On the incorporation of interval-valued fuzzy sets into the Bousi-Prolog system: declarative semantics, implementation and applications

In this paper we analyse the benefits of incorporating interval-valued fuzzy sets into the Bousi-Prolog system. A syntax, declarative semantics and im- plementation for this extension is presented and formalised. We show, by using potential applications, that fuzzy logic programming frameworks enhanced with them can correctly work together with lexical resources and ontologies in order to improve their capabilities for knowledge representation and reasoning

Fuzzy expert systems in civil engineering

Imperial Users onl

XESS: The XML expert system shell

The XML Expert System Shell (XESS) was designed to alleviate some of the difficulties associated with translating a knowledge base from one expert system to another. The major goal of XESS is to allow programmers to model an expert system, complete with traditional facts and rules, in an XML-based language that leverages the universally understood terms used when teaching artificial intelligence to students. XML, the extensible markup language, is a text-based standard for information interchange between disparate systems1; it was originally designed to represent data in an easily parsable, human readable format2. While some extensions of the XML specification, particularly the Simple Object Access Protocol (SOAP), have long since abandoned human readability, the core XML specification is still used frequently to produce documents that can easily be exchanged between computational platforms and created or understood by human beings. The XESS-XML language inherits all of the usability of XML; it can be edited by hand in any text editor, is human readable, and can be parsed using XML parsers commonly available in any modern programming language. The XML Schema specification provides a mechanism for explicitly defining the content of an XML document so that a document can be validated3,4,5. XML schemas specify the make-up of an XML document in exacting detail6, using a pseudo-object-oriented syntax to specify exactly which entities are allowed in the document, the attributes of those entities, where they are allowed in the document, and how often they may occur. The XESS-XML language is defined as a fully extensible XML Schema, which can be used to validate any knowledge base written in the language. The Schema provides entities for common facts (e.g. predictes, structs) and a robust syntax for expressing rules in an if-then-else format, as well as the actions that should be taken in the event that a rule is fired. Additionally, because XML schemas are fully extensible, the XESS schema may be extended to add additional functionality such as support for fuzzy logic, new clause types, or new actions to be taken when rules are fired. In addition to the XML language, XESS also includes an object oriented interpreter specification that defines a robust set of language independent APIs for interacting with the expert system. This interpreter specification is meant to set expectations, both for XESS developers and users, as to the features provided by the XESS API regardless of the language in which the interpreter has been implemented. As part of the specification, the XESS API also provides object oriented definitions for XESS plug-ins; a plug-in is capable of translating from an XESS document to the native language of a specific expert system shell in a generic way (i.e. not specific to any one rule set) and back again. This allows users to express custom expert system shells in the XESS-XML language, parse them using an XESS interpreter written in any language, and translate them to a specific expert system shell through the use of an XESS plug-in without needing to learn the specific expert system shell language or rewriting the knowledge base once for each shell tested

Cogitator : a parallel, fuzzy, database-driven expert system

The quest to build anthropomorphic machines has led researchers to focus on knowledge and the manipulation thereof. Recently, the expert system was proposed as a solution, working well in small, well understood domains. However these initial attempts highlighted the tedious process associated with building systems to display intelligence, the most notable being the Knowledge Acquisition Bottleneck. Attempts to circumvent this problem have led researchers to propose the use of machine learning databases as a source of knowledge. Attempts to utilise databases as sources of knowledge has led to the development Database-Driven Expert Systems. Furthermore, it has been ascertained that a requisite for intelligent systems is powerful computation. In response to these problems and proposals, a new type of database-driven expert system, Cogitator is proposed. It is shown to circumvent the Knowledge Acquisition Bottleneck and posess many other advantages over both traditional expert systems and connectionist systems, whilst having non-serious disadvantages.KMBT_22

Fuzzy Knowledge Based Reliability Evaluation and Its Application to Power Generating System

PhDThe method of using Fuzzy Sets Theory(FST) and Fuzzy Reasoning(FR) to aid reliability evaluation in a complex and uncertain environment is studied, with special reference to electrical power generating system reliability evaluation. Device(component) reliability prediction contributes significantly to a system's reliability through their ability to identify source and causes of unreliability. The main factors which affect reliability are identified in Reliability Prediction Process(RPP). However, the relation between reliability and each affecting factor is not a necessary and sufficient one. It is difficult to express this kind of relation precisely in terms of quantitative mathematics. It is acknowledged that human experts possesses some special characteristics that enable them to learn and reason in a vague and fuzzy environment based on their experience. Therefore, reliability prediction can be classified as a human engineer oriented decision process. A fuzzy knowledge based reliability prediction framework, in which speciality rather than generality is emphasised, is proposed in the first part of the thesis. For this purpose, various factors affected device reliability are investigated and the knowledge trees for predicting three reliability indices, i.e. failure rate, maintenance time and human error rate are presented. Human experts' empirical and heuristic knowledge are represented by fuzzy linguistic rules and fuzzy compositional rule of inference is employed as inference tool. Two approaches to system reliability evaluation are presented in the second part of this thesis. In first approach, fuzzy arithmetic are conducted as the foundation for system reliability evaluation under the fuzzy envimnment The objective is to extend the underlying fuzzy concept into strict mathematics framework in order to arrive at decision on system adequacy based on imprecise and qualitative information. To achieve this, various reliability indices are modelled as Trapezoidal Fuzzy Numbers(TFN) and are proceeded by extended fuzzy arithmetic operators. In second approach, the knowledge of system reliability evaluation are modelled in the form of fuzzy combination production rules and device combination sequence control algorithm. System reliability are evaluated by using fuzzy inference system. Comparison of two approaches are carried out through case studies. As an application, power generating system reliability adequacy is studied. Under the assumption that both unit reliability data and load data are subjectively estimated, these fuzzy data are modelled as triangular fuzzy numbers, fuzzy capacity outage model and fuzzy load model are developed by using fuzzy arithmetic operations. Power generating system adequacy is evaluated by convoluting fuzzy capacity outage model with fuzzy load model. A fuzzy risk index named "Possibility Of Load Loss" (POLL) is defined based on the concept of fuzzy containment The proposed new index is tested on IEEE Reliability Test System (RTS) and satisfactory results are obtained Finally, the implementation issues of Fuzzy Rule Based Expert System Shell (FRBESS) are reported. The application of ERBESS to device reliability prediction and system reliability evaluation is discussed

Aspects of functional programming

This thesis explores the application of functional programming in new areas and its implementation using new technologies. We show how functional languages can be used to implement solutions to problems in fuzzy logic using a number of languages: Haskell, Ginger and Aladin. A compiler for the weakly-typed, lazy language Ginger is developed using Java byte-code as its target code. This is used as the inspiration for an implementation of Aladin, a simple functional language which has two novel features: its primitives are designed to be written in any language, and evaluation is controlled by declaring the strictness of all functions. Efficient denotational and operational semantics are given for this machine and an implementation is devel- oped using these semantics. We then show that by using the advantages of Aladin (simplicity and strictness control) we can employ partial evaluation to achieve con- siderable speed-ups in the running times of Aladin programs

The ciao system

Abstract is not available