基于角色和规则的访问控制

分析了传统RBAC模型在应用中存在的不足,引入了上下文和规则的概念,并将权限分为使能型、激活型和限制型,提出了基于角色和规则的访问控制模型。通过在设计时定义安全策略,并在运行时捕获上下文信息来应用安全策略,从而能够为系统提供更细粒度的访问控制,同时也可以降低传统RBAC模型中角色权限分配的工作量。     

Fuzzy rule-based networks for control

The authors present a method for learning fuzzy logic membership functions and rules to approximate a numerical function from a set of examples of the function's independent variables and the resulting function value. This method uses a three-step approach to building a complete function approximation system: first, learning the membership functions and creating a cell-based rule representation; second, simplifying the cell-based rules using an information-theoretic approach for induction of rules from discrete-valued data; and, finally, constructing a computational (neural) network to compute the function value given its independent variables. This function approximation system is demonstrated with a simple control example: learning the truck and trailer backer-upper control system     

Apertium: a free/open-source platform for rule-based machine translation

Apertium is a free/open-source platform for rule-based machine translation. It is being widely used to build machine translation systems for a variety of language pairs, especially in those cases (mainly with related-language pairs) where shallow transfer suffices to produce good quality translations, although it has also proven useful in assimilation scenarios with more distant pairs involved. This article summarises the Apertium platform: the translation engine, the encoding of linguistic data, and the tools developed around the platform. The present limitations of the platform and the challenges posed for the coming years are also discussed. Finally, evaluation results for some of the most active language pairs are presented. An appendix describes Apertium as a free/open-source project.     

Framework for building rule-based machine diagnostic expert systems

In the endeavour to build an expert system called XBAK using Personal Consultant Plus for the diagnosis of sophisticated equipment used in microchip manufacturing, a rule-based machine diagnostic expert system architecture was developed. The approach, features and technical implementation of this application-independent problem-solving structure are described. The architecture can be used as a framework for solving similar problems in the area of machine diagnostics.     

Probabilistic rule-based argumentation for norm-governed learning agents

This paper proposes an approach to investigate norm-governed learning agents which combines a logic-based formalism with an equation-based counterpart. This dual formalism enables us to describe the reasoning of such agents and their interactions using argumentation, and, at the same time, to capture systemic features using equations. The approach is applied to norm emergence and internalisation in systems of learning agents. The logical formalism is rooted into a probabilistic defeasible logic instantiating Dung??s argumentation framework. Rules of this logic are attached with probabilities to describe the agents?? minds and behaviours as well as uncertain environments. Then, the equation-based model for reinforcement learning, defined over this probability distribution, allows agents to adapt to their environment and self-organise.     

Modeling control in rule-based systems

The Comex modeling tool (control knowledge modeling and execution tool) focuses on modeling a problem-solving strategy and representing control knowledge, the knowledge of how to select among several problem-solving actions. In addition, Comex has facilities that let you execute early versions of the model to simulate the intended system's behavior and continuously execute the evolving model, until it becomes the real system. Comex addresses the rule-based paradigm's biggest shortcoming by adding control structures to executable models. Beginning with the specification phase, you can simulate a system's behavior, then map tasks to a rule base. The result is a structured, rule-based system built according to accepted software-engineering principles     

基于机器学习的绿色建筑智能控制

为提高绿色建筑中智能控制系统的自学习能力,通过分析用户对智能家居操作设定的规律,综合判断外部因素（室内外温度、湿度和光照度等）和内部因素（历史设定数据）对用户满意设定的影响。采用一种改进型自适应PSO优化的SVM模型,使用粒子群算法优化SVM模型的惩罚因子及核函数参数,充分发挥粒子群算法的寻优能力以及SVM 模型对小样本和非线性关系的学习能力。以用户的满意温度设定为例,验证结果表明,该算法可以提高绿色建筑智能控制系统的自学习能力。     

Autonomous machine learning of effective control strategies with connectionist-net

We report on an investigation of the feasibility of using connectionist-nets of the Rumelhart semi-linear feedforward type for learning and exercising intelligent system control. Of particular interest is the ability of such nets to discover the basis for decision and control. Of great interest also is an unexpected finding that such nets automatically build up a great deal of redundancy within itself so that it has a certain holographic quality to it and its performance degrades gradually with internal damage.     

Motion planning and control for mobile robot navigation using machine learning: a survey

Autonomous Robots - Moving in complex environments is an essential capability of intelligent mobile robots. Decades of research and engineering have been dedicated to developing sophisticated...     

Support vector learning for fuzzy rule-based classification systems

To design a fuzzy rule-based classification system (fuzzy classifier) with good generalization ability in a high dimensional feature space has been an active research topic for a long time. As a powerful machine learning approach for pattern recognition problems, the support vector machine (SVM) is known to have good generalization ability. More importantly, an SVM can work very well on a high- (or even infinite) dimensional feature space. This paper investigates the connection between fuzzy classifiers and kernel machines, establishes a link between fuzzy rules and kernels, and proposes a learning algorithm for fuzzy classifiers. We first show that a fuzzy classifier implicitly defines a translation invariant kernel under the assumption that all membership functions associated with the same input variable are generated from location transformation of a reference function. Fuzzy inference on the IF-part of a fuzzy rule can be viewed as evaluating the kernel function. The kernel function is then proven to be a Mercer kernel if the reference functions meet a certain spectral requirement. The corresponding fuzzy classifier is named positive definite fuzzy classifier (PDFC). A PDFC can be built from the given training samples based on a support vector learning approach with the IF-part fuzzy rules given by the support vectors. Since the learning process minimizes an upper bound on the expected risk (expected prediction error) instead of the empirical risk (training error), the resulting PDFC usually has good generalization. Moreover, because of the sparsity properties of the SVMs, the number of fuzzy rules is irrelevant to the dimension of input space. In this sense, we avoid the "curse of dimensionality." Finally, PDFCs with different reference functions are constructed using the support vector learning approach. The performance of the PDFCs is illustrated by extensive experimental results. Comparisons with other methods are also provided.     

Two learning approaches for a rule-based intuitive reasoner

This paper is a discussion of two continuous learning approaches for improving classification accuracy for an intuitive reasoner algorithm. The reasoner predicted the value of a given target variable by multiple iterations of forward-chained, rule-based inference. Each rule in the reasoner’s rule set had associated with it a weight, referred to here as “Strength of Belief” (SB). The value of SB of a rule indicated the certainty level of that rule. In each iteration of reasoning, any instances of similar values for a given variable were replaced by a single consolidated datum and the SB associated with the consolidated datum was increased. At the end of the reasoning process, the class (value) of the target variable which had the highest SB was reported as the conclusion. The rule set for the reasoner was generated based on a training data set that contained 80% of the data in a weather database comprising 50 years worth of hourly measurements for 54 weather variables. Each rule was induced based on only a small subset of the weather data. The intuitive reasoner was tested by using the induced rules to predict a number of pre-selected target variables using 275 test cases created from the test data. The first continuous learning approach was to identify relevant input variables for the reasoner, and the second was to rebalance the rule set used by the reasoner by adjusting the SB associated with each of the rules. Because of the way the rules were induced, the resulting rules did not contain any information about the relevance of the 53 possible input variables to the task of predicting a given target variable for previously unseen cases. A method was developed to identify which input variables were most relevant to the task based on the induced rule set. This method resulted in higher prediction accuracy of the intuitive reasoner than using a set of randomly chosen input variables for four of six target variables. The second continuous learning approach was intended to address the class imbalance problem in the rule set. The intuitive reasoner appeared to over-fit classes (values) which had frequent representation in the rule set. To address this problem, a heuristic was developed that generated adjustment factors for the SB values of the rules. The use of this heuristic improved the classification accuracy of the intuitive reasoner for four of the six target variables.     

Fuzzy inference in rule-based real-time control

In this paper research into the application of ‘expert system’-like inference mechanisms in the field of fuzzy control is adressed. Using techniques from the area of rule-based expert systems, a more flexible way of design and modification is presented of new and existing fuzzy systems for modelling and control. In comparison with ‘normal’ applications of fuzzy inference, the ćompositional rule of inference is replaced by a fuzzy inference engine. General applicability of the fuzzy inference engine is made possible by its general character as a fuzzy expert system shell. Succesful implementations in simulation and realtime control environments show the flexibility and usefullness of the described fuzzy inference engine.     

基于机器学习的工业控制网络异常检测方法

针对工业控制网络中常用的Modbus协议,提出了一种工业控制网络的异常检测方法。使用n-gram(n=0. 5,1,1. 5,2,2. 5)算法从Modbus正常报文帧的有效载荷中进行特征提取,确定出5组特征向量,并结合单类支持向量机(OCSVM)算法训练出5个初级学习器,对5个同质的初级学习器采用学习法进行集成,最终得出次级学习器,利用上述两层模型,即可完成异常识别。在气体管道网络原始数据集中进行试验,最终误报率为8%,漏报率为6%。     

Combining rule-based learning and memory-based learning for automatic word spacing in simple message service

Short message service (SMS) is a widely used service in modern mobile phones that allows users to send or receive short text messages. Current SMS, however, has two problems of inconvenient input and short message length. These problems can be resolved if a phone has an ability of automatic word spacing. This is because users need not put spaces in sending messages and longer messages are possible as they contain no space. Thus, automatic word spacing will be a very useful tool for SMS, if it can be commercially served. The practical issues of implementing it on the devices such as mobile phones are small memory and low computing power of the devices. To tackle these problems, this paper proposes a combined model of rule-based learning and memory-based learning. According to the experimental results, the model shows higher accuracy than rule-based learning or memory-based learning alone. In addition, the generated rules are so small and simple that the proposed model is appropriate for small memory devices.     

Matxin, an open-source rule-based machine translation system for Basque

We present the first publicly available machine translation (MT) system for Basque. The fact that Basque is both a morphologically rich and less-resourced language makes the use of statistical approaches difficult, and raises the need to develop a rule-based architecture which can be combined in the future with statistical techniques. The MT architecture proposed reuses several open-source tools and is based on a unique XML format to facilitate the flow between the different modules, which eases the interaction among different developers of tools and resources. The result is the rule-based Matxin MT system, an open-source toolkit, whose first implementation translates from Spanish to Basque. We have performed innovative work on the following tasks: construction of a dependency analyser for Spanish, use of rich linguistic information to translate prepositions and syntactic functions (such as subject and object markers), construction of an efficient module for verbal chunk transfer, and design and implementation of modules for ordering words and phrases, independently of the source language.     

Support vector learning mechanism for fuzzy rule-based modeling: a new approach

This paper describes a fuzzy modeling framework based on support vector machine, a rule-based framework that explicitly characterizes the representation in fuzzy inference procedure. The support vector learning mechanism provides an architecture to extract support vectors for generating fuzzy IF-THEN rules from the training data set, and a method to describe the fuzzy system in terms of kernel functions. Thus, it has the inherent advantage that the model does not have to determine the number of rules in advance, and the overall fuzzy inference system can be represented as series expansion of fuzzy basis functions. The performance of the proposed approach is compared to other fuzzy rule-based modeling methods using four data sets.     

Privacy: a machine learning view

The problem of disseminating a data set for machine learning while controlling the disclosure of data source identity is described using a commuting diagram of functions. This formalization is used to present and analyze an optimization problem balancing privacy and data utility requirements. The analysis points to the application of a generalization mechanism for maintaining privacy in view of machine learning needs. We present new proofs of NP-hardness of the problem of minimizing information loss while satisfying a set of privacy requirements, both with and without the addition of a particular uniform coding requirement. As an initial analysis of the approximation properties of the problem, we show that the cell suppression problem with a constant number of attributes can be approximated within a constant. As a side effect, proofs of NP-hardness of the minimum k-union, maximum k-intersection, and parallel versions of these are presented. Bounded versions of these problems are also shown to be approximable within a constant.