Toward a declarative semantics for infinite objects in logic programming

A greatest fixed point characterization of the minimal infinite objects computed by a nonterminating logic program is presented, avoiding difficulties experienced by other attempts in the literature. A minimal infinite object is included in the denotation just when (1) it is successively finitely approximated by a fair infinite computation of the program and (2) any nonterminating computation which continually approximates this object in fact constructs it. Minimal objects are the most general constructible by nonterminating computations of the program.     

Towards a Declarative Semantics of Inheritance with Exceptions

This paper presents a declarative semantics of compositional inheritance in an object-oriented logic programming framework with explicit exceptions,based on the iterated least fixpoint semantics to normal logic programs.Taking logic objects with exceptions as a kind of non-monotonic theory,the nonmonotonicity of inheritance is achieved,which is of importance for modeling incoplete knowledge and requirement specifications in both artificial intelligence and software engineering.     

On the declarative and procedural semantics of deductive object-oriented systems

We present declarative and procedural semantics for a deductive object-oriented language, Gulog. The declarative semantics is based on preferred minimal models. We describe both bottom-up and top-down query evaluation procedures and show that they are sound with respect to the declarative semantics. The results contribute to our understanding of the interaction of inheritance, overriding and deduction in the presence of both functional and set-valued methods, and multiple inheritance.     

A sharp proof rule for procedures in wp semantics

Summary A proof rule for the procedure call is proposed that has the property that the precondition it defines is the weakest precondition that can be inferred solely from the procedure's specification. Thus the rule enforces exactly the abstraction introduced by the specification. Gries's proof rule for the procedure call is shown not to have this property in cases when the specification involves so-called specification variables.     

A general proof rule for procedures in predicate transformer semantics

Summary A proof rule for the procedure call is derived for procedures with value, result and value-result parameters. It is extended to procedures with unrestricted global variables and to recursive procedures. Like D. Gries's proof rule, it is based on the substitution rule for assignment. However, it is more general and much simpler to apply. Assume that {U} S {V} has been proved about the procedure body S. The proof rule for determining whether a call establishes predicate E is based on finding an adaptation A satisfying AV E, where E is derived from E by some substitution of parameters.     

置信规则库规则约简的粗糙集方法

针对置信规则中规则数的"组合爆炸"问题,目前的解决方法主要是基于特征提取的规则约简方法,有效性依赖于专家知识.鉴于此,提出基于粗糙集理论的无需依赖规则库以外知识的客观方法,按照等价类划分思想逐条分析置信规则,进而消除冗余的候选值.最后,以装甲装备能力评估作为实例进行分析,分别从规则约简数、决策准确性方面与具有代表性的主观方法进行对比,结果表明,所提出方法是有效可行的,且优于现有规则约简主观方法.     

基于语义的E-Learning资源库建设

介绍语义Web和本体的基本概念,描述领域内的概念、属性和属性之间的关系,在领域本体的基础上,实现了E-Learning资源的语义标注.     

SLDNF resolution with non-safe rule and fixpoint semantics for general logic programs

The SLDNF resolution (SLD resolution with negation as failure) is often restricted to yield a safe rule, that is, negation as failure rule is adopted only in the case that the selected negative literal in each goal should be in ground. In this paper we investigate extensions of goals in SLDNF resolutions with the case of selecting non-ground negative literals. Since Shepherdson's proposal is thought of as most general [16, 18] we formally show how the success and failure sets by Sherpherdson's SLDNFS resolution are related with a fixpoint semantics, which is generalized to be concerned with atom sets involving the variables.     

Optimal design of a fuzzy rule base

This paper considers the issues of forming a fuzzy database and ensuring its fullness and consistency. In addition, a certain method of designing the database structure using an hierarchical system of linguistic rules is suggested.     

基于入侵防御系统的完备化规则库

对于目前入侵防御系统（IPS）中规则库日益增大并且无法完备的特点,提出了一套完备化规则库系统。通过对网络操作行为的分解和对各种数据的挖掘,形成最基本最小的网络行为语言描述单元（网络行为描述DNA）,再使用Teiresias进化算法对其进行全局遍历性的组合,计算产生新的描述语言,进行可行性判断后,对可行的结果进行规则匹配并进行风险评估,产生新的防御规则,从而达到入侵防御系统规则库完备的目的。     

Learning rule base in linguistic expert systems

 Linguistic fuzzy control is introduced in a pure logical framework. The problem of learning of the linguistic rule base from the data obtained by monitoring of successful control and the problem of learning the linguistic context are studied. The methods are demonstrated by results of experiments. Supported by the grant No. 201/96/0985 of the GAČR and the project VS96037 of MŠMT of the Czech Republic.     

Approximate well-founded semantics, query answering and generalized normal logic programs over lattices

The management of imprecise information in logic programs becomes important whenever the real world information to be represented is of an imperfect nature and the classical crisp true, false approximation is not adequate. In this work, we consider normal logic programs over complete lattices, where computable truth combination functions may appear in the rule bodies to manipulate truth values and we will provide a top-down query answering procedure.     

Automatic generation of a fuzzy rule base for constant turning force

An automatic fuzzy rule base generation method is proposed to control nonlinear and timevarying turning processes with constant cutting forces. Based on this study, the optimum fuzzy rule base for the control of turning processes can be self-organized without the need for experienced manufacturing engineers. A fuzzy logic controller based on these fuzzy rules can adjust feed rate on line to achieve an optimal production rate in turning operations.     

Evolutionary design of fuzzy rule base for nonlinear systemmodeling and control

In designing fuzzy models and controllers, we encounter a major difficulty in the identification of an optimized fuzzy rule base, which is traditionally achieved by a tedious trial-and-error process. The paper presents an approach to the evolutionary design of an optimal fuzzy rule base for modeling and control. Evolutionary programming is used to simultaneously evolve the structure and the parameter of fuzzy rule base for a given task. To check the effectiveness of the suggested approach, four numerical examples are examined. The performance of the identified fuzzy rule bases is demonstrated     

A neural network and fuzzy rule base hybrid for pattern classification

 This paper presents a novel hybrid of the two complimentary technologies of soft computing viz. neural networks and fuzzy logic to design a fuzzy rule based pattern classifier for problems with higher dimensional feature spaces. The neural network component of the hybrid, which acts as a pre-processor, is designed to take care of the all-important issue of feature selection. To circumvent the disadvantages of the popular back propagation algorithm to train the neural network, a meta-heuristic viz. threshold accepting (TA) has been used instead. Then, a fuzzy rule based classifier takes over the classification task with a reduced feature set. A combinatorial optimisation problem is formulated to minimise the number of rules in the classifier while guaranteeing high classification power. A modified threshold accepting algorithm proposed elsewhere by the authors (Ravi V, Zimmermann H.-J. (2000) Eur J Oper Res 123: 16–28) has been employed to solve this optimization problem. The proposed methodology has been demonstrated for (1) the wine classification problem having 13 features and (2) the Wisconsin breast cancer determination problem having 9 features. On the basis of these examples the results seem to be very interesting, as there is no reduction in the classification power in either of the problems, despite the fact that some of the original features have been completely eliminated from the study. On the contrary, the chosen features in both the problems yielded 100% classification power in some cases.     

Active rule base development for dynamic vertical partitioning of multimedia databases

Currently, vertical partitioning has been used in multimedia databases in order to take advantage of its potential benefits in query optimization. Nevertheless, most vertical partitioning algorithms are static; this means that they optimize a vertical partitioning scheme (VPS) according to a workload, but if this workload suffers changes, the VPS may be degraded, which would result in long query response time. This paper presents a set of active rules to perform dynamic vertical partitioning in multimedia databases. First of all, these rules collect all the information that a vertical partitioning algorithm needs as input. Then, they evaluate this information in order to know if the database has experienced enough changes to trigger a performance evaluator. In this case, if the performance of the database falls below a threshold previously calculated by the rules, the vertical partitioning algorithm is triggered, which gets a new VPS. Finally, the rules materialize the new VPS. Our active rule base is implemented in the system DYMOND, which is an active rule-based system for dynamic vertical partitioning of multimedia databases. DYMOND’s architecture and workflow are presented in this paper. Moreover, a case study is used to clarify and evaluate the functionality of our active rule base. Additionally, authors of this paper performed a qualitative evaluation with the aim of comparing and evaluating DYMOND’s functionality. The results showed that DYMOND improved query performance in multimedia databases.