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 26 papers from the inductivelogicprogramming - 14thinternationalconference, ilp 2004. the topics discussed include: automated synthesis of data analysis programs: learning in logic;at the i...
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the proceedings contain 26 papers from the inductivelogicprogramming - 14thinternationalconference, ilp 2004. the topics discussed include: automated synthesis of data analysis programs: learning in logic;at the interface of inductivelogicprogramming and statistics;from promising to profitable applications of ilp: a case study in drug discovery;systems biology: a new challenge for ilp;macro-operators revisited in inductivelogicprogramming;bottom-up ilp using large refinement steps;on the effect of caching in recursive theory learning;learning an approximation to inductivelogicprogramming clause evaluation;on avoiding redundancy in inductivelogicprogramming;and learning logic programs with annotated disjunctions.
the proceedings contain 26 papers from the conference on inductivelogicprogramming 15thinternationalconference, ilp 2005. the topics discussed include: guiding inference through relational reinforcement learning;c...
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the proceedings contain 26 papers from the conference on inductivelogicprogramming 15thinternationalconference, ilp 2005. the topics discussed include: guiding inference through relational reinforcement learning;converting semantic meta-knowledge into inductive bias;distance based generaliztion;automatic induction of abduction and abstraction theories from observations;strategies to parallelize ilp systems;inducing casual laws by regular inferece;spatial clustering of structured objects;predicate selection for structural decision trees;inductive equivalence of logic programs;and a study of applying dimensionality reduction to restrict the size of a hypothesis space.
the proceedings contain 26 papers. the special focus in this conference is on inductivelogicprogramming. the topics include: Automated synthesis of data analysis programs;at the interface of inductivelogic programm...
ISBN:
(纸本)9783540229414
the proceedings contain 26 papers. the special focus in this conference is on inductivelogicprogramming. the topics include: Automated synthesis of data analysis programs;at the interface of inductivelogicprogramming and statistics;from promising to profitable applications of ilp;experiences with extracting relations from biomedical text;macro operators revisited in inductivelogicprogramming;bottom-up ilp using large refinement steps;on the effect of caching in recursive theory learning;efficiently scaling foil for multi-relational data mining of large datasets;learning an approximation to inductivelogicprogramming clause evaluation;learning ensembles of first-order clauses for recall precision curves;automatic induction of first-order logic descriptors type domains from observations;on avoiding redundancy in inductivelogicprogramming;learning goal hierarchies from structured observations and expert annotations;an efficient algorithm for reducing clauses based on constraint satisfaction techniques;improving rule evaluation using multitask learning;learning logic programs with annotated disjunctions;a simulated annealing framework for ilp;modelling inhibition in metabolic pathways through abduction and induction;first order random forests with complex aggregates;a Monte Carlo study of randomised restarted search in ilp and learning, logic, and probability.
this book constitutes the thoroughly refereed post-conference proceedings of the 24thinternationalconference on inductivelogicprogramming, ilp 2014, held in Nancy, France, in September 2014. the 14 revised papers ...
ISBN:
(纸本)9783319237077
this book constitutes the thoroughly refereed post-conference proceedings of the 24thinternationalconference on inductivelogicprogramming, ilp 2014, held in Nancy, France, in September 2014. the 14 revised papers presented were carefully reviewed and selected from 41 submissions. the papers focus on topics such as the inducing of logic programs, learning from data represented withlogic, multi-relational machine learning, learning from graphs, and applications of these techniques to important problems in fields like bioinformatics, medicine, and text mining.
the proceedings contain 14 papers. the special focus in this conference is on inductivelogicprogramming. the topics include: Relational kernel-based grasping with numerical features;complex aggregates within random ...
ISBN:
(纸本)9783319405650
the proceedings contain 14 papers. the special focus in this conference is on inductivelogicprogramming. the topics include: Relational kernel-based grasping with numerical features;complex aggregates within random forests;distributed parameter learning for probabilistic ontologies;meta-interpretive learning of data transformation programs;statistical relational learning with soft quantifiers;ontology learning from interpretations in lightweight description logics;constructing markov logic networks from first-order default rules;a note on mining all graphs;processing markov logic networks with GPUs;using ilp to identify pathway activation patterns in systems biology;an algebraic prolog for kernel programming;an exercise in declarative modeling for relational query mining;learning inference by induction and identification of transition models of biological systems in the presence of transition noise.
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.
this paper explores a hybrid approach to the multimodal co-construction of explanations for robot faults, integrating inductivelogicprogramming (ilp) and Large Language Models (LLMs). As AI and robotics continue to ...
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ISBN:
(纸本)9798400704635
this paper explores a hybrid approach to the multimodal co-construction of explanations for robot faults, integrating inductivelogicprogramming (ilp) and Large Language Models (LLMs). As AI and robotics continue to permeate various aspects of daily life, the ability of these systems to explain their actions and failures is crucial for fostering user trust and ensuring safe interactions. We propose a framework that combines the interpretability of ilp, which generates logical rules from data, withthe linguistic capabilities of LLMs, which provide natural language explanations. this approach enables the generation of coherent, contextually appropriate explanations that can be tailored to the needs of users.
the integration of abduction and induction has lead to a variety of non-monotonic ilp systems. XHAIL is one of these systems, in which abduction is used to compute hypotheses that subsume Kernel Sets. On the other han...
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the integration of abduction and induction has lead to a variety of non-monotonic ilp systems. XHAIL is one of these systems, in which abduction is used to compute hypotheses that subsume Kernel Sets. On the other hand, Peircebayes is a recently proposed logic-based probabilistic programming approach that combines abduction with parameter learning to learn distributions of most likely explanations. In this paper, we propose an approach for integrating probabilistic inference withilp. the basic idea is to redefine the inductive task of XHAIL as a statistical abduction, and to use Peircebayes to learn probability distribution of hypotheses. An initial evaluation of the proposed algorithm is given using synthetic data.
State-of-the-art theta-subsumption engines like Django (C) and Resumer2 (Java) are implemented in imperative languages. Since theta-subsumption is inherently a logic problem, in this paper we explore how to e ffi cien...
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ISBN:
(纸本)9783939897170
State-of-the-art theta-subsumption engines like Django (C) and Resumer2 (Java) are implemented in imperative languages. Since theta-subsumption is inherently a logic problem, in this paper we explore how to e ffi ciently implement it in Prolog. theta-subsumption is an important problem in computational logic and particularly relevant to the inductivelogicprogramming (ilp) community as it is at the core of the hypotheses coverage test which is often the bottleneck of an ilp system. Also, since most of those systems are implemented in Prolog, they can immediately take advantage of a Prolog based theta-subsumption engine. We present a relatively simple (approximate to 1000 lines in Prolog) but e ffi cient and general theta-subsumption engine, Subsumer. Crucial to Subsumer's performance is the dynamic and recursive decomposition of a clause in sets of independent components. Also important are ideas borrowed from constraint programmingthat empower Subsumer to e ffi ciently work on clauses with up to several thousand literals and several dozen distinct variables. Using the notoriously challenging Phase Transition dataset we show that, cputime wise, Subsumer clearly outperforms the Django subsumption engine and is competitive withthe more sophisticated, state-of-the-art, Resumer2. Furthermore, Subsumer's memory requirements are only a small fraction of those engines and it can handle arbitrary Prolog clauses whereas Django and Resumer2 can only handle Datalog clauses.
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