模糊规则自动获取及其性能的分析

提出了一种基于模糊神经网络获取模糊规则及其进行模糊系统参数学习的方法,通过实例进行了自动列车运行系统仿真,总结了这种方法的特点。结论表明,所提出的模糊规则生成和模糊系统学习方法是行之有效的。     

基于词性和语义知识的汉语句法规则学习

本文提出了一种汉语句法规则学习的新方法。本方法的特点是:在规则的学习和表示上都利用了词性、语义以及上下文相关的信息。它不仅能自动学习上下文无关的二元规则,而且还能自动发现词类搭配中的歧义结构,并利用语义和上下文相关信息将歧义规则在句法分析之前进行排除。实验结果表明,该方法较好地解决了汉语句法规则的自动获取及排歧问题并极大地降低了句法分析的难度,显示了很好的应用前景。     

基于上下文依赖规则覆盖的句子生成

基于规则覆盖的句子生成,是上下文无关文法句子生成的主要方法,但是它也具有局限性。最近提出的上下文依赖规则覆盖,能根据文法的内部结构不同而具有不同的分支集合,比规则覆盖的精度更高。目前,尚未见这种上下文依赖规则覆盖的句子生成算法。该文在规则覆盖的句子生成算法的基础上,实现一个基于上下文依赖规则覆盖的句子生成算法。该算法已在机器上实现并经过实验检验。     

一种基于Rough Sets和模糊神经网络的规则获取的方法

该文提出了一种基于RoughSets思想获取初始规则,并通过模糊神经网络优化,最后再进行简化获取模糊规则,及模糊系统参数学习的方法。并通过实例进行了自动列车运行系统仿真。文中还基于上述实例,将这种基于模糊神经网络的学习与控制方法与标准的BP网络和基本的模糊系统方法进行了比较,并总结了这种方法的特点。结论表明,该文所提出的模糊规则生成和模糊系统学习方法是行之有效的。     

面向MDA的业务逻辑模型到代码的转换方法研究

业务逻辑自动生成是代码自动生成领域中一个匾待解决的问题。为了解决这个问题,分析了业务逻辑难于生成的原因,在MDA的基础上引入产生式规则的思想,研究PSM模型到代码的转换过程。提出一种基于产生式规则的建模方法对逻辑中业务规则及业务流程进行建横,并基于产生式系统原理,对代码生成器进行设计与实现。基于本方法实现的代码生成器,实现了业务规则及业务流程模型到代码的转换,使目标代码业务逻辑与数据分离,一定程吱上解决了业务逻辑代码自动生成问题。     

基于转移的音字转换纠错规则获取技术

文中描述了一种在音字转换系统中从规模不限的在线文本中自动获取纠错规则的机器学习技术。该技术从音字转换结果中自动获取误转换结果及其相应的上下文信息,从而生成转移规则集。该转移规则集应用于音字转换的后处理模块,使音字转换系统率进一步提高,并使系统具备了很强的灵活性和可扩展性。     

基于模块化可达图的服务组合验证及BPEL代码生成

为了解决基于中介器服务组合方法的状态爆炸和不能自动生成BPEL(business process execution language)的问题,提出了一种基于Petri网模块化可达图的服务组合验证方法.服务组合的Petri网模型通过中介变迁进行模型分割,分别对各个部分构建可达图,再对模块化可达图进行服务组合可行性分析.采用模块化可达图可以大量节省空间开销,有效避免空间爆炸问题.在验证服务组合可行的情况下提出了基于ECA规则形式的BPEL过程代码自动生成方法,也就是将服务组合Petri网模型的中介变迁以及每个服务对外接口的调用或操作都映射生成为ECA规则形式的BPEL代码段.通过对一个电子商务实例进行分析,说明了所提出方法的有效性.     

基于ASP的智能空间中上下文感知问题的研究

智能空间和回答集程序ASP的整合解决了智能空间中固定优先关系下的资源冲突问题。然而,智能空间是一个上下文敏感的、动态的环境,随着用户在空间中行为的改变,空间中的信息和服务也要发生动态的变化。原有的基于本体的上下文感知框架仅能实现不同本体信息的推理,而没有考虑环境信息对于上下文感知的影响。为此,基于回答集程序提出一种智能空间中的上下文感知框架,动态感知用户的上下文本体以及环境信息,完成用户在空间中的上下文动态推理。首先,使用本体描述用户的上下文信息;然后使用回答集程序表达上下文推理规则,并引入缺省规则依据本体信息以及环境信息动态决策上下文响应的优先关系;最后,求得回答集程序的解,即为用户上下文事件的决策结果,从而帮助用户实现智能推理。实验结果表明,该框架可以动态决策空间中的优先关系,有效实现空间中的上下文推理。     

基于移动代理的上下文感知系统研究

上下文表是用上下文信息描述实体状态,原子上下文感知反应则说明了系统根据当前上下文和历史上下文做出合适的响应.基于以上两个概念设计了上下文感知移动代理、系统代理和上下文感知移动代理服务环境,目的是解决普适计算系统需要自动适应用户行为及环境变化的问题.首先使用历史上下文与当前上下文提取上下文表达式,根据该表达式在上下文反应容器中匹配对应的操作,然后由系统代理执行该操作,为用户提供任务相关的服务或者信息.其次根据原子上下文感知提出若干实例,并由上下文感知演算验证,同时指出上下文感知演算的不足.最后,用染色Petri网对一个实例场景仿真,证明了系统的可行性.     

基于新编码器和相似度约束的图像修复

现有的图像修复方法存在受损区域修复痕迹明显、语义不连续、不清晰等问题,针对这些问题本文提出了一种基于新型编码器并结合上下文感知损失的图像修复方法.本文方法采用生成对抗网络作为基本网络架构,为了能够充分学习图像特征得到更清晰的修复结果,引入了SE-ResNet提取图像的有效特征;同时提出联合上下文感知损失训练生成网络以约束局部特征的相似性,使得修复图像更加接近原图且更加真实自然.本文在多个公共数据集上进行实验,证明了本文所提方法能够更好地对破损图像进行修复.     

Inductive rule generation and interpretation

Abstract

The problem of knowledge acquisition has been recognized as the major bottleneck in the development of knowledge-based systems. An encouraging approach to alleviate this problem is inductive learning. Inductive learning systems accept, as input, a set of data that represent instances of the problem domain and produce, as output, the rules of the knowledge base. Each data item is described by a set of attribute values and is assigned to a unique decision class. A common characteristic of the existing inductive learning systems, is that they are empirical in nature and do not take into account the implications of the inductive rule generation process on the performance of the resulting set of rules. That performance is assessed when the rules are used to classify new unlabelled data. This paper demonstrates that the performance of a rule set is a function of the rule generation and rule interpretation processes. These two processes are interrelated and should not be considered separately. The interrelation of rule generation and rule interpretation is analysed and suggestions to improve the performance of existing inductive learning systems, are forwarded.     

基于组合训练的规则嵌入多智能体强化学习方法

多智能体强化学习方法在仿真模拟、游戏对抗、推荐系统等许多方面取得了突出的进展。然而,现实世界的复杂问题使得强化学习方法存在无效探索多、训练速度慢、学习能力难以持续提升等问题。该研究嵌入规则的多智能体强化学习技术,提出基于组合训练的规则与学习结合的方式,分别设计融合规则的多智能体强化学习模型与规则选择模型,通过组合训练将两者有机结合,能够根据当前态势决定使用强化学习决策还是使用规则决策,有效解决在学习中使用哪些规则以及规则使用时机的问题。依托中国电子科技集团发布的多智能体对抗平台,对提出的方法进行实验分析和验证。通过与内置对手对抗,嵌入规则的方法经过约1.4万局训练就收敛到60%的胜率,而没有嵌入规则的算法需要约1.7万局的时候收敛到50%的胜率,结果表明嵌入规则的方法能够有效提升学习的收敛速度和最终效果。     

ECA rule learning in dynamic environments

Through the development of management and intelligent control systems, we can make useful decision by using incoming data. These systems are used commonly in dynamic environments that some of which are been rule-based architectures. Event–Condition–Action (ECA) rule is one of the types that are used in dynamic environments. ECA rules have been designed for the systems that need automatic response to certain conditions or events. Changes of environmental conditions during the time are important factors impacting a reduction of the effectiveness of these rules which are implied by changing users demands of the systems that vary over time. Also, the rate of the changes in the rules are not known which means we are faced with the lack of information about rate of occurrence of new unknown conditions as a result of dynamics environments. Therefore, an intelligent rule learning is required for ECA rules to maintain the efficiency of the system. To the best knowledge of the authors, ECA rule learning has not been investigated. An intelligent rule learning for ECA rules are studied in this paper and a method is presented by using a combination of multi flexible fuzzy tree (MFlexDT) algorithm and neural network. Hence data loss could be avoided by considering the uncertainty aspect. Owing to runtime, speed, and also stream data in dynamic environments, a hierarchical learning model is proposed. We evaluate the performance of the proposed method for resource management in the Grid and e-commerce as case studies by modeling and simulating. A case study is presented to show the applicability of the proposed method.     

基于NSGA-II的扩展置信规则库激活规则多目标优化方法

针对扩展置信规则库（extended belief rule base,EBRB）系统在不一致的激活规则过多时推理准确性不高的问题,引入带精英策略的快速非支配排序遗传算法（NSGA-Ⅱ）,提出一种基于NSGA-Ⅱ的激活规则多目标优化方法。该方法首先将激活权重大于零的规则（即激活规则）进行二进制编码,把最终参与合成推理的激活规则集合的不一致性以及激活权重和作为多目标优化问题的目标函数,通过带精英策略的快速非支配排序遗传算法求解不一致性更小的激活规则集合,从而降低不一致激活规则对于EBRB系统推理准确性的影响。为了验证本文方法的有效性和可行性,引入非线性函数和输油管道检漏实例进行测试。实验结果表明,基于NSGA-Ⅱ的扩展置信规则库激活规则多目标优化方法能够有效提高EBRB系统的推理能力。     

Credit Assignment in Rule Discovery Systems Based on Genetic Algorithms

In rule discovery systems, learning often proceeds by first assessing the quality of the system's current rules and then modifying rules based on that assessment. This paper addresses the credit assignment problem that arises when long sequences of rules fire between successive external rewards. The focus is on the kinds of rule assessment schemes which have been proposed for rule discovery systems that use genetic algorithms as the primary rule modification strategy. Two distinct approaches to rule learning with genetic algorithms have been previously reported, each approach offering a useful solution to a different level of the credit assignment problem. We describe a system, called RUDI, that exploits both approaches. We present analytic and experimental results that support the hypothesis that multiple levels of credit assignment can improve the performance of rule learning systems based on genetic algorithms.     

正负关联规则两级置信度阈值设置方法

针对传统正负关联规则置信度阈值设置方法难以控制低可信度规则数量和易遗漏有趣规则的问题,提出了一个结合项集相关性的两级置信度阈值设置方法（PNMC-TWO）。首先,基于规则的无矛盾性、有效性和有趣性考虑,以相关度-支持度-置信度为框架,从规则置信度与项集支持度的计算关系出发,系统地分析了正负关联规则置信度取值随规则的项集支持度大小变化的规律;然后,与实际挖掘中用户对高可信度且有趣的规则需求相结合,提出了一个新的设置模型,避免了传统方法设置阈值时的盲目性和随意性;最后,从规则数量和规则质量两方面对所提方法与原双阈值法进行了实验对比。实验结果表明,所提方法不仅可以更好地确保提取出的关联规则有效和有趣,还可以显著地降低可信度低的关联规则数量。     

A simulation study on the performance of pickup-dispatching rules for multiple-load AGVs

In this paper, the pickup-dispatching problem of multiple-load AGVs (automated guided vehicles) is studied. This problem is defined in the multiple-load control process proposed by Ho and Chien [Ho, Y. C., & Chien, S. H. (2004). A simulation study on the performance of delivery-dispatching rules for multiple-load AGVs. In E. Kozan (Ed.), Proceedings of abstracts and papers (On CD-ROM) of the 5th Asia-Pacific industrial engineering and management systems conference and the 7th Asia-Pacific division meeting of the international foundation of production research (pp. 18.1.1–18.1.15). Brisbane: APIEMS]. Their control process identifies four problems faced by a multiple-load AGV. These problems are task-determination, delivery-dispatching, pickup-dispatching and load-selection. This paper focuses on the third problem. For this problem, nine pickup-dispatching rules are proposed and studied. The first, second and fourth problems are not the main focus of this study, thus only one task-determination rule, one delivery-dispatching rule and two load-selection rules are adopted for them. The objective of this study is twofold. First, to understand the performance of the proposed rules in different performance measures, e.g., the system’s throughput, the mean flow time of parts (MFTP) and the mean tardiness of parts (MTP). Second, the effects that the proposed rules have on each other’s performance are investigated. Computer simulations are used to achieve these objectives. The experimental results reveal a rule that dispatches vehicles to the machine with the greatest output queue length is the best in all performance measures. Also, distance-based or due-time-based rules do not perform as well as queue-based rules. It is also found that the performance of pickup-dispatching rules is affected by different load-selections rules.     

Reliable representations for association rules

Association rule mining has contributed to many advances in the area of knowledge discovery. However, the quality of the discovered association rules is a big concern and has drawn more and more attention recently. One problem with the quality of the discovered association rules is the huge size of the extracted rule set. Often for a dataset, a huge number of rules can be extracted, but many of them can be redundant to other rules and thus useless in practice. Mining non-redundant rules is a promising approach to solve this problem. In this paper, we first propose a definition for redundancy, then propose a concise representation, called a Reliable basis, for representing non-redundant association rules. The Reliable basis contains a set of non-redundant rules which are derived using frequent closed itemsets and their generators instead of using frequent itemsets that are usually used by traditional association rule mining approaches. An important contribution of this paper is that we propose to use the certainty factor as the criterion to measure the strength of the discovered association rules. Using this criterion, we can ensure the elimination of as many redundant rules as possible without reducing the inference capacity of the remaining extracted non-redundant rules. We prove that the redundancy elimination, based on the proposed Reliable basis, does not reduce the strength of belief in the extracted rules. We also prove that all association rules, their supports and confidences, can be retrieved from the Reliable basis without accessing the dataset. Therefore the Reliable basis is a lossless representation of association rules. Experimental results show that the proposed Reliable basis can significantly reduce the number of extracted rules. We also conduct experiments on the application of association rules to the area of product recommendation. The experimental results show that the non-redundant association rules extracted using the proposed method retain the same inference capacity as the entire rule set. This result indicates that using non-redundant rules only is sufficient to solve real problems needless using the entire rule set.     

A fuzzy clustering method for generating fuzzy models

A systematic fuzzy approach considering both accuracy and interpretability is developed in the paper. First, a fuzzy modeling method based on a new objective function is proposed. The proposed method can deal with the problem where the input variables have an affect on the input space of the fuzzy system while the output variables do not exert any influence on input space of fuzzy system. Then rule reduction is performed to obtain the model structure of the fuzzy system by QR decomposition of the fuzzy reference matrix. According to analysis of the rank loss of the matrix, the important rules and unimportant rules can be confirmed in this paper. Simulation results demonstrate that the proposed approach can be used to build fuzzy models of nonlinear systems. Copyright © 2008 John Wiley and Sons Asia Pte Ltd and Chinese Automatic Control Society     

Optimal sensor rules and unified fusion rules for multisensor multi-hypothesis network decision systems with channel errors

In this paper, for general jointly distributed sensor observations, we present optimal sensor rules with channel errors for a given fusion rule. Then, the unified fusion rules problem for multisensor multi-hypothesis network decision systems with channel errors is studied as an extension of our previous results for ideal channels, i.e., people only need to optimize sensor rules under the proposed unified fusion rules to achieve global optimal decision performance. More significantly, the unified fusion rules do not depend on distributions of sensor observations, decision criterion, and the characteristics of fading channels. Finally, several numerical examples support the above analytic results and show some interesting phenomena which can not be seen in ideal channel case.