Activation pattern controlled rules: Towards an integration of data-driven and command-driven programming

The attractions and drawbacks of data-driven programming are discussed in the context of rule-based forward chaining systems. The relationships between data-driven and command-driven programming are analyzed in the context of a course-registration example. A new form of production rule, called an activation pattern controlled rule, that generalizes classical forward chaining rules is introduced. Activation pattern controlled rules are triggered by calls of commands; that is, by the intension to perform a command but not necessarily by the result of applying the command itself. We demonstrate that activation pattern controlled rules facilitate the integration of data-driven and command-driven programming, support preventive programming as well, and allow for writing rule-based programs more transparently. We also survey our experiences in implementing an inference engine for activation pattern controlled rules.     

Integrating forward-chaining rules with operations and permanent knowledge bases

Traditionally, rule-based forward-chaining systems are considered to be standalone, working on a volatile memory. This paper focuses on the integration of forward-chaining rules with command-driven programming paradigms in the context of permanent, integrated knowledge bases. A system architecture is proposed that integrates the data management functions of large computerized knowledge bases into a module called a knowledge base management system (KBMS). Experiences we had in integrating rules with operations into a prototype KBMS called DALI are surveyed. For this integration, a new form of production rule, called the activation pattern controlled rule, is introduced, which augments traditional forward-chaining rules by a second, additional left-hand side, which allows making rules sensitive to calls of particular operations. Activation pattern controlled rules play an important role in DALI's system architecture, because they facilitate the storage of knowledge that has been specified relying on mixed programming, a combination of data-driven, command-driven, and preventive programming. The general problems of implementing permanent knowledge bases that contain rules and operations are discussed, and an algorithm for implementating activation pattern controlled rules, called IPTREAT, a generalization of the TREAT algorithm, is provided. Furthermore, the paper intends to clarify the differences between traditional, volatile rule-based systems and rule-based systems that are geared toward knowledge integration by supporting a permanent knowledge base.This paper is an extended and significantly revised version of a paper entitled Integrating Rules into a Knowledge Base Management System, which was presented at the First International Conference on Systems Integration, April 1990 [1].     

基于规则的UML设计模型的一致性检验

统一建模语言(UML)是业界公认的主流面向对象建模语言,为系统开发提供了丰富的建模元素。由于UML不同建模元素之间缺乏准确定义的关系,因此UML模型往往会出现不一致性问题。针对该问题,提出了一种基于规则的检验方法。该方法把UML设计模型和一致性条件分别映射为规则系统的事实库和规则库,如果事实库与规则库不匹配,则表示设计模型中存在不一致性。我们使用自主开发的一种“面向对象-规则语言系统”作为检验一致性的规则系统,它集成了面向对象语言和规则语言两种范型,有利于统一使用C++语言来设计并实现一致性检验工具,提高一致性检验效率。     

基于改进规则激活率的扩展置信规则库推理方法

数据驱动的扩展置信规则库系统,是在传统置信规则库的基础上利用关系数据来生成规则,使用该方法构建规则库简单有效。然而,该方法激活的规则存在不一致与不完整,并且该方法无法处理零激活的输入。鉴于此,本文提出基于改进规则激活率的扩展置信规则库方法,通过高斯核改进个体匹配度计算方法,权衡激活规则的一致性与完整性,并利用k近邻思想解决规则零激活问题。最后,本文选取非线性函数拟合实验和输油管道检漏实验来检验所提方法的效率和准确度。实验结果表明该方法既保证了扩展置信规则库系统的推理效率,也提高了推理结果的精度。     

Forward reasoning and dependency-directed backtracking in a system for computer-aided circuit analysis

We present a rule-based system for computer-aided circuit analysis. The set of rules, called EL, is written in a rule language called ARS. Rules are implemented by ARS as pattern-directed invocation demons monitoring an associative data base. Deductions are performed in an antecedent manner, giving EL's analysis a catch-as-catch-can flavour suggestive of the behavior of expert circuit analyzers. We call this style of circuit analysis propagation of constraints. The system threads deduced facts with justifications which mention the antecedent facts and the rule used. These justifications may be examined by the user to gain insight into the operation of the set of rules as they apply to a problem. The same justifications are used by the system to determine the currently active data-base context for reasoning in hypothetical situations. They are also used by the system in the analysis of failures to reduce the search space. This leads to effective control of combinatorial search which we call dependency-directed backtracking.     

Connectionist expert system with adaptive learning capability

A neural network expert system called adaptive connectionist expert system (ACES) which will learn adaptively from past experience is described. ACES is based on the neural logic network, which is capable of doing both pattern processing and logical inferencing. The authors discuss two strategies, pattern matching ACES and rule inferencing ACES. The pattern matching ACES makes use of past examples to construct its neural logic network and fine-tunes itself adaptively during its use by further examples supplied. The rule inferencing ACES conceptualizes new rules based on the frequencies of use on the rule-based neural logic network. A new rule could be considered as a pattern matching example and be incorporated into pattern matching ACES     

A graph-based approach for rule integrity and maintainability in expert system maintenance

Just as conventional software systems have maintenance costs far exceeding development costs, so too do rule-based expert systems. They are frequently developed by an incremental and iterative method, where knowledge and decision rules are extracted and added to the system in a piecemeal manner throughout system evolution. Thus, ensuring the correctness and consistency of the rule base (RB) becomes an important, though challenging task. However, most research work in expert systems has focused on building and validating rule bases, leaving the maintenance issue unexplored. We propose a graph-based approach, called the object classification model (OCM), as a methodology for RB maintenance. An experiment was conducted to compare the OCM with traditional RB maintenance methods. The results show that the OCM helps knowledge engineers retain rule-base integrity and, thus, increase rule-base maintainability.     

Rule-base structure identification in an adaptive-network-basedfuzzy inference system

We summarize Jang's architecture of employing an adaptive network and the Kalman filtering algorithm to identify the system parameters. Given a surface structure, the adaptively adjusted inference system performs well on a number of interpolation problems. We generalize Jang's basic model so that it can be used to solve classification problems by employing parameterized t-norms. We also enhance the model to include weights of importance so that feature selection becomes a component of the modeling scheme. Next, we discuss two ways of identifying system structures based on Jang's architecture: the top-down approach, and the bottom-up approach. We introduce a data structure, called a fuzzy binary boxtree, to organize rules so that the rule base can be matched against input signals with logarithmic efficiency. To preserve the advantage of parallel processing assumed in fuzzy rule-based inference systems, we give a parallel algorithm for pattern matching with a linear speedup. Moreover, as we consider the communication and storage cost of an interpolation model. We propose a rule combination mechanism to build a simplified version of the original rule base according to a given focus set. This scheme can be used in various situations of pattern representation or data compression, such as in image coding or in hierarchical pattern recognition     

Computer-Aided Systems Engineering Methodology Support and Its Effect on the Output of Structured Analysis

The information systems development literature indicates that there is no conclusive, empirical evidence that CASE improves the quality of system specifications or the resulting information systems. One role of a CASE tool is to serve as a methodology companion—to assist an analyst in the creation of documentation passed to succeeding phases of the life cycle, and to guide the analyst through a particular systems development methodology. A framework for comparing the level of methodology support provided by a CASE tool is proposed and applied to 27 structured analysis methodology rules. The framework contains seven levels of rule enforcement, ranging from real-time enforcement of a rule to the absence of enforcement for a rule. The methodology enforcement framework is then applied to two popular, commercial CASE tools. Each CASE tool was used by eight project teams over a two month period to construct a functional specification for a hotel information system. The goal of the study was to examine the influence on the functional specification of the level of methodology support provided by the CASE tool for each structured analysis methodology rule. Methodology errors in the system specification were noted for each structured analysis methodology rule. An analysis of the frequency of errors indicates that internal consistency rules are easily adhered to regardless of the level of methodology support provided by the CASE tool, while hierarchical consistency rules are adhered to more frequently in the presence of rigorous methodology support.     

Effect of rule weights in fuzzy rule-based classification systems

This paper examines the effect of rule weights in fuzzy rule-based classification systems. Each fuzzy IF-THEN rule in our classification system has antecedent linguistic values and a single consequent class. We use a fuzzy reasoning method based on a single winner rule in the classification phase. The winner rule for a new pattern is the fuzzy IF-THEN rule that has the maximum compatibility grade with the new pattern. When we use fuzzy IF-THEN rules with certainty grades, the winner is determined as the rule with the maximum product of the compatibility grade and the certainty grade. In this paper, the effect of rule weights is illustrated by drawing classification boundaries using fuzzy IF-THEN rules with/without certainty grades. It is also shown that certainty grades play an important role when a fuzzy rule-based classification system is a mixture of general rules and specific rules. Through computer simulations, we show that comprehensible fuzzy rule-based systems with high classification performance can be designed without modifying the membership functions of antecedent linguistic values when we use fuzzy IF-THEN rules with certainty grades     

A fuzzy Petri net-based expert system and its application to damageassessment of bridges

In this paper, a fuzzy Petri net approach to modeling fuzzy rule-based reasoning is proposed to bring together the possibilistic entailment and the fuzzy reasoning to handle uncertain and imprecise information. The three key components in our fuzzy rule-based reasoning-fuzzy propositions, truth-qualified fuzzy rules, and truth-qualified fuzzy facts-can be formulated as fuzzy places, uncertain transitions, and uncertain fuzzy tokens, respectively. Four types of uncertain transitions-inference, aggregation, duplication, and aggregation-duplication transitions-are introduced to fulfil the mechanism of fuzzy rule-based reasoning. A framework of integrated expert systems based on our fuzzy Petri net, called fuzzy Petri net-based expert system (FPNES), is implemented in Java. Major features of FPNES include knowledge representation through the use of hierarchical fuzzy Petri nets, a reasoning mechanism based on fuzzy Petri nets, and transformation of modularized fuzzy rule bases into hierarchical fuzzy Petri nets. An application to the damage assessment of the Da-Shi bridge in Taiwan is used as an illustrative example of FPNES.     

Performance evaluation of fuzzy rule-based classification systems obtained by multi-objective genetic algorithms

In this paper, we examine the classification performance of fuzzy if-then rules selected by a GA-based multi-objective rule selection method. This rule selection method can be applied to high-dimensional pattern classification problems with many continuous attributes by restricting the number of antecedent conditions of each candidate fuzzy if-then rule. As candidate rules, we only use fuzzy if-then rules with a small number of antecedent conditions. Thus it is easy for human users to understand each rule selected by our method. Our rule selection method has two objectives: to minimize the number of selected fuzzy if-then rules and to maximize the number of correctly classified patterns. In our multi-objective fuzzy rule selection problem, there exist several solutions (i.e., several rule sets) called “non-dominated solutions” because two conflicting objectives are considered. In this paper, we examine the performance of our GA-based rule selection method by computer simulations on a real-world pattern classification problem with many continuous attributes. First we examine the classification performance of our method for training patterns by computer simulations. Next we examine the generalization ability for test patterns. We show that a fuzzy rule-based classification system with an appropriate number of rules has high generalization ability.     

An algebraic approach to revising propositional rule-based knowledge bases

One of the important topics in knowledge base revision is to introduce an efficient implementation algorithm. Algebraic approaches have good characteristics and implementation method; they may be a choice to solve the problem. An algebraic approach is presented to revise propositional rule-based knowledge bases in this paper. A way is firstly introduced to transform a propositional rule-based knowledge base into a Petri net. A knowledge base is represented by a Petri net, and facts are represented by the initial marking. Thus, the consistency check of a knowledge base is equivalent to the reachability problem of Petri nets. The reachability of Petri nets can be decided by whether the state equation has a solution; hence the consistency check can also be implemented by algebraic approach. Furthermore, algorithms are introduced to revise a propositional rule-based knowledge base, as well as extended logic programming. Compared with related works, the algorithms presented in the paper are efficient, and the time complexities of these algorithms are polynomial.     

The effect of knowledge representation schemes on maintainabilityof knowledge-based systems

Uses an experimental approach to investigate the representational effects of knowledge on maintainability, and compares this with the structural effects of rule sets investigated by Davis (1990). Results show that an object-based system, compared to a structured rule-based system, was easier to maintain in terms of the time to do the maintenance tasks, but not necessarily in terms of accuracy of the alterations. However, in some instances, subsumption and redundancies were introduced into the rule-based system, which can cause problems for subsequent maintenance. Subjects perceived the structured rule-base system as more complex than the object-oriented system, and perceived the object structure as more useful than the rule modularization and documentation     

Fuzzy rule base systems verification using high-level Petri nets

In this paper, we propose a Petri nets formalism for the verification of rule-based systems. Typical structural errors in a rule-based system are redundancy, inconsistency, incompleteness, and circularity. Since our verification is based on Petri nets and their incidence matrix, we need to transform rules into a Petri nets first, then derive an incidence matrix from the net. In order to let fuzzy rule-based systems detect above the structural errors, we are presenting a Petri-nets-based mechanism. This mechanism consists of three phases: rule normalization, rules transformation, and rule verification. Rules will be first normalized into Horn clauses, then transform the normalized rules into a high-level Petri net, and finally we verify these normalized rules. In addition, we are presenting our approach to simulate the truth conditions which still hold after a transition firing and negation in Petri nets for rule base modeling. In this paper, we refer to fuzzy rules as the rules with certainty factors, the degree of truth is computed in an algebraic form based on state equation which can be implemented in matrix computation in Petri nets. Therefore, the fuzzy reasoning problems can be transformed as the liner equation problems that can be solved in parallel. We have implemented a Petri nets tool to realize the mechanism presented fuzzy rules in this paper.     

A software engineering methodology for rule-based systems

Current expert systems are typically difficult to change once they are built. The authors introduce a method for developing more easily maintainable rule-based expert systems, which is based on dividing the rules into groups and focusing attention on those facts that carry information between rules in different groups. They describe a new algorithm for grouping the rules of a knowledge base automatically and a notation set of software tools for the proposed method. The approach is supported by a study of the connectivity of rules and facts in rule-based systems; it is found that they indeed have the latent structure necessary for the programming methodology. Recent experimental results also support the approach. In contrast to the homogeneous way in which the facts of a rule-based system are usually viewed, this approach shows that certain facts are more important than others with regard to future modifications of the rules     

A Gaussian mixture model based discretization algorithm for associative classification of medical data

Knowledge-based systems such as expert systems are of particular interest in medical applications as extracted if-then rules can provide interpretable results. Various rule induction algorithms have been proposed to effectively extract knowledge from data, and they can be combined with classification methods to form rule-based classifiers. However, most of the rule-based classifiers can not directly handle numerical data such as blood pressure. A data preprocessing step called discretization is required to convert such numerical data into a categorical format. Existing discretization algorithms do not take into account the multimodal class densities of numerical variables in datasets, which may degrade the performance of rule-based classifiers. In this paper, a new Gaussian Mixture Model based Discretization Algorithm (GMBD) is proposed that preserve the most frequent patterns of the original dataset by taking into account the multimodal distribution of the numerical variables. The effectiveness of GMBD algorithm was verified using six publicly available medical datasets. According to the experimental results, the GMBD algorithm outperformed five other static discretization methods in terms of the number of generated rules and classification accuracy in the associative classification algorithm. Consequently, our proposed approach has a potential to enhance the performance of rule-based classifiers used in clinical expert systems.     

Patterns of self-stabilization in database consistency maintenance

A substantial portion of the database programming efforts are invested in integrity constraints enforcement. Traditionally, both the constraint semantics and their enforcement were embedded inside application programs. In recent years several studies have dealt with specifying integrity constraints as separate entities (e.g. rules), and relating the database consistency requirements to these rules. In this paper we deal with the complementary issue of stabilizing the database when update exceptions occur. While a simplistic approach is to abort any transaction that inflicts consistency violations, this is not always the desired action. We take advantage of the empirical observation that most of the exception-handling policies follow a small number of behavior patterns. Unlike some previous approaches that base their repair solution on syntactic analysis of the constraints and performance issues, we base our approach on the application semantics as reflected in these behavioral patterns. We describe a model that uses high-level abstractions called stabilizer types denoting these behavior patterns for consistency restorations, whose exact semantics is case dependent. It follows the fault tolerance's self-stabilization approach. An inference mechanism translates these abstractions into executable active rules. This approach provides high-level language to the exception handling portion of the application and substantially reduces the required programming.     

An Adjustable Description Quality Measure for Pattern Discovery Using the AQ Methodology

In concept learning and data mining tasks, the learner is typically faced with a choice of many possible hypotheses or patterns characterizing the input data. If one can assume that training data contain no noise, then the primary conditions a hypothesis must satisfy are consistency and completeness with regard to the data. In real-world applications, however, data are often noisy, and the insistence on the full completeness and consistency of the hypothesis is no longer valid. In such situations, the problem is to determine a hypothesis that represents the best trade-off between completeness and consistency. This paper presents an approach to this problem in which a learner seeks rules optimizing a rule quality criterion that combines the rule coverage (a measure of completeness) and training accuracy (a measure of inconsistency). These factors are combined into a single rule quality measure through a lexicographical evaluation functional (LEF). The method has been implemented in the AQ18 learning system for natural induction and pattern discovery, and compared with several other methods. Experiments have shown that the proposed method can be easily tailored to different problems and can simulate different rule learners by modifying the parameter of the rule quality criterion.     

Adaptive fuzzy rule-based classification systems

This paper proposes an adaptive method to construct a fuzzy rule-based classification system with high performance for pattern classification problems. The proposed method consists of two procedures: an error correction-based learning procedure, and an additional learning procedure. The error correction-based learning procedure adjusts the grade of certainty of each fuzzy rule by its classification performance. That is, when a pattern is misclassified by a particular fuzzy rule, the grade of certainty of that rule is decreased. On the contrary, when a pattern is correctly classified, the grade of certainty is increased. Because the error correction-based learning procedure is not meaningful after all the given patterns are correctly classified, we cannot adjust a classification boundary in such a case. To acquire a more intuitively acceptable boundary, we propose an additional learning procedure. We also propose a method for selecting significant fuzzy rules by pruning unnecessary fuzzy rules, which consists of the error correction-based learning procedure and the concept of forgetting. We can construct a compact fuzzy rule-based classification system with high performance