多特征融合的入侵检测

从人认识事物的特点出发，对认识过程进行抽象，把它用到入侵检测系统中，提出了多特征融合的入侵检测方法。在此方法的实现中，以特征元素为基本单元，形成特征规则，采用基于可信度的不精确推理，判断系统被入侵的可能性。还讨论了模式的形成，给出了实现模型。此方法能有效提高计算机系统抵御入侵及自身免疫的能力。     

Set-valued ordered information systems

Set-valued ordered information systems can be classified into two categories: disjunctive and conjunctive systems. Through introducing two new dominance relations to set-valued information systems, we first introduce the conjunctive/disjunctive set-valued ordered information systems, and develop an approach to queuing problems for objects in presence of multiple attributes and criteria. Then, we present a dominance-based rough set approach for these two types of set-valued ordered information systems, which is mainly based on substitution of the indiscernibility relation by a dominance relation. Through the lower/upper approximation of a decision, some certain/possible decision rules from a so-called set-valued ordered decision table can be extracted. Finally, we present attribute reduction (also called criteria reduction in ordered information systems) approaches to these two types of ordered information systems and ordered decision tables, which can be used to simplify a set-valued ordered information system and find decision rules directly from a set-valued ordered decision table. These criteria reduction approaches can eliminate those criteria that are not essential from the viewpoint of the ordering of objects or decision rules.     

Dominance relation and rules in an incomplete ordered information system

Rough sets theory has proved to be a useful mathematical tool for classification and prediction. However, as many real‐world problems deal with ordering objects instead of classifying objects, one of the extensions of the classical rough sets approach is the dominance‐based rough sets approach, which is mainly based on substitution of the indiscernibility relation by a dominance relation. In this article, we present a dominance‐based rough sets approach to reasoning in incomplete ordered information systems. The approach shows how to find decision rules directly from an incomplete ordered decision table. We propose a reduction of knowledge that eliminates only that information that is not essential from the point of view of the ordering of objects or decision rules. © 2005 Wiley Periodicals, Inc. Int J Int Syst 20: 13–27, 2005.     

一种新的多维稀疏规则模糊插值推理方法

经典的插值理论针对一维稀疏规则库的条件，提出了各种不同的插值方法，取得了很多很好的经验．但对多维稀疏规则条件的近似推理，研究很少，仅有的几种插值方法，存在着难以保证推理结果的凸性和正规性等问题．为了在多维稀疏规则条件下能得到好的插值推理结果。提出了一种基于几何相似的插值推理方法．该方法能较好地保证推理结果隶属函数的凸性和正规性，为智能系统中的模糊推理提供了一个十分有用的工具．     

多变量规则的线性插值推理方法

插值推理是稀疏规则条件下的一类重要的推理方法，单变量的情况已有较多研究，但针对多变量情况的研究还不多，仅有的几种插值方法，存在着难以保证推理结果的凸性和正规性等问题。多变量规则的插值推理是插值推理研究的重要方面，为了在多变量稀疏规则条件下能得到好的插值推理结果，本文对多变量规则的插值推理方法进行了研究，提出了一个多变量规则的线性插值推理方法。该方法能较好地保证推理结果隶属函数的凸性和正规性，为智能系统中的模糊推理提供了一个十分有用的工具。     

Rough Set Approach to Pattern Extraction from Classifiers

We discuss the impact of inductive reasoning on the rough set to concept approximation. In inductive reasoning one cannot define inclusion degrees of object neighborhoods directly into the target concepts but only into some neighborhoods relevant to such concepts. Such degrees together with degrees of inclusion of patterns in target concepts make it possible to define outputs of classifiers for new classified objects. We show how among formulas used for classifier construction from decision rules one can search for new patterns relevant for the incremental concept approximation.     

An approach to fuzzy default reasoning for function approximation

This paper discusses fuzzy reasoning for approximately realizing nonlinear functions by a small number of fuzzy if-then rules with different specificity levels. Our fuzzy rule base is a mixture of general and specific rules, which overlap with each other in the input space. General rules work as default rules in our fuzzy rule base. First, we briefly describe existing approaches to the handling of default rules in the framework of possibility theory. Next, we show that standard interpolation-based fuzzy reasoning leads to counterintuitive results when general rules include specific rules with different consequents. Then, we demonstrate that intuitively acceptable results are obtained from a non-standard inclusion-based fuzzy reasoning method. Our approach is based on the preference for more specific rules, which is a commonly used idea in the field of default reasoning. When a general rule includes a specific rule and they are both compatible with an input vector, the weight of the general rule is discounted in fuzzy reasoning. We also discuss the case where general rules do not perfectly but partially include specific rules. Then we propose a genetics-based machine learning (GBML) algorithm for extracting a small number of fuzzy if-then rules with different specificity levels from numerical data using our inclusion-based fuzzy reasoning method. Finally, we describe how our approach can be applied to the approximate realization of fuzzy number-valued nonlinear functions     

An actor model for rule-based systems

Our research originates from a study of the possibilities of integrating rules and objects in knowledge-based systems. In the present work, we are interested in the interactionist perspective of an object. The stepwise reasoning of a diagnostic expert system, possibly involving subgoaling and interactions with the environment, can be easily codified by means of production rules over proposition literals. This set of rules can be graphically represented in a network manner denoting the relations between the rules. The individual nodes in the network can be expressed by means of autonomous objects and their relations, interpreted as possible communications between them. The objects are given a structure and a proper behaviour and cooperate for performing logical reasoning by means of forward and backward chaining inference processes. Therefore, designing this system implies addressing several basic issues such as inter-object communications and their synchronization. The problem here is not necessarily to develop a great intelligence locally but to develop strong networks of good communicators. This approach belongs to the interactionist representation current, where objects are called actors. In principle, the actors may carry out computation in parallel and provide a conceptual foundation for massively concurrent object-oriented paradigms. From this point of view, a system allowing for the simultaneous investigation of several rules and premises in the forward or the backward chaining would be significantly more efficient.     

面向大型数据表的粗分析方法

该文运用粗糙集理论,提出一种提取大型数据表中决策规则的方法。首先根据条件属性及属性值的权重将数据表分解成多个子表,再利用粗分析方法分别对各子表简约,综合每个子表归纳出的具有一定置信度的子规则形成规则集,做出推理和决策。     

优势关系下模糊目标信息系统的决策规则优化

利用基于优势关系的模糊粗糙集模型,讨论了模糊决策信息系统中优化序决策规则的获取问题。利用优势关系定义了模糊目标信息系统中对象的三种属性约简。给出了它们的判定定理,构造相应的区分函数,利用布尔推理技术计算对象的属性约简,得到三类新的优化序决策规则。     

A rule-based support system for dissonance discovery and control applied to car driving

This paper is based on the concept of dissonance, that is, gaps or conflicts existing in a specific knowledge base or among different knowledge bases. It presents a rule-based system that assists human operators in dissonance discovery and control by taking into account two kinds of dissonance, i.e., affordance to study conflicts of use, and inconsistencies to study conflicts of intention and action, through the analysis of cognitive behavior implemented in knowledge bases. This system elaborates the knowledge base composed of rules, and analyzes the knowledge content to discover new knowledge by creating additional rules, or to identify inconsistencies when conflicts between rules occur. The affordance discovery control process uses a deductive and an inductive reasoning algorithm of which the aim is to establish new rules using existing ones. The inconsistency discovery control process applies an abductive reasoning algorithm in order to determine contradictory rules when existing rules may result in opposite intentions being accomplished. Two groups of inconsistencies are addressed: interferences involving several decision makers, and contradictions involving the same decision maker. A knowledge acquisition control process facilitates the creation of the initial rules that contain parameters such as intentions relating to the goals to be achieved, actions to be performed to achieve these intentions, objects used to carry out these actions and the decision makers who execute these actions using the corresponding objects. A feasibility study taking into account five rule bases relating to the manual use of an Automated Speed Control System (ASCS), the automated control of the car speed by the ASCS, the manual control of aquaplaning, the manual control of the car speed, and the manual control of car fuel consumption is proposed to validate the rule-based support system.     

Rough set feature extraction by remarkable degrees with real world decision-making problems

In this paper, a novel approach for simplifying the obtained if–then rules from decision table by rough set based methods is proposed. This approach can extract main features of objects in different decision classes by remarkable degrees. Using the proposed method, two real world decision-making problems are studied. The first one is for analyzing the main features of Japanese industries. The second is for anatomizing the life situations of senior citizens in Japan. The analysis results show that the proposed method is powerful for piecing out the main features of decision classes in the decision table which has many attributes and objects. The obtained main features can provide deep insight into the situations of objects and are useful for making a decision in the real world.     

Introducing possibilistic logic in ILP for dealing with exceptions

In this paper we propose a new formalization of the inductive logic programming (ILP) problem for a better handling of exceptions. It is now encoded in first-order possibilistic logic. This allows us to handle exceptions by means of prioritized rules, thus taking lessons from non-monotonic reasoning. Indeed, in classical first-order logic, the exceptions of the rules that constitute a hypothesis accumulate and classifying an example in two different classes, even if one is the right one, is not correct. The possibilistic formalization provides a sound encoding of non-monotonic reasoning that copes with rules with exceptions and prevents an example to be classified in more than one class. The benefits of our approach with respect to the use of first-order decision lists are pointed out. The possibilistic logic view of ILP problem leads to an optimization problem at the algorithmic level. An algorithm based on simulated annealing that in one turn computes the set of rules together with their priority levels is proposed. The reported experiments show that the algorithm is competitive to standard ILP approaches on benchmark examples.     

利用粗糙集理论实现工艺决策的冲突消解

根据粗糙集理论在解决不确定性问题方面的特性,引入了基于粗糙集理论的工艺知识度量方法,构造了度量工艺规则相似性的规则距离公式,并在分析工艺决策中传统冲突消解策略的基础上,建立了工艺决策推理过程中规则集约简的方法．与匹配度排序方法的比较表明,文中方法可有效地解决工艺决策中冲突消解的问题．     

CAUPS系统中的时空推理机制与算法

动态城市规划方案仿真系统中的一个重要问题就是抽象和提取仿真对象之间涉及到的空间和时间逻辑关系,并以此作为仿真过程的基本准则和规范动态推理演算。为了建立CAUPS系统中的时空推理机制,针对城市规划方案的动态仿真过程中需要应用到的基本准则和规范,在传统时空关系描述和推理规范的基础上定义出适用于面向城市规划动态仿真的时空关系。考虑到城市规划过程中存在的非刚体对象,提出面向城市辅助规划系统方案应用结果仿真的时间空间推理规范,该规范能够非常好的支持如植被、水域等非刚体对象,并给出一个适合多Agent系统采用的时间空间推理规范执行解决方案,包括时间推理规范算法和空间推理规范算法。面向城市规划的时间推理规范算法和空间推理规范算法已经被成功应用于CAUPS系统底层的多Agent交互关系调整中。     

铁路超限超重货物装载加固可拓实例推理方法

铁路超限超重货物具有长大、笨重和价值昂贵等特征,装载加固影响因素众多且无法完全量化表达,超限超重货物装载加固决策问题是一个半结构化问题,设计装载加固可拓实例推理技术对提升铁路超限超重货物安全装载水平和运输质量尤为重要。结合铁路超限超重货物特征及其装载加固决策要素,采用可拓基元与实例推理技术,构造超限超重货物装载加固实例推理基础数据与推理规则模块的可拓基元模型,分析装载加固可拓实例属性取值特征,给出局部与全局相似度计算公式,设计超限超重货物装载加固可拓实例推理算法,确定待解实例的解。实例运用表明所给出的可拓实例推理方法可制定出合理安全的装载加固方案,切实有效解决铁路超限超重货物装载加固决策问题。     

Reasoning with Sets of Defaults in Default Logic

We present a general approach for representing and reasoning with sets of defaults in default logic, focusing on reasoning about preferences among sets of defaults. First, we consider how to control the application of a set of defaults so that either all apply (if possible) or none do (if not). From this, an approach to dealing with preferences among sets of default rules is developed. We begin with an ordered default theory , consisting of a standard default theory, but with possible preferences on sets of rules. This theory is transformed into a second, standard default theory wherein the preferences are respected. The approach differs from other work, in that we obtain standard default theories and do not rely on prioritized versions of default logic. In practical terms this means we can immediately use existing default logic theorem provers for an implementation. Also, we directly generate just those extensions containing the most preferred applied rules; in contrast, most previous approaches generate all extensions, then select the most preferred. In a major application of the approach, we show how semimonotonic default theories can be encoded so that reasoning can be carried out at the object level. With this, we can reason about default extensions from within the framework of a standard default logic. Hence one can encode notions such as skeptical and credulous conclusions, and can reason about such conclusions within a single extension.