检索结果-内蒙古大学图书馆

PageRank, ProPPR, and Stochastic logic Programs 1

24th international conference on inductive logic programming (ilp)

作者： Van Daele, Dries Kimmig, Angelika De Raedt, Luc Katholieke Univ Leuven Dept Comp Sci Leuven Belgium

ISBN: (数字)9783319237084

ISBN: (纸本)9783319237084;9783319237077

A key feature of ProPPR, a recent probabilistic logic language inspired by stochastic logic programs (SLPs), is its use of personalized PageRank for efficient inference. We adopt this view of probabilistic inference as a random walk over a graph constructed from a labeled logic program to investigate the relationship between these two languages, showing that the differences in semantics rule out direct, generally applicable translations between them.

关键词： Probabilistic logics

来源：评论

学校读者我要写书评

暂无评论

Nonmonotonic Learning in Large Biological Networks 1

引用

24th international conference on inductive logic programming (ilp)

作者： Bragaglia, Stefano Ray, Oliver Univ Bristol Dept Comp Sci Bristol BS8 1TH Avon England

ISBN: (数字)9783319237084

ISBN: (纸本)9783319237084;9783319237077

this paper introduces a new open-source implementation of a nonmonotonic learning method called XHAIL and shows how it can be used for abductive and inductive inference on metabolic networks that are many times larger than could be handled by the preceding prototype. We summarise several implementation improvements that increase its efficiency and we introduce an extended form of language bias that further increases its usability. We investigate the system's scalability in a case study involving real data previously collected by a Robot Scientist and show how it led to the discovery of an error in a whole-organism model of yeast metabolism.

关键词： ilp ALP ASP Metabolic networks Completion Revision

来源：评论

学校读者我要写书评

暂无评论

Probabilistic logic programming for hybrid relational domains

引用

MACHINE LEARNING 2016年第3期103卷 407-449页

作者： Nitti, Davide De Laet, Tinne De Raedt, Luc Katholieke Univ Leuven Dept Comp Sci Leuven Belgium Katholieke Univ Leuven Fac Engn Sci Leuven Belgium

We introduce a probabilistic language and an efficient inference algorithm based on distributional clauses for static and dynamic inference in hybrid relational domains. Static inference is based on sampling, where the samples represent (partial) worlds (with discrete and continuous variables). Furthermore, we use backward reasoning to determine which facts should be included in the partial worlds. For filtering in dynamic models we combine the static inference algorithm with particle filters and guarantee that the previous partial samples can be safely forgotten, a condition that does not hold in most logical filtering frameworks. Experiments show that the proposed framework can outperform classic sampling methods for static and dynamic inference and that it is promising for robotics and vision applications. In addition, it provides the correct results in domains in which most probabilistic programming languages fail.

关键词： Probabilistic programming Statistical relational learning Discrete and continuous distributions Particle filter Likelihood weighting logic programming

来源：评论

学校读者我要写书评

暂无评论

Mode-directed inverse entailment for full clausal theories

引用

Joint conference of the 17th international conference on inductive logic programming/24th international conference on Machine Learning

作者： Ray, Oliver Inoue, Katsumi Univ Bristol Bristol BS8 1TH Avon England Natl Inst Informat Tokyo Japan

ISBN: (纸本)9783540784685

Mode declarations are a successful form of language bias in explanatory ilp. But, while they are heavily used in Horn systems, they have yet to be similarly exploited in more expressive clausal settings. this paper presents a mode-directed ilp procedure for full clausal logic. It employs a first-order inference engine to abductively and inductively explain a set of examples with respect to a background theory. Each stage of hypothesis formation is guided by mode declarations using a generalisation of efficient Horn clause techniques for inverting entailment. Our approach exploits language bias more effectively than previous non-Horn ilp methods and avoids the need for interactive user assistance.

关键词： Formal languages

来源：评论

学校读者我要写书评

暂无评论

Learning Prime Implicant Conditions from Interpretation Transition 1

引用

24th international conference on inductive logic programming (ilp)

作者： Ribeiro, Tony Inoue, Katsumi Grad Univ Adv Studies Sokendai Chiyoda Ku Tokyo 1018430 Japan Res Org Informat & Syst Natl Inst Informat Chiyoda Ku Tokyo 1018430 Japan

ISBN: (数字)9783319237084

ISBN: (纸本)9783319237084;9783319237077

In a previous work we proposed a framework for learning normal logic programs from transitions of interpretations. Given a set of pairs of interpretations (I, J) such that J = T-P (I), where T-P is the immediate consequence operator, we infer the program P. Here we propose a new learning approach that is more efficient in terms of output quality. this new approach relies on specialization in place of generalization. It generates hypotheses by specialization from the most general clauses until no negative transition is covered. Contrary to previous approaches, the output of this method does not depend on variables/transitions ordering. the new method guarantees that the learned rules are minimal, that is, the body of each rule constitutes a prime implicant to infer the head.

关键词： Dynamical systems Boolean networks Attractors Supported models Learning from interpretation inductive logic programming

来源：评论

学校读者我要写书评

暂无评论

the Most Probable Explanation for Probabilistic logic Programs with Annotated Disjunctions 1

引用

24th international conference on inductive logic programming (ilp)

作者： Shterionov, Dimitar Renkens, Joris Vlasselaer, Jonas Kimmig, Angelika Meert, Wannes Janssens, Gerda KULeuven Leuven Belgium

ISBN: (数字)9783319237084

ISBN: (纸本)9783319237084;9783319237077

Probabilistic logic languages, such as ProbLog and CP-logic, are probabilistic generalizations of logic programming that allow one to model probability distributions over complex, structured domains. their key probabilistic constructs are probabilistic facts and annotated disjunctions to represent binary and mutli-valued random variables, respectively. ProbLog allows the use of annotated disjunctions by translating them into probabilistic facts and rules. this encoding is tailored towards the task of computing the marginal probability of a query given evidence (MARG), but is not correct for the task of finding the most probable explanation (MPE) with important applications e.g., diagnostics and scheduling. In this work, we propose a new encoding of annotated disjunctions which allows correct MARG and MPE. We explore from both theoretical and experimental perspective the trade-off between the encoding suitable only for MARG inference and the newly proposed (general) approach.

关键词： Probabilistic logic programming Statistical relational learning Most Probable Explanation logic programs with annotated disjunctions ProbLog

来源：评论

学校读者我要写书评

暂无评论

Learning probabilistic logic models from probabilistic examples

引用

Joint conference of the 17th international conference on inductive logic programming/24th international conference on Machine Learning

作者： Chen, Jianzhong Muggleton, Stephen Santos, Jose Univ London Imperial Coll Sci Technol & Med Dept Comp London SW7 2AZ England

ISBN: (纸本)3540784683

We revisit an application developed originally using abductive inductive logic programming (ilp) for modeling inhibition in metabolic networks. the example data was derived from studies of the effects of toxins on rats using Nuclear Magnetic Resonance (NMR) time-trace analysis of their biofluids together with background knowledge representing a subset of the Kyoto Encyclopedia of Genes and Genomes (KEGG). We now apply two Probabilistic ilp (Pilp) approaches-abductive Stochastic logic Programs (SLPs) and programming In Statistical modeling (PRISM) to the application. Both approaches support abductive learning and probability predictions. Abductive SLPs are a Pilp framework that provides possible worlds semantics to SLPs through abduction. Instead of learning logic models from non-probabilistic examples as done in ilp, the Pilp approach applied in this paper is based on a general technique for introducing probability labels within a standard scientific experimental setting involving control and treated data. Our results demonstrate that the Pilp approach provides a way of learning probabilistic logic models from probabilistic examples, and the Pilp models learned from probabilistic examples lead to a significant decrease in error accompanied by improved insight from the learned results compared with the Pilp models learned from non-probabilistic examples.

关键词： probabilistic inductive logic programming stochastic logic programs abduction probabilistic examples

来源：评论

学校读者我要写书评

暂无评论

TopLog: ilp Using a logic Program Declarative Bias

TopLog: ILP Using a Logic Program Declarative Bias

引用

24th international conference on logic programming (ICLP)

作者： Muggleton, Stephen H. Santos, Jose C. A. Tamaddoni-Nezhad, Alireza Univ London Imperial Coll Sci Technol & Med Dept Comp London SW7 2AZ England

ISBN: (纸本)9783540899815

this paper introduces a new inductive, logic programming. (ilp) framework called Top Directed Hypothesis Derivation (TDHD) In this framework each hypothesised clause must be derivable from a given logic program called top theory (T). the top theory call be viewed as a declarative bias which defines the hypothesis space. this replaces the metalogical mode, statements which are used ill many ilp) systems. Firstly we present a theoretical framework for TDHD and show that standard SLID derivation call be used to efficiently derive hypotheses from) T. Secondly. we present a prototype implementation of TDHD within a new ilp system called TopLog. thirdly. we show that the accuracy and efficiency of TopLog, oil several benchmark datasets, is competitive with a state of the art ilp system like Aleph.

关键词： Computer circuits

来源：评论

学校读者我要写书评

暂无评论

TopLog: ilp Using a logic Program Declarative Bias

TopLog: ILP Using a Logic Program Declarative Bias

引用

24th international conference on logic programming (ICLP)

作者： Santos, Jose Carlos Almeida Univ London Imperial Coll Sci Technol & Med Dept Comp London England

ISBN: (纸本)9783540899815

Although inductive logic programming can have other usages such as program synthesis, it is normally used as a logic based supervised machine learning algorithm. the usual setting for its application is, given: 1) a set of background knowledge facts B, 2) a set of examples E, find: a set of hypotheses H, such that B,H∈ ∈E . H, the induced model, is a set of Horn rules thus being easily comprehensible by a human. © 2008 Springer Berlin Heidelberg.

关键词： Computer circuits

来源：评论

学校读者我要写书评

暂无评论

inductive Learning Using Constraint-Driven Bias 1

引用

24th international conference on inductive logic programming (ilp)

作者： Athakravi, Duangtida Alrajeh, Dalal Broda, Krysia Russo, Alessandra Satoh, Ken Univ London Imperial Coll Sci Technol & Med London England Natl Inst Informat Tokyo Japan

ISBN: (数字)9783319237084

ISBN: (纸本)9783319237084;9783319237077

Heuristics such as the Occam Razor's principle have played a significant role in reducing the search for solutions of a learning task, by giving preference to most compressed hypotheses. For some application domains, however, these heuristics may become too weak and lead to solutions that are irrelevant or inapplicable. this is particularly the case when hypotheses ought to conform, within the scope of a given language bias, to precise domain-dependent structures. In this paper we introduce a notion of inductive learning through constraint-driven bias that addresses this problem. Specifically, we propose a notion of learning task in which the hypothesis space, induced by its mode declaration, is further constrained by domain-specific denials, and acceptable hypotheses are (brave inductive) solutions that conform with the given domain-specific constraints. We provide an implementation of this new learning task by extending the ASPAL learning approach and leveraging on its meta-level representation of hypothesis space to compute acceptable hypotheses. We demonstrate the usefulness of this new notion of learning by applying it to two class of problems - automated revision of software system goals models and learning of stratified normal programs.

关键词： inductive logic programming Answer set programming Meta-level constraints

来源：评论

学校读者我要写书评

暂无评论

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案：

请选择收藏分类：

通借通还

建议与咨询 留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案： 新增检索档案 确定 取消

请选择收藏分类： 新增自定义分类 确定 取消

通借通还

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

请选择保存的检索档案：

请选择收藏分类：