选择策略对遗传归纳逻辑程序设计收敛性能的影响

采用遗传算法（GA）作为归纳逻辑程序设计（ILP）的搜索策略，可以提高ILP方法的鲁棒性和适应性，文章简要叙述了对作者提出的遗传归纳逻辑程序设计（GILP）算法作的改进，测试了选择策略对GILP算法收敛性能的影响，采用不同的选择策略不会影响算法的最终收敛结果，但会产生不同的选择压力，导致算法具有不同的收敛速率。     

多关系数据挖掘方法研究

目前大多数数据挖掘方法是从单关系中发现模式,而多关系数据挖掘（MRDM）则可直接从关系数据库的多表中抽取有效模式。MRDM可以解决原有命题数据挖掘方法不能解决的问题,它不仅有更强的信息表示能力,可以表示和发现更复杂的模式,还可以在挖掘进程中有效地利用背景知识来提高挖掘效率和准确率。近年来,借鉴归纳逻辑程序设计（ILP）技术,已经形成许多多关系数据挖掘方法,如关系关联规则挖掘方法、关系分类聚类方法等。     

Web知识规则提取的FOIL算法改进

将一阶学习的FOIL算法应用到Web知识规则的提取是当前学习Web知识所普遍采用的方法．本文在FOIL算法的基础上进行了改进，提出了基于网页间联系的新的路径学习算法，使得原算法的稳定性和精确度都明显提高。     

关系tri-training:利用无标记数据学习一阶规则

针对目前归纳逻辑程序设计(inductive logic programming,ILP)系统要求训练数据充分且无法利用无标记数据的不足,提出了一种利用无标记数据学习一阶规则的算法——关系tri-training(relational-tri-training,R-tri-training)算法。该算法将基于命题逻辑表示的半监督学习算法tri-training的思想引入到基于一阶逻辑表示的ILP系统,在ILP框架下研究如何利用无标记样例信息辅助分类器训练。R-tri-training算法首先根据标记数据和背景知识初始化三个不同的ILP系统,然后迭代地用无标记样例对三个分类器进行精化,即如果两个分类器对一个无标记样例的标记结果一致,则在一定条件下该样例将被标记给另一个分类器作为新的训练样例。标准数据集上实验结果表明:R-tri-training能有效地利用无标记数据提高学习性能,且R-tri-training算法性能优于GILP(genetic inductive logic programming)、NFOIL、KFOIL和ALEPH。     

逻辑程序的实现策略(一)

Horn子句和上下文无关方法的相似性、对应性,使上下文无关文法的最有效算法——Earley算法的思想,应用于逻辑程序的实现。然而逻辑程序毕竟是一种面向问题的逻辑语言,是问题自动求解描述语言,因此它的实现算法兼有语言和问题求解系统两方面,这样的有智能特征的语言只用机械方法不可能对每个问题都达到最佳,只有用智能才能使之高效和完备。 本文给出了逻辑程序的一个实现算法。它是面向问题的。它是智能和并行结合,自顶向下分析和自底向上分析结合,宽度搜索和深度优先结合,数据驱动和需要驱动结合的算法。作者在智能实现逻辑程序方面提出了面向问题的四个策略,指出了要把智能引入逻辑程序的方向。     

基于图论的化学分子数据挖掘研究

近年来,人们提出了很多频繁图模式挖掘的算法。首先分析了贪婪搜索策略,然后对各种不同的图数据挖掘的方法进行比较。受购物篮分析的影响,基于ILP方法引起了人们的重视。如何修改各种不同的图数据挖掘方法以适用化学分子数据的挖掘是人们研究的热点问题。因为化学分子不仅是标准的图结构,而且它有典型的频繁环和链结构,还有一些频繁出现的代表原子类型的结点,所以在这个领域有一些特殊问题需要考虑。     

扩展的动态逻辑程序及其在产品个性化推荐中的应用

动态逻辑程序能很好的处理知识库更新问题, 但它不能描述和处理具有偏好的知识更新问题. 因此, 本文在动态逻辑程序的基础上, 提出了一种新的扩展的动态逻辑程序, 它通过对规则头部使用有序析取的方法使其能够描述和处理具有偏好的知识更新问题, 进一步增强了知识的表达和推理能力, 并且定义了其最优回答集语义. 同时将这种新的扩展的动态逻辑程序应用于产品推荐系统中, 使用户获得的推荐信息具有个性化特点, 达到个性化推荐的目的. 最后以一个产品个性化推荐实例讨论扩展的动态逻辑程序在产品个性化推荐中的应用.     

LP在语义Web数据挖掘研究中的应用

语义网络数据挖掘是基于语义网络环境的数据挖掘，它给数据挖掘技术的应用研究提出了新的课题。归纳逻辑程序设计是由机器学习与逻辑程序设计交叉所形成的一个研究领域，它为知识工程等人工智能的应用领域提供了新的强有力的技术支持。分析了现有几种常用数据挖掘技术在语义Web环境下应用的局限性，提出了采用归纳逻辑程序设计（ILP）作为语义Web上适合的数据挖掘技术，给出了应用这种技术的算法描述，通过具体实例验证了其可行性。     

求解多峰函数全局最优解的混合演化算法

1引言 多峰函数优化问题有着广泛的实际应用背景,而传统的搜索策略往往只能找到局部最优解,难以满足需要.遗传算法作为一种全局搜索策略,结合了达尔文适者生存的原则和随机搜索的思想[2,3],与传统搜索方式相比,既消除了解中的不适应因素,又利用了原有解中已有的知识,以极强的解决问题的能力和广泛的实用性渗透到科研和国民经济等各个领域.     

逻辑程序并行执行模型的模拟实验研究

本文介绍以SES-PIM系统为工具,对三种不同的逻辑程序执行模型进行对此模拟实验研究的结果。实验表明:PSOF模型能有效地开发确定性和非确定性逻辑程序的AND和OR两种并行性;PSOT模型却只能开发逻辑程序中的OR并行性;而SSOT模型不能开发逻辑程序中的并行性。对于本文使用的五个典型问题,PSOF模型的平均并行度是PSOT模型的3至6倍,是SSOT模型的3至19倍;PSOF模型的搜索速度是PSOT模型的2至6倍,是SSOT模型的3至13倍。     

归纳逻辑程序设计综述

归纳逻辑程序设计是由机器学习与逻辑程序设计交叉所形成的一个研究领域,是机器学习的前沿研究课题。该文首先从归纳逻辑程序设计的问题背景、类型划分和搜索程序子句三个方面介绍了归纳逻辑程序设计系统的概貌;然后结合实验室的相关研究工作,回顾了归纳逻辑程序设计研究的发展;之后介绍了归纳逻辑程序设计领域中需要深入研究的若干问题,并提出了新的解决思路;最后是总结,以引起读者对归纳逻辑程序设计领域研究的进一步关注。     

归纳逻辑程序设计方法综述

归纳逻辑程序设计是机器学习与逻辑程序设计交叉所形成的一个研究领域,克服了传统机器学习方法的两个主要限制：即知识表示的限制和背景知识利用的限制,成为机器学习的前沿研究课题。首先从归纳逻辑程序设计的产生背景、定义、应用领域及问题背景介绍了归纳逻辑程序设计系统的概貌,对归纳逻辑程序设计方法的研究现状进行了总结和分析,最后探讨了该领域的进一步的研究方向。     

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.     

基于归纳逻辑程序设计的学习方法及其实现的研究

归纳逻辑程序设计是机器学习领域中的一个新方法，它研究的是从实例和背景知识进行逻辑程序（新知识）的构造．本文介绍了归纳逻辑程序设计的基本理论和方法，并介绍了这种学习方法在专家系统中的应用情况．     

Evolutionary program induction directed by logic grammars

Program induction generates a computer program that can produce the desired behavior for a given set of situations. Two of the approaches in program induction are inductive logic programming (ILP) and genetic programming (GP). Since their formalisms are so different, these two approaches cannot be integrated easily, although they share many common goals and functionalities. A unification will greatly enhance their problem-solving power. Moreover, they are restricted in the computer languages in which programs can be induced. In this paper, we present a flexible system called LOGENPRO (The LOgic gramar-based GENetic PROgramming system) that uses some of the techniques of GP and ILP. It is based on a formalism of logic grammars. The system applies logic grammars to control the evolution of programs in various programming languages and represent context-sensitive information and domain-dependent knowledge. Experiments have been performed to demonstrate that LOGENPRO can emulate GP and GP with automatically defined functions (ADFs). Moreover, LOGENPRO can employ knowledge such as argument types in a unified framework. The experiments show that LOGENPRO has superior performance to that of GP and GP with ADFs when more domain-dependent knowledge is available. We have applied LOGENPRO to evolve general recursive functions for the even-n-parity problem from noisy training examples. A number of experiments have been performed to determine the impact of domain-specific knowledge and noise in training examples on the speed of learning.     

Scaling Up Inductive Logic Programming by Learning from Interpretations

When comparing inductive logic programming (ILP) and attribute-value learning techniques, there is a trade-off between expressive power and efficiency. Inductive logic programming techniques are typically more expressive but also less efficient. Therefore, the data sets handled by current inductive logic programming systems are small according to general standards within the data mining community. The main source of inefficiency lies in the assumption that several examples may be related to each other, so they cannot be handled independently.Within the learning from interpretations framework for inductive logic programming this assumption is unnecessary, which allows to scale up existing ILP algorithms. In this paper we explain this learning setting in the context of relational databases. We relate the setting to propositional data mining and to the classical ILP setting, and show that learning from interpretations corresponds to learning from multiple relations and thus extends the expressiveness of propositional learning, while maintaining its efficiency to a large extent (which is not the case in the classical ILP setting).As a case study, we present two alternative implementations of the ILP system TILDE (Top-down Induction of Logical DEcision trees): TILDEclassic, which loads all data in main memory, and TILDELDS, which loads the examples one by one. We experimentally compare the implementations, showing TILDELDS can handle large data sets (in the order of 100,000 examples or 100 MB) and indeed scales up linearly in the number of examples.     

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.     

Improving the efficiency of inductive logic programming systems

Inductive logic programming (ILP) is a sub‐field of machine learning that provides an excellent framework for multi‐relational data mining applications. The advantages of ILP have been successfully demonstrated in complex and relevant industrial and scientific problems. However, to produce valuable models, ILP systems often require long running times and large amounts of memory. In this paper we address fundamental issues that have direct impact on the efficiency of ILP systems. Namely, we discuss how improvements in the indexing mechanisms of an underlying logic programming system benefit ILP performance. Furthermore, we propose novel data structures to reduce memory requirements and we suggest a new lazy evaluation technique to search the hypothesis space more efficiently. These proposals have been implemented in the April ILP system and evaluated using several well‐known data sets. The results observed show significant improvements in running time without compromising the accuracy of the models generated. Indeed, the combined techniques achieve several order of magnitudes speedup in some data sets. Moreover, memory requirements are reduced in nearly half of the data sets. Copyright © 2008 John Wiley & Sons, Ltd.     

Learning programs by learning from failures

Machine Learning - We describe an inductive logic programming (ILP) approach called learning from failures. In this approach, an ILP system (the learner) decomposes the learning problem into three...     

应用于空间关联规则挖掘的ILP方法

文章介绍了应用于空间关联规则挖掘的ILP方法。ILP方法全称为归纳逻辑程序设计,这种方法有利于从空间领域发现有价值的知识,系统地研究地理层的层次结构,处理诸多空间对象的空间特性。这种方法已在一个ILP系统SPADA中实现,该文将通过SPADA应用空间数据的一些实例来说明ILP方法的特点。