the proceedings contain 16 papers. the special focus in this conference is on inductivelogicprogramming. the topics include: A new algorithm for learning range restricted horn expressions;a refinement operator for d...
ISBN:
(纸本)354067795X
the proceedings contain 16 papers. the special focus in this conference is on inductivelogicprogramming. the topics include: A new algorithm for learning range restricted horn expressions;a refinement operator for description logics;executing query packs in ilp;a logical database mining querylan guage;induction of recursive theories in the normal ilp setting;extending k-means clustering to first-order representations;theory completion using inverse entailment;solving selection problems using preference relation based on bayesian learning;concurrent execution of optimal hypothesis search for inverse entailment;using ilp to improve planning in hierarchical reinforcement learning;inverse entailment in nonmonotonic logic programs;a note on two simple transformations for improving the efficiency of an ilp system;searching the subsumption lattice by a genetic algorithm and new conditions for the existence of least generalizations under relative subsumption.
the proceedings contain 10 papers. the special focus in this conference is on . the topics include: Large-Scale Assessment of Deep Relational Machines;how Much Can Experimental Cost Be Reduced in Active Learning of Ag...
ISBN:
(纸本)9783319999593
the proceedings contain 10 papers. the special focus in this conference is on . the topics include: Large-Scale Assessment of Deep Relational Machines;how Much Can Experimental Cost Be Reduced in Active Learning of Agent Strategies?;diagnostics of Trains with Semantic Diagnostics Rules;the game of bridge: A challenge for ilp;Sampling-Based SAT/ASP Multi-model Optimization as a Framework for Probabilistic Inference;Explaining Black-Box Classifiers withilp – Empowering LIME with Aleph to Approximate Non-linear Decisions with Relational Rules;learning Dynamics with Synchronous, Asynchronous and General Semantics;was the Year 2000 a Leap Year? Step-Wise Narrowing theories with Metagol.
the proceedings contain 10 papers. the special focus in this conference is on inductivelogicprogramming. the topics include: Estimation-based search space traversal in Pilp environments;inductivelogicprogramming m...
ISBN:
(纸本)9783319633411
the proceedings contain 10 papers. the special focus in this conference is on inductivelogicprogramming. the topics include: Estimation-based search space traversal in Pilp environments;inductivelogicprogramming meets relational databases;online structure learning for traffic management;learning through advice-seeking via transfer;distributional learning of regular formal graph system of bounded degree;learning relational dependency networks for relation extraction;towards nonmonotonic relational learning from knowledge graphs;learning predictive categories using lifted relational neural networks and generation of near-optimal solutions using ilp-guided sampling.
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,...
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 withilp;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.
the field of inductivelogicprogramming (ilp) has made steady progress, since the first ilp workshop in 1991, based on a balance of developments in theory, implementations and applications. More recently there has be...
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the field of inductivelogicprogramming (ilp) has made steady progress, since the first ilp workshop in 1991, based on a balance of developments in theory, implementations and applications. More recently there has been an increased emphasis on Probabilistic ilp and the related fields of Statistical Relational Learning (SRL) and Structured Prediction. the goal of the current paper is to consider these emerging trends and chart out the strategic directions and open problems for the broader area of structured machine learning for the next 10 years.
inductivelogicprogramming (ilp) is built on a foundation laid by research in machine learning and computational logic. Armed withthis strong foundation, ilp has been applied to important and interesting problems in...
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inductivelogicprogramming (ilp) is built on a foundation laid by research in machine learning and computational logic. Armed withthis strong foundation, ilp has been applied to important and interesting problems in the life sciences, engineering and the arts. this paper begins by briefly reviewing some example applications, in order to illustrate the benefits of ilp. In turn, the applications have brought into focus the need for more research into specific topics. We enumerate and elaborate five of these: (1) novel search methods;(2) incorporation of explicit probabilities;(3) incorporation of special-purpose reasoners;(4) parallel execution using commodity components;and (5) enhanced human interaction. It is our hypothesis that progress in each of these areas can greatly improve the contributions that can be made withilp;and that, with assistance from research workers in other areas, significant progress in each of these areas is possible.
Inverse entailment (IE) is known as a technique for finding inductive hypotheses in Horn theories. When a background theory is nonmonotonic, however, IE is not applicable in its present form. the purpose of this paper...
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this article presents a combination of unsupervised and supervised learning techniques for the generation of word segmentation rules from a raw list of words. First, a language bias for word se mentation is introduced...
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this article presents a combination of unsupervised and supervised learning techniques for the generation of word segmentation rules from a raw list of words. First, a language bias for word se mentation is introduced and a simple genetic algorithm is used in the search for a segmentation that corresponds to the best bias value. In the second phase, the words segmented by the genetic algorithm are used as an input for the first order decision list learner CLOG. the result is a set of first order rules which can be used for segmentation of unseen words. When applied on either the training data or unseen data, these rules produce segmentations which are linguistically meaningful, and to a large degree conforming to the annotation provided.
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 t...
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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.
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