Interpreting and extracting fuzzy decision rules from fuzzy information systems and their inference

Information systems, which contain only crisp data, precise and unique attribute values for all objects, have been widely investigated. Due to the fact that in realworld applications imprecise data are abundant, uncertainty is inherent in real information systems. In this paper, information systems are called fuzzy information systems, and formalized by (objects; attributes; f), in which f is a fuzzy set and expresses some uncertainty between an object and its attribute values. To interpret and extract fuzzy decision rules from fuzzy information systems, the meta-theory based on modal logic proposed by Resconi et al. is modified. The modified meta-theory not only expresses uncertainty between objects and their attributes, but also uncertainty in the process of recognizing fuzzy information systems. In addition, according to perception computing (proposed by Zadeh), granules of fuzzy information systems can be represented by fuzzy decision rules, so that, fuzzy inference methods can be used to obtain the decision attribute of a new object. Finally, a novel way of combining evidences based on the modified meta-theory is introduced, which extends the concept of combining evidences based on Dempster-Shafer theory.     

Rule inferencing and object-orientation for boundary elements mesh design

In this paper, object orientation and rule inferencing are used to automate the design of boundary element meshes. The work is part of an effort to develop an object-oriented integrated environment for the damage tolerance design of aircraft stiffened panels and other parts such as fuselage lap-joints. An inference domain class library has been developed. Inference domain objects are used to create symbolic representations for the structural configuration and for production rules that describe the mesh design strategy. An objectoriented inference engine is utilized to apply this rule set to the knowledge base where the symbolic representation of the structural configuration is stored. The technique was implemented using the C++ programming language.     

基于优势-直觉模糊粗糙模型的IT项目绩效审计评价规则获取

针对我国政府审计机关对政府投资的I`I}项目进行绩效审计评价规则知识获取的困难,考虑了条件属性取值 为优势精确值、分类结果为直觉模糊值的决策系统规则获取问题。首先比较条件属性值的大小,构建对象的优势部 域,再由对象邻域的直觉模糊值确定对象的上下近似;根据对象的上下近似和不同对象的直觉模糊值确定对象间的区 分关系,利用分辫矩阵给出知识约简和规则提取算法;最后将直觉模糊粗糙模型应用于政府I"I'项目绩效审计评价规 则的获取,得到了较为合理的IT项目绩效评价规则。     

Fuzzy inference mechanism for recognition of contact states in intelligent robotic assembly

This paper presents a methodology for generating a fuzzy inference mechanism (FIM) for recognizing contact states within robotic part mating using active compliant motion. In the part mating process, significant uncertainties are inherently present. As a result it is pertinent that contact states recognition systems operating in such environment be able to make decisions on the contact state currently present in the process, based on data full of uncertainties and imprecision. In such conditions, implementation of fuzzy logic and interval inference brings significant robustness to the system. As a starting point for FIM generation, we use a quasi-static model of the mating force between objects. By applying Discrete Wavelet Transform to the signal generated using this model, we extract qualitative and representative features for classification into contact states. Thus, the obtained patterns are optimally classified using support vector machines (SVM). We exploit the equivalence of SVM and Takagi–Sugeno fuzzy rules based systems for generation of FIM for classification into contact states. In this way, crisp granulation of the feature space obtained using SVM is replaced by optimal fuzzy granulation and robustness of the recognition system is significantly increased. The information machine for contact states recognition that is designed using the given methodology simultaneously uses the advantages of creation of machine based on the process model and the advantages of application of FIM. Unlike the common methods, our approach for creating a knowledge base for the inference machine is neither heuristic, intuitive nor empirical. The proposed methodology was elaborated and experimentally tested using an example of a cylindrical peg in hole as a typical benchmark test.     

Simulation and knowledge based objects

A knowledge-based simulation model was developed by using a hypertext-like semi-object-oriented environment (SuperCard^TM). This environment enabled the building of a tightly coupled knowledge representation scheme and simulation algorithm. Our knowledge-based simulation model combines three different paradigms: production rules, an inference engine, and knowledge-based objects. The production rule paradigm is used as the basis for describing and coding the processes to be simulated. The knowledge-based objects are built as the active objects of the simulation. A knowledge-based object has a number of rules that support its behavior and an agenda that defines its goals. A knowledge-based object then proceeds in the simulation process (via the inference engine) until its goals are achieved. Since an inference engine with a knowledge base is built into the simulation process, such a system has the ability to reason about the behavior of the model. In addition, the knowledge-based simulation model has a friendly user interface. This enables changing the parameters of the simulation model without doing any programming. The inference engine in the knowledge-based simulation combines backward chaining, decision lattices, and links so as to produce an overall pattern of inference that is as efficient as possible.     

A probabilistic interpretation of the medical expert system CADIAG-2

CADIAG-2 is a well-known expert system aimed at providing support for medical diagnose in the field of internal medicine. CADIAG-2 consists of a knowledge base in the form of a set of IF-THEN rules that relate distinct medical entities, in this paper interpreted as conditional probabilistic statements, and an inference engine constructed upon methods of fuzzy set theory. The aim underlying this paper is the understanding of the logical structure of the inference in CADIAG-2. To that purpose, we provide a (probabilistic) logical formalisation of the inference of the system and check its adequacy with probabilistic logic.     

Formalization of object-oriented database model with rules

The paper investigates knowledge representation in an object-oriented database management system first within the data model with rules and second in the computational model by using logic. Issues of structure, integrity, and retrieval are focused on. The proposed system provides object-oriented concepts for describing complex structured data, rules for expressing object-dependent constraints and object associations, and, finally, logic for inference and retrieval.     

Knowledge object modeling

Knowledge objects are an integration of the object-oriented paradigm with logic rules. The proper integration provides a flexible and powerful environment, as rule-based components provide facilities for deductive retrieval and pattern matching, and object-oriented components provide a clear intuitive structure for programs in the form of class hierarchies. Based on the knowledge object concept, this paper presents a factor-centered representation language, Factor++, which models logic rules into the object-oriented paradigm, and a scheduling system, Schedular, using the Factor++ framework The construction of Schedular demonstrates that by using an object-oriented representation of knowledge objects, users can be given explicit control of the object hierarchy to customize the system to their particular needs, which includes letting users select among scheduling and other methods. In Schedular, rules are designed as derivation rules and constraint rules. The purpose of rules is either to restrict object structure and behavior, or to infer new data from the existing data. Rules are arranged in positions so that object methods are automatically firing up if environment changes are detected by these rules     

Fuzzy rule-based reasoning approach for event detection and annotation of broadcast soccer video

This paper presents an approach for event detection and annotation of broadcast soccer video. It benefits from the fact that occurrence of some audiovisual features demonstrates remarkable patterns for detection of semantic events. However, the goal of this paper is to propose a flexible system that can be able to be used with minimum reliance on predefined sequences of features and domain knowledge derivative structures. To achieve this goal, we design a fuzzy rule-based reasoning system as a classifier which adopts statistical information from a set of audiovisual features as its crisp input values and produces semantic concepts corresponding to the occurred events. A set of tuples is created by discretization and fuzzification of continuous feature vectors derived from the training data. We extract the hidden knowledge among the tuples and correlation between the features and related events by constructing a decision tree (DT). A set of fuzzy rules is generated by traversing each path from root toward leaf nodes of constructed DT. These rules are inserted in fuzzy rule base of designed fuzzy system and employed by fuzzy inference engine to perform decision-making process and predict the occurred events in input video. Experimental results conducted on a large set of broadcast soccer videos demonstrate the effectiveness of the proposed approach.     

PREPARE: a tool for knowledge base verification

The knowledge base is the most important component in a knowledge-based system. Because a knowledge base is often built in an incremental, piecemeal fashion, potential errors may be inadvertently brought into it. One of the critical issues in developing reliable knowledge-based systems is how to verify the correctness of a knowledge base. The paper describes an automated tool called PREPARE for detecting potential errors in a knowledge base. PREPARE is based on modeling a knowledge base by using a predicate/transition net representation. Inconsistent, redundant, subsumed, circular, and incomplete rules in a knowledge base are then defined as patterns of the predicate/transition net model, and are detected through a syntactic pattern recognition method. The research results to date have indicated that: the methodology ran be adopted in knowledge-based systems where logic is used as knowledge representation formalism; the tool can be invoked at any stage of the system's development, even without a fully functioning inference engine; the predicate/transition net model of knowledge bases is easy to implement and provides a clear and understandable display of the knowledge to be used by the system     

面向后件集的模糊推理机制: 用于区间型type-2模糊逻辑系统

目前,大多数模糊推理都是利用t-范数和t-余范数或其改进形式对连接词进行建模,这些模型不能将模糊规则中前件集与后件集之间的相关性信息引入到模糊推理过程,这会丢失蕴含在规则中的一些信息甚至导致推理结果与实际经验严重不符.为解决此问题,本文首先引入模糊集合面向对象变换的概念,并将其推广,建立了合成type-2模糊集合模型.基于此模型,针对区间型type-2模糊逻辑系统,提出一种面向后件集的模糊推理机制,该机制能将前件集与后件集的相关性信息(包括清晰数和模糊数两种情形)引入到模糊推理过程.仿真结果表明,该方法能捕获到模糊规则中更多的不确定性信息,并为模糊逻辑系统的设计提供更大的自由度.     

Intelligent Fril/SQL interrogator

The intelligent Fril/SQL interrogator is an object‐oriented and knowledge‐based support query system, which is implemented by the set of logic objects linking one another. These logic objects integrate SQL query, support logic programming language—Fril and Fril query together by processing them in sequence in slots of each logic object. This approach therefore takes advantage of both object‐oriented system and a logic programming‐based system. Fuzzy logic data mining and a machine learning tool kit built in the intelligent interrogator can automatically provide a knowledge base or rules to assist a human to analyze huge data sets or create intelligent controllers. Alternatively, users can write or edit the knowledge base or rules according to their requirements, so that the intelligent interrogator is also a support logic programming environment where users can write and run various Fril programs through these logic objects. © 2007 Wiley Periodicals, Inc. Int J Int Syst 22: 279–302, 2007.     

Issues in an inference platform for generating deductive knowledge: a case study in cultural heritage digital libraries using the CIDOC CRM

Most information retrieval research focuses collecting documents that match the same set of concepts. This study considers a more advanced problem, namely how to discover knowledge not contained in a single source from combined historical facts. By using a well-designed core ontology in the cultural domain (CIDOC CRM, ISO21127), this study discusses the requirement for a robust inference platform for real-life knowledge discovery and integration over distributed sources. The methodology and design are justified in detail through functional requirements for an inference service with the capability of inferring new knowledge from combinations of facts distributed over different sources. A number of critical issues for developing such a robust inference platform are identified, namely (1) systematic accumulation of common concepts and inference rules; (2) extending the ontology with metaclasses; (3) accumulation of factual and categorical knowledge; (4) incorporation of fuzzy inference into the inference engine, and (5) improvement of performance and scalability in the inference engine.     

专家系统中基于粗集的知识获取、更新与推理

知识获取、知识更新和不确定性推理是设计专家系统的重要方面。根据粗集理论，提出了一种专家系统的结构模型，该系统在规则获取的基础上，利用系统运行的实例增量式地更新知识库中的规则及其参数，以改善系统的性能，利用知识库中的规则及数量参数进行不确定性推理，得出结论的可信度。     

Support logic programming

This article describes a support logic programming system which uses a theory of support pairs to model various forms of uncertainty. It should find application to designing expert systems and is of a query language type like Prolog. Uncertainty associated with facts and rules is represented by a pair of supports and uses ideas from Zadeh's fuzzy set theory and Shafer's evidence theory. A calculus is derived for such a system and various models of interpretation given. the article provides a form of knowledge representation and inference under uncertainty suitable for expert systems and a closed world assumption is not assumed. Facts not in the knowledge base are uncertain rather than assumed to be false.     

Complex object comparison in a fuzzy context

The comparison concept plays a determining role in many problems related to object management in an Object-Oriented Database Model. Object comparison is appropriately managed in a crisp object-oriented context by means of the concepts of identity and value equality. However, when dealing with imprecise or imperfect objects, questions like ‘To which extent may two objects be the same one?’ or ‘How similar are two objects?’ have not a clear answer, because the equality concept becomes fuzzy. In this paper we present a set of operators that are useful when comparing objects in a fuzzy environment. In particular, we introduce a generalized resemblance degree between two fuzzy sets of imprecise objects and a generalized resemblance degree to compare complex fuzzy objects within a given class.