一种自顶向下和自底向上相结合的ILP算法

归纳逻辑程序设计(ILP)是机器学习的一个重要分支,给定一个样例集和相关背景知识,ILP研究如何构建与其相一致的逻辑程序,这些逻辑程序由有限一阶子句组成。文章描述了一种综合当前一些ILP方法多方面优势的算法ICCR,ICCR溶合了以FOIL为代表的自顶向下搜索策略和以GOLEM为代表的自底向上搜索策略,并能根据需要发明新谓词、学习递归逻辑程序,对比实验表明,对相同的样例及背景知识,ICCR比FOIL和GOLEM能学到精度更高的目标逻辑程序。     

归纳逻辑程序设计中的优化问题研究

归纳逻辑程序设计的核心问题是如何从背景知识中优选谓词构造满足约束的归纳假设,按Ｏｃｃａｍ准则,满足约束的最精简归纳假设为优,但迄今归纳逻辑程序设计中精简归纳假设构造的计算复杂性尚未解决。     

基于遗传算法的一阶规则获取

基于一阶谓词逻辑的一阶规则挖掘方法可处理多表挖掘且具有强的知识表达能力,成为数据挖掘(DM)技术中一种渐受重视的新方法。为了解决现有方法规则获取的性能瓶颈问题,该文提出了一种新的基于遗传算法的一阶规则挖掘算法(GILP)。实验结果表明,GILP算法能有效地挖掘一阶规则。     

自然语言处理中的逻辑文法

逻辑文法是指用谓词逻辑来表达的文法。它属于计算语言学的范畴,是逻辑程序设计和现代语言学相结合的产物。在人工智能的自然语言处理等领域里,谓词逻辑通常用来描述知识和逻辑推理。70年代,逻辑用于程序设计的思想以Prolog语言的形式投入应用以来,谓词逻辑不再仅仅用于描述这些问题,还作为逻辑程序设计的工具去描述解决问题的过程。PROLOG语言使得逻辑和程序设计这     

利用谓词/变迁网证明的一阶谓词逻辑命题

研究了证明一般的一阶谓词逻辑命题的方法，根据网逻辑的思想，利用谓词/变迁网对一般形式的一阶谓词逻辑命题进行了图形表示，提出了2种一阶谓词逻辑命题的证明方法：图形证明法和矩阵证明法。举出一个实际的例子来说明证明思路。     

基于动态描述逻辑DDL的动作理论

基于一阶谓词逻辑或高阶逻辑的动作理论与采用命题语言的动作理论之间存在一个关于描述和推理能力的鸿沟;作为描述逻辑的动态扩展,动态描述逻辑DDL为基于描述逻辑的动作刻画和推理提供了一种途径.系统地研究了基于DDL的动作表示和推理问题.首先,在应用描述逻辑对静态领域知识进行刻画的基础上,引入带参数的原子动作定义式和带参数的复...     

一种基于Markov逻辑网的中文地理名称实体解析方法

根据Markov逻辑网融合一阶谓词逻辑和概率图模型的复杂性及不确定性处理能力的优点,提出将Markov逻辑网和基于本体与WEB搜索的属性抽取算法相结合的命名实体解析方法(MLN_AENER),解决一般基于Markov逻辑网的实体解析方法对非结构化的命名实体解析效果不佳的问题,并将该方法针对中文地理名称解析问题进行相应设计和实验。实验结果表明该方法具有较好的解析效果。     

高阶逻辑下知识表示与聚类方法的研究*

针对一阶逻辑在复杂结构数据环境中存在模式搜索空间庞大和不能发明新谓词的缺点,提出了使用类型化的高阶逻辑知识表示语言Escher去表示各种复杂结构的数据,利用其强类型语法有效地约束知识发现过程中模式的搜索空间和高阶的特点去解决新谓词构造的问题。设计了以Escher为基础的复杂结构数据中的知识发现过程和基于复杂结构数据的聚类算法,并以实验验证了其有效性。     

基于支持集策略的归结推理方法的实现及其优化

人工智能原理中,基于一阶谓词逻辑下的归结推理方法可以在机器上实现"自动定理证明以及问题的求解".本文探讨了基于支持集策略的归结推理方法的实现,同时应用启发性搜索的策略,对该推理方法进行了优化.     

Parallel and Sequential Algorithms for Data Mining Using Inductive Logic

Inductive logic is a research area in the intersection of machine learning and logic programming, and has been increasingly applied to data mining. Inductive logic studies learning from examples, within the framework provided by clausal logic. It provides a uniform and expressive means of representation: examples, background knowledge, and induced theories are all expressed in first-order logic. Such an expressive representation is computationally expensive, so it is natural to consider improving the performance of inductive logic data mining using parallelism. We present a parallelization technique for inductive logic, and implement a parallel version of a core inductive logic programming system: Progol. The technique provides perfect partitioning of computation and data access and communication requirements are small, so almost linear speedup is readily achieved. However, we also show why the information flow of the technique permits superlinear speedup over the standard sequential algorithm. Performance results on several datasets and platforms are reported. The results have wider implications for the design on parallel and sequential data-mining algorithms. Received 30 August 2000 / Revised 30 January 2001 / Accepted in revised form 16 May 2001     

持续时态数据挖掘的研究

基于一阶线性时态逻辑。形式化定义时态数据挖掘中的主要概念。利用线性状态结构对每个时间点上的一阶语言符号进行赋值。并度量公式的真值范围．按照挖掘段概念．开发持续挖掘过程模型，用于归纳局部一阶规则与推导高阶规则．基于信息扩散原理．提出一阶规则的度量值估计方法和规则泛化算法．最后通过算例说明了扩散估计和算法的有效性．     

Discovering rules to design newspapers: An inductive constraint logic programming approach

Inductive logic programming combines both machine learning and logic programming techniques. ILP uses first-order predicate logic restricted to Horn clauses as an underlying language. Thus, programs induced by an ILP system inherit the classical limitations of PROLOG programs. Constraint logic programming avoids some of the limitations of logic programming, and so ILP aims to induce programs that employ this paradigm. Current ILP systems that induce constrained logic programs extend systems based on the normal semantics ofILP. In this article we introduce IC-Log, a new system that induces constrained logic programs and relies on an extension ofa nonmonotonic semantics-based system. We then present an application of IC-Log in the field ofcomputer-aided publishing.     

推广规则后一阶逻辑公式的准真度

计量逻辑理论是逻辑概念程度化研究方向的一个重要分支。但目前计量谓词逻辑的相关研究中，都不曾涉及推广规则。一阶逻辑公式的准真度理论是计量谓词逻辑的一个重要的研究成果，讨论经过推广规则后，一阶逻辑公式准真度的变化情况，证明经过推广规则后，一阶逻辑公式在基于准真度的一阶逻辑公式集的分类中类别不变。     

Fast relational learning using bottom clause propositionalization with artificial neural networks

Relational learning can be described as the task of learning first-order logic rules from examples. It has enabled a number of new machine learning applications, e.g. graph mining and link analysis. Inductive Logic Programming (ILP) performs relational learning either directly by manipulating first-order rules or through propositionalization, which translates the relational task into an attribute-value learning task by representing subsets of relations as features. In this paper, we introduce a fast method and system for relational learning based on a novel propositionalization called Bottom Clause Propositionalization (BCP). Bottom clauses are boundaries in the hypothesis search space used by ILP systems Progol and Aleph. Bottom clauses carry semantic meaning and can be mapped directly onto numerical vectors, simplifying the feature extraction process. We have integrated BCP with a well-known neural-symbolic system, C-IL²P, to perform learning from numerical vectors. C-IL²P uses background knowledge in the form of propositional logic programs to build a neural network. The integrated system, which we call CILP++, handles first-order logic knowledge and is available for download from Sourceforge. We have evaluated CILP++ on seven ILP datasets, comparing results with Aleph and a well-known propositionalization method, RSD. The results show that CILP++ can achieve accuracy comparable to Aleph, while being generally faster, BCP achieved statistically significant improvement in accuracy in comparison with RSD when running with a neural network, but BCP and RSD perform similarly when running with C4.5. We have also extended CILP++ to include a statistical feature selection method, mRMR, with preliminary results indicating that a reduction of more than 90 % of features can be achieved with a small loss of accuracy.     

一种基于模糊逻辑规则的数据挖掘方法

分析了模糊逻辑规则的形成过程，介绍了模糊推理方法，提出了基于模糊规则化的数据挖掘方法。由分析可以看出，基于模糊逻辑规则的方法能从大量的数据集合中有效地发现有价值但不明显的信息并挖掘出有价值的信息。例如，在银行借贷中，根据数据库中的数据对借贷方进行评估，挖掘出影响贷款安全的有关单位信息等。     

Filtering Multi-Instance Problems to Reduce Dimensionality in Relational Learning

Attribute-value based representations, standard in today's data mining systems, have a limited expressiveness. Inductive Logic Programming provides an interesting alternative, particularly for learning from structured examples whose parts, each with its own attributes, are related to each other by means of first-order predicates. Several subsets of first-order logic (FOL) with different expressive power have been proposed in Inductive Logic Programming (ILP). The challenge lies in the fact that the more expressive the subset of FOL the learner works with, the more critical the dimensionality of the learning task. The Datalog language is expressive enough to represent realistic learning problems when data is given directly in a relational database, making it a suitable tool for data mining. Consequently, it is important to elaborate techniques that will dynamically decrease the dimensionality of learning tasks expressed in Datalog, just as Feature Subset Selection (FSS) techniques do it in attribute-value learning. The idea of re-using these techniques in ILP runs immediately into a problem as ILP examples have variable size and do not share the same set of literals. We propose here the first paradigm that brings Feature Subset Selection to the level of ILP, in languages at least as expressive as Datalog. The main idea is to first perform a change of representation, which approximates the original relational problem by a multi-instance problem. The representation obtained as the result is suitable for FSS techniques which we adapted from attribute-value learning by taking into account some of the characteristics of the data due to the change of representation. We present the simple FSS proposed for the task, the requisite change of representation, and the entire method combining those two algorithms. The method acts as a filter, preprocessing the relational data, prior to the model building, which outputs relational examples with empirically relevant literals. We discuss experiments in which the method was successfully applied to two real-world domains.     

基于高阶逻辑的复杂结构归纳学习研究

归纳学习的目的在于发现样例与离散的类之间的映射关系,样例及归纳的映射都需用某个形式化语言描述.归纳学习器采用的形式化语言经历了属性-值语言、一阶逻辑、类型化的高阶逻辑三个阶段,后者能克服前二者在知识表达及学习过程中的很多缺点.本文首先阐述了基于高阶逻辑的复杂结构归纳学习产生的历史背景;其次介绍了基于高阶逻辑的编程语言--Escher的知识描述形式及目前已提出的三种学习方法;复杂结构的归纳学习在机器学习领域的应用及如何解决一些现实问题的讨论随后给出; 最后分析了复杂结构归纳学习的研究所面临的挑战性问题.     

知识发现中的因果关联规则挖掘研究

因果关联规则是知识库中一类重要的知识类型,具有重要的应用价值。首先对因果关系的特殊性质进行了分析,然后基于语言场和广义归纳逻辑因果模型,从表示、挖掘、评价和应用几方面,对因果关联规则的研究进行了详细论述。并在此基础上提出了隐含因果关联规则的概念。通过语言场和推理机制的运用,使因果关联规则这一重要知识形式的挖掘和评价过程具有良好的逻辑性和扩张性。     

Hammer for Coq: Automation for Dependent Type Theory

Hammers provide most powerful general purpose automation for proof assistants based on HOL and set theory today. Despite the gaining popularity of the more advanced versions of type theory, such as those based on the Calculus of Inductive Constructions, the construction of hammers for such foundations has been hindered so far by the lack of translation and reconstruction components. In this paper, we present an architecture of a full hammer for dependent type theory together with its implementation for the Coq proof assistant. A key component of the hammer is a proposed translation from the Calculus of Inductive Constructions, with certain extensions introduced by Coq, to untyped first-order logic. The translation is “sufficiently” sound and complete to be of practical use for automated theorem provers. We also introduce a proof reconstruction mechanism based on an eauto-type algorithm combined with limited rewriting, congruence closure and some forward reasoning. The algorithm is able to re-prove in the Coq logic most of the theorems established by the ATPs. Together with machine-learning based selection of relevant premises this constitutes a full hammer system. The performance of the whole procedure is evaluated in a bootstrapping scenario emulating the development of the Coq standard library. For each theorem in the library only the previous theorems and proofs can be used. We show that 40.8% of the theorems can be proved in a push-button mode in about 40 s of real time on a 8-CPU system.