Knowledge-based Methodology Research Articles

An integrated approach to analyse system behaviour using knowledge-based approximate reasoning methodologies

Recent advances in technology and the growing complexity of systems have made the job of system-reliability engineers more challenging, because they have to analyse the uncertain behaviour of systems with the help of various analytical techniques that require the knowledge of precise numerical probabilities and component functional dependencies; the information for which is rather difficult to obtain. To cope with such situations, this paper presents the application of knowledge-based approximate reasoning methodologies, that is, fuzzy and grey approaches, as one of the most viable and effective tools for behaviour analysis of systems. An illustrative case from the paper industry is presented. Various parameters of importance to managerial decision-making, such as repair time, failure rate, mean time between failures, availability and expected number of failures related to system performance, are computed to quantify the behaviour in objective terms. Further, a risk-ranking approach-based on fuzzy and grey relational analysis is discussed to prioritise various failure causes associated with the components. Compared with the traditional Failure Mode and Effect Analysis (FMEA), the proposed fuzzy and grey approach provides more pragmatic results.

THE DEVELOPMENT OF AN EXPERT SYSTEM FOR MEASUREMENT SYSTEMS EVALUATION

This article examines and demonstrates the use of expert systems (ES) technology to augment and to enhance measurement systems evaluation procedures. It presents the results of a research project in which an expert system that utilizes a knowledge-based methodology to facilitate or to automate the process in conducting a gauging R&R analysis is developed. The paper begins with a discussion of the steps involved in a variable gauge study, pointing out that inefficiencies and inaccuracies often arise due to the lengthy time requirement and heavy statistical burden placed on the study team. The paper then describes the features and the functionality of the PC-based expert system developed in the research. Numerous screens are provided to illustrate these features. Finally, a synopsis of the primary benefits of the system is given, as well as its limitations that call for further research efforts.

Unsupervised learning techniques for fine-tuning fuzzy cognitive map causal links

Fuzzy Cognitive Maps (FCMs) constitute an attractive knowledge-based methodology, combining the robust properties of fuzzy logic and neural networks. FCMs represent causal knowledge as a signed directed graph with feedback and provide an intuitive framework which incorporates the experts’ knowledge. FCMs handle available information and knowledge from an abstract point of view. They develop behavioural model of the system exploiting the experience and knowledge of experts. The construction of FCMs is based mainly on experts who determine the structure of FCM, i.e. concepts and weighted interconnections among concepts. But this methodology may not be a sufficient model of the system because the human factor is not always reliable. Thus the FCM model of the system may requires restructuring which is achieved through adjustment the weights of FCM interconnections using specific learning algorithms for FCMs. In this article, two unsupervised learning algorithms are presented and compared for training FCMs; how they define, select or fine-tuning weights of the causal interconnections among concepts. The implementation and results of these unsupervised learning techniques for an industrial process control problem are discussed. The simulations results of training the process system verify the effectiveness, validity and advantageous characteristics of those learning techniques for FCMs.

Using statistical and knowledge-based approaches for literature-based discovery

The explosive growth in biomedical literature has made it difficult for researchers to keep up with advancements, even in their own narrow specializations. While researchers formulate new hypotheses to test, it is very important for them to identify connections to their work from other parts of the literature. However, the current volume of information has become a great barrier for this task and new automated tools are needed to help researchers identify new knowledge that bridges gaps across distinct sections of the literature. In this paper, we present a literature-based discovery system called LitLinker that incorporates knowledge-based methodologies with a statistical method to mine the biomedical literature for new, potentially causal connections between biomedical terms. We demonstrate LitLinker’s ability to capture novel and interesting connections between diseases and chemicals, drugs, genes, or molecular sequences from the published biomedical literature. We also evaluate LitLinker’s performance by using the information retrieval metrics of precision and recall.

Development of a Knowledge-Based Information Management System for Plant Maintenance

In this paper, a knowledge-based information system for the plant operation of steel making company has been proposed. The system, which is named as K-VRS(Knowledge-based Virtual Reality System), provides a connection between ERP plant maintenance module and knowledge-based engineering methodologies, and thus, enables network-based highly effective plant maintenance process. The developed system is expected to play a great role for more efficient and safer plant maintenance.

설비 관리를 위한 지식기반 정보관리 시스템의 개발

Recently, the importance of plant maintenance(PM) was highly raised to provide efficient plant operation which highly affects the productivity. For this reason, a number of engineering methodologies, such as risk-based inspection(RBI), fitness for service guidelines(FFS), plant lifecycle management(PLM), have been applied to improve the plant operation efficiency. Also, a network-based business operation system, which is called ERP(Enterprise Resource Planning), has been introduced in the field of plant maintenance. However, there was no attempt to connect engineering methodologies to the ERP PM system. In this paper, a knowledge-based information system for the plant operation of steel making company has been proposed. This system which is named as K-VRS(Knowledge-based Virtual Reality System), provides a connection between ERP plant maintenance module and knowledge-based engineering methodologies, and thus, enables network-based highly effective plant maintenance process. The developed system is expected to play a great role for more efficient and safer plant maintenance.

Expert computer knowledge-based methodology using fuzzy sets for evaluating water conservation programs in Jordan

This paper aims to devise a new methodology for evaluating water conservation programs based on expert computer knowledge-based systems and fuzzy set analyses. Computer-programmed surveys were carried out on a random sample of water users in Amman City, Jordan, before and after they received water educational material. Relative weights were given for each response and a cumulative grade was estimated. The follow-up surveys reveal that a reasonable rise of water-related knowledge is achieved after respondents are exposed to water educational material. Fuzzy set analyses reveal that some fluctuations in the relative rise of public awareness programs may be attributed to insufficient knowledge. The fuzzy set logic technique enabled us to condense a great many data into a small set of variable rules. This in turn made our expert system effective in evaluating water conservation programs.

A working prototype of a dynamic geographical information system

The objective of this work is to develop a dynamic geographical information system (DGIS) applicable for the simulation of spatio-temporal phenomena. DGIS is designed and implemented as a dynamic raster GIS in a fully integrated systems strategy using theory of modelling and simulation, discrete event systems specifications, and knowledge-based simulation methodologies. The approach accommodates the representations and operators of current GIS in a broader systems representation scheme that includes the constructs for handling continuous time, model base, multiple resolution, hierarchy, taxonomy, variable structure, and intelligent agents. DGIS is defined as an endomorphic system that can be transformed by the user into different, application-specific, spatial dynamic simulation environments depending on the models selected to fit the various aspects of its structure. In this paper we describe the theory, methods, and technologies upon which the system is designed and implemented and discuss how it can support complex spatial simulations. To probe operational applicability, an instance of DGIS is generated: a fire spread simulation environment, which is tested in the simulation of 15 real fire events.

FUZZY COGNITIVE MAPS MODEL FOR LARGE SCALE COMPLEX SYSTEMS

Most of the technological systems are characterized by complexity, coupled relationships with feedback and a demanding procedure to develop their models. This paper presents the Soft Computing technique of Fuzzy Cognitive Maps that are knowledge-based methodologies suitable to describe and model complex systems and handle with available information from an abstract point of view. Fuzzy cognitive Maps develop behavioral model of the system exploiting the experience and knowledge of experts. Fuzzy Cognitive Maps applicability in modeling complex systems is presented and a general hierarchical structure is proposed to model any complex system. Within the hierarchical structure, Fuzzy Cognitive Map models the coordinator of the system and develops an abstract conceptual model of the complex system.

A KNOWLEDGE-BASED ANSWER ENGINE FOR OPEN-DOMAIN QUESTIONS

This paper describes a knowledge-based methodology of mining textual answers from large collections of texts. We present an answer engine capable of providing answers to any question posed in natural language, regardless of the domain of interest. The answers are produced by combining semantic data from question taxonomies with information abductively inferred from texts. The high precision of the retrieved answers is due to novel, efficient knowledge processing techniques.

On compositional modelling

Many solutions to AI problems require the task to be represented in one of a multitude of rigorous mathematical formalisms. The construction of such mathematical models forms a difficult problem which is often left to the user of the problem-solver. This void between problem-solvers and their problems is studied by the eclectic field of automated modelling. Within this field, compositional modelling, a knowledge-based methodology for system-modelling, has established itself as a leading approach. In general, a compositional modeller organises knowledge in a structure of composable fragments that relate to particular system components or processes. Its embedded inference mechanism chooses the appropriate fragments with respect to a given problem, instantiates and assembles them into a consistent system model. Many different types of compositional modeller exist, however, with significant differences in their knowledge representation and approach to inference. This paper examines compositional modelling. It presents a general framework for building and analysing compositional modellers. Based on this framework, a number of influential compositional modellers are examined and compared. The paper also identifies the strengths and weaknesses of compositional modelling and discusses some typical applications.

A knowledge-based approach for handling exceptions in business processes

This paper describes a novel knowledge-based methodology and toolset for helping business process designers and participants better manage exceptions (unexpected deviations from an ideal sequence o...

Development of a case-based decision support system for process modeling in BPR

This paper presents a knowledge-based methodology for business process reengineering that uses a case-based reasoning paradigm to provide decision support to its users in the modeling of a current problem and a redesign of critical business processes. As a process modeling tool for representing the business process, the event process chain (EPC) modeling method is used in this paper. We developed a CAPMOSS (CAse-based Process MOdeling Supporting System) to support our proposed methodology. To reengineer a new business process problem, CAPMOSS retrieves from its case base the case that is most similar to the current problem. CAPMOSS uses a retrieved case to guide the structuring of AS-IS models and TO-BE models of a target business process. Using the transformational knowledge of a retrieved case, CAPMOSS helps the user to transform an AS-IS model into a TO-BE model for the target process with ease and the purchasing process in a government R&D institute is explained as an application of this approach. Copyright © 2000 John Wiley & Sons, Ltd.

Knowledge-based language for modeling linear programming problems

The innovative aspect of this research is to apply knowledge-based methodology to model linear programming problems. This article introduces a graphic and declarative language, and shows the modeling process from the knowledge acquisition to the automatic generation of the algebraic model to be used by a solver.

A knowledge-based methodology for supporting multilingual and user-tailored interfaces

The need for multilingual and user-tailored interfaces imposes new requirements upon the software industry: software applications must “speak” the language of users. Language engineering and knowledge engineering can assist the development of such interfaces. This paper presents a methodology for the creation of a language-independent knowledge base (KB), which can be used for the development of multilingual and user-tailored interfaces. This KB contains knowledge about the user interface components and functions and its creation is part of a software internationalisation process. The methodology aims at reducing the cost of setting up and managing this KB, by exploiting the benefits of controlled language use in technical writing. A case study for the dynamic generation of multilingual and user-tailored diagnostic messages is presented. Finally, the paper discusses related approaches in the area of multilinguality as well as in the area of software internationalisation and localisation, summarises the main results, and presents our plans for further exploitation of the methodology.

Manufacturing flexibility measurement: a fuzzy logic framework

Flexibility is recognized as an important feature in manufacturing. This paper suggests a knowledge-based methodology for the measurement of manufacturing flexibility. We claim that flexibility is an inherently vague notion and an essential requirement in its measurement is the involvement of human perception and belief. Nine different flexibility types are measured, while the overall flexibility is given as the combined effect of these types. Knowledge is represented via IF (fuzzy antecedents) THEN (fuzzy consequent) rules, which are used to model the functional dependencies between operational characteristics, such as setup time and cost, versatility, part variety, transfer speed, etc. The proposed scheme is illustrated through an example.

On-line decisions about permitted/protected left-hand turns in distributed traffic signal control

A long-term project was started some time ago, using a distributed and knowledge-based methodology for realtime, traffic-adaptive control for streets and highway ramps. In this paper, it is argued that the traffic scenarios fully determine whether a protected or a permitted left-hand turn is needed. A flexible and inexpensive on-line decision methodology is described, concerning the phasing regime at an intersection. Simulation experiments have shown that this integrated Intersection Analyzer, coupled with a signaling (phasing and timing) system, performs better than the empirical “50,000 rule” described by McShane and Roess (Traffic Engineering, Prentice Hall, Englewood Cliffs, NJ, 1990) in all configurations and traffic densities. While some of the expert systems described in the literature may approximate the quality of the new system in some instances, they lack the flexibility and generality of the approach described in this paper.

Knowledge-based methodology for intelligent sequence switching, fault identification and service restoration of distribution system

In this paper a new approach to intelligent sequence switching in substations and service restoration of distribution system using finite stochastic processes in conditional probability is presented. The technique exploits the results of probability by initially predicting the future states of the nodes in the search procedure. The knowledge base is handled through probabilistic reasoning. This is coupled with direct reasoning or searching. This improves the reliability of the heuristics. The fault identifier uses deductive logic for identifying the fault point. The proposed technique is tested for a 66/11 kV substation, having 4 U.G cable feeders with 36 load points and 8 RTUs, in Mysore, South India.

Development of a hull form definition tool with a related knowledge-based advisory system

The development of a parametric hull form design system utilizing combined knowledge-based and objectoriented methodologies, approaches, and techniques is the major purpose of this research. One objective was the development of a powerful, intuitive, and parameter-based hull definition system with a related hull definition advisory system that would provide useful and necessary advice to the user. Object and knowledge modeling activities were conducted during the analysis and design stage to facilitate the implementation of a hull design program and advisory system. The hull design system includes the generation of a geometric model from a process of aggregating simple user-selected shapes representing different boundaries. Design involves the capability to vary simple parameters such as entrance angles, length-breadth ratios, and radii to define local and general surface geometries. Hydrostatic calculations are included for analysis; results are referred to an advisory system for evaluation and the determination of the acceptability of parameters and analysis results. The advisory system includes a knowledge bases consisting of hull form databases, results of the statistical analysis of data, design parameter constraints, and expert knowledge acquired from designers, reference materials, and technical reports. Use of the design tool with the advisory system results in the rapid generation of hull forms and associated design evaluations. Flexibility in decision making results from the separation of the design system from the advisory system. The independence of the two systems allows for a wide range of user experience with experts using the design tool with minimal reference to the advisory system. On the other hand, the expertise and knowledge stored in the advisory system is fully available to inexperienced designers and users.

Development of distributed problem solving systems for dynamic environments

As knowledge-based techniques are introduced in supervision and control applications, the need for a structured, methodological approach in the development of these systems increases. Current knowledge-based systems development methodologies are founded on individualistic models of expertise, and cannot face the challenge of interacting with humans or other systems in dynamic application environments. This article introduces a development method that combines models of expertise with interaction-based distributed artificial intelligence. The applicability of the method is demonstrated on an example of supervision of a power transmission network. The analysis of the proposed system is based on the KADS methodology. The design phase is structured in a series of steps: first, the domain description is used to select a model of distributed problem solving. Then, problem constraints and a set of design rules guide the mapping of the analysis' entities to a design solution. Design decisions are justified at every step. Alternative design proposals are experimentally compared with the aid of a multi-agent platform and a simulator. The experimental results indicate possible design refinements. Finally, the generality of the approach is discussed and future research directions are indicated. >