNING 2001年第1-2期43卷 81-95页

作者： Turcotte, M Muggleton, SH Sternberg, MJE Imperial Canc Res Fund Biomolec Modelling Lab London WC2A 3PX England Univ York Dept Comp Sci York YO1 5DD N Yorkshire England

As a form of Machine Learning the study of inductive logic programming (ilp) is motivated by a central belief: relational description languages are better tin terms of accuracy and understandability) than propositional ones for certain real-world applications. this claim is investigated here for a particular application in structural molecular biology, that of constructing readable descriptions of the major protein folds. To the authors' knowledge Machine Learning has not previously been applied systematically to this task. In this application, the domain expert (third author) identified a natural divide between essentially propositional features and more structurally-oriented relational ones. the following null hypotheses are tested: 1) for a given ilp system (Progol) provision of relational background knowledge does not increase predictive accuracy, 2) a good propositional learning system (C5.0) without relational background knowledge will outperform Progol with relational background knowledge, 3) relational background knowledge does not produce improved explanatory insight. Null hypotheses 1) and 2) are both refuted on cross-validation results carried out over 20 of the most populated protein folds. Hypothesis 3 is refuted by demonstration of various insightful rules discovered only in the relationally-oriented learned rules.

关键词： inductive logic programming scientific discovery protein fold

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