Abstract: A belief rule-base inference methodology using the evidential reasoning approach (RIMER) has been developed recently, where a new belief rule representation scheme is proposed to extend traditional IF-THEN rules. The belief rule expression matrix in RIMER provides a compact framework for representing expert knowledge. However, it is difficult to accurately determine the parameters of a belief rule base (BRB) entirely subjectively, particularly, for a large-scale BRB with hundreds or even thousands of rules. In addition, a change in rule weight or attribute weight may lead to changes in the performance of a BRB. As such, there is a need to develop a supporting mechanism that can be used to train, in a locally optimal way, a BRB that is initially built using expert knowledge. In this paper, several new optimization models for locally training a BRB are developed. The new models are either single- or multiple-objective nonlinear optimization problems. The main feature of these new models is that only partial input and output information is required, which can be either incomplete or vague, either numerical or judgmental, or mixed. The models can be used to fine tune a BRB whose internal structure is initially decided by experts' domain-specific knowledge or common sense judgments. As such, a wide range of knowledge representation schemes can be handled, thereby facilitating the construction of various types of BRB systems. Conclusions drawn from such a trained BRB with partially built-in expert knowledge can simulate real situations in a meaningful, consistent, and locally optimal way. A numerical study for a hierarchical rule base is examined to demonstrate how the new models can be implemented as well as their potential applications.

Abstract: We consider approximate inference in hybrid Bayesian Networks (BNs) and present a new iterative algorithm that efficiently combines dynamic discretization with robust propagation algorithms on junction trees. Our approach offers a significant extension to Bayesian Network theory and practice by offering a flexible way of modeling continuous nodes in BNs conditioned on complex configurations of evidence and intermixed with discrete nodes as both parents and children of continuous nodes. Our algorithm is implemented in a commercial Bayesian Network software package, AgenaRisk, which allows model construction and testing to be carried out easily. The results from the empirical trials clearly show how our software can deal effectively with different type of hybrid models containing elements of expert judgment as well as statistical inference. In particular, we show how the rapid convergence of the algorithm towards zones of high probability density, make robust inference analysis possible even in situations where, due to the lack of information in both prior and data, robust sampling becomes unfeasible.

Abstract: From the Publisher: Expert Systems in Construction and Structural Engineering is a valuable reference both for researchers interested in the state-of-the-art of civil engineering expert systems, and practitioners interested in exploring the practical applications of this new technology.

Abstract: Architectural and design patterns represent effective techniques to package expert knowledge in a reusable way. Over time, they have proven to be very successful in software engineering. Moreover, in the security discipline, a well-known principle calls for the use of standard, time- tested solutions rather than inventing ad-hoc solutions from scratch. Clearly, security patterns provide a way to adhere to this principle. However, their adoption does not live up to their potential. To understand the reasons, this paper analyzes an extensive set of published security patterns according to several dimensions and outlines the directions for improvement.

Abstract: In this paper we describe a data mining approach for detection of anomalous vessel behaviour. The suggested approach is based on Bayesian networks which have two important advantages compared to opaque machine learning techniques such as neural networks: (1) possibility to easily include expert knowledge into the model, and (2) possibility for humans to understand and interpret the learned model. Our approach is implemented and tested on synthetic data, where initial results show that it can be used for detection of single-object anomalies such as speeding.

Abstract: Selection of an optimal non-traditional machining (NTM) process for generating a desired feature on a given material requires the consideration of several factors among which the type of the workpiece material and shape to be machined are the most significant ones. This paper presents a quality function deployment (QFD) based methodology to ease out the optimal NTM process selection procedure. It includes the design of a QFD-based expert system that can automate the decision making process with the help of graphical user interfaces and visual aids. The developed expert system employs the use of a house of quality (HOQ) matrix for comparison of the relevant product and process characteristics. The weights obtained for various process characteristics are utilized to estimate an overall score for each of the NTM processes. Finally, if some of the NTM processes satisfy certain critical criteria, they are again compared with each other on the basis of their overall scores and the process having the maximum score is selected as the optimal choice.

Abstract: Decision support systems are often not empirically evaluated, especially the underlying modelling components. This can be attributed to such systems necessarily being designed to handle complex and poorly structured problems and decision making. Nonetheless, evaluation is critical and should be focused on empirical testing whenever possible. Verification and validation, in combination, comprise such evaluation. Verification is ensuring that the system is internally complete, coherent, and logical from a modelling and programming perspective. Validation is examining whether the system is realistic and useful to the user or decision maker, and should answer the question: “Was the system successful at addressing its intended purpose?” A rich literature exists on verification and validation of expert systems and other artificial intelligence methods; however, no single evaluation methodology has emerged as preeminent. At least five approaches to validation are feasible. First, under some conditions, decision support system performance can be tested against a preselected gold standard. Second, real-time and historic data sets can be used for comparison with simulated output. Third, panels of experts can be judiciously used, but often are not an option in some ecological domains. Fourth, sensitivity analysis of system outputs in relation to inputs can be informative. Fifth, when validation of a complete system is impossible, examining major components can be substituted, recognizing the potential pitfalls. I provide an example of evaluation of a decision support system for trumpeter swan (Cygnus buccinator) management that I developed using interacting intelligent agents, expert systems, and a queuing system. Predicted swan distributions over a 13-year period were assessed against observed numbers. Population survey numbers and banding (ringing) studies may provide long term data useful in empirical evaluation of decision support.

Abstract: In this work, an expert system has been designed for selecting suppliers in the supply chain management area. A supply chain management system has been designed and an expert system tool has been developed for supplier selection process. Although supply chain management might be established for almost any sector, the automotive sector has been selected for this research. One of the most crucial steps of supply chain management is to determine supplier selection criteria and weigh factors to use during this process. A questionnaire has been implemented in the region of Sakarya among 19 top managers of medium and large sized factories. According to the results obtained from this work an expert system is a suitable tool for selecting suppliers.

Abstract: Chinese Medicine (CM) has been recognized as the popular complementary medicine in the world and the abundance of healing knowledge has been transmitted and enriched from generation to generation. The well-recorded archive of Chinese medical knowledge has grown and developed into very detailed theory and philosophy together with a comprehensive and subtle system of diagnosis. Recently, the Internet has been quite often used in a variety of applications for medical purpose, i.e. to provide real-time and platform-independent access which can build and share knowledge about clinical diagnosis experiences, knacks, diseases treatments, and even prescriptions. In this respect, there is a real need of an intelligent system for Chinese medical physician or education because Chinese medicine has evolved complex methods of diagnosis and treatment tailored to the individual’s subtle patterns of disharmony. This allows it not only to treat fully manifest diseases, but also to assist in maintaining health and balance to prevent illnesses from occurring. Ontologies become an important mechanism to build knowledge-based systems. The role of ontologies is to capture domain knowledge and provide a commonly agreed upon understanding of a domain. The advantages include the sharing and re-use of knowledge, and the better engineering of knowledge-based system with respect to acquisition, verification, and maintenance. In this paper, we propose a unifying framework for intelligent disease diagnosis system named CMDS – Chinese Medical Diagnostic System where the medical ontology has been integrated for the system development, and the methodologies of its implementation for digestive health. CMDS uses web interface and expert system technology to act as human expertise and diagnose a number of digestive system diseases. Besides, to efficiently elicit the knowledge of digestive system from domain experts and construct the medical ontology, a hybrid knowledge acquisition strategy is proposed. CMDS provides a truly precise analysis for digestive system disease and the prototype system can diagnose up to 50 types of diseases amongst 10 species of primary digestive system, and just uses over 500 rules and 600 images for various diseases. The satisfactory performance of the system has proven that it could serve the educational purpose, act as a consultant role and the functionality can be extended to the whole body health system.

Abstract: In the present study, a fault diagnosis system using acoustic emission with an adaptive order tracking technique and fuzzy-logic interference for a scooter platform is described. Order tracking of acoustic or vibration signal is a well-known technique that can be used for fault diagnosis of rotating machinery. Unfortunately, most of the conventional order-tracking methods are primarily based on Fourier analysis with the revolution of the machinery. Thus, the frequency smearing effect often arises in some critical conditions. In the present study, the order tracking problem is treated as the tracking of frequency-varying bandpass signals and the order amplitudes can be calculated with high resolution. The order amplitude figures are then used for creating the data bank in the proposed intelligent fault diagnosis system. A fuzzy-logic inference is proposed to develop the diagnostic rules of the data base in the present fault diagnosis system. The experimental works are carried to evaluate the effect of the proposed system for fault diagnosis in a scooter platform under various operation conditions. The experimental results indicated that the proposed expert system is effective for increasing accuracy in fault diagnosis of scooters.

Abstract: This paper describes a new technique based on rough sets to extract decision rules from large volumes of data captured by protection, control, and monitoring intelligent electronic devices. The methodology correctly identifies faults from large datasets and could be used to assist operators in their decision-making processes. Building knowledge for a fault diagnostic system is a time-consuming and costly process. The quality of a knowledge base can sometimes be hampered by a large number of superfluous decision-making rules that can lead to an unnecessarily large knowledge base system and inefficient or even detrimental rule maintenance. The methodology proposed cannot only induce decision rules efficiently but can also reduce the size of the knowledge base without causing loss of useful information. Results can be used by an expert system to generate supervisory automation and to support operators, for example, during an emergency situation. This methodology involves the generation of human-machine interface alarms. These can then be used for diagnosis of the type and cause of a fault event to give suggestions for network restoration and post-emergency repair. A power systems computer aided design/electromagnetic transients including dc simulator has been used to investigate the effect of faults and switching actions on the protection and control equipment associated with a typical distribution network. The fundamental ideas of rough set theory are discussed, followed by a rule assessment method that is outlined using an illustrative example.

Abstract: This is the first of two articles that report on the development, testing, and evaluation of the Individualized Instruction for Data Access System (IIDA) IIDA is an ex ample of a class of computer systems which serve as intermediaries, enabling their users to perform a complex task on another computer, and which are coming to be known as expert systems The system was designed to encourage end users of information retrieval systems to perform their own searches by (1) instructing them in how to search, using computer-assisted instruction, and (2) assisting with the performance of the search by providing diagnostic analyses of the users' performance as well as answering their questions about how to use system commands The system's design is described, as well as the various tests of its performance and the evaluation of test results The conclusion is drawn that end users can become successful searchers through such an assistant, for the kinds of searches tested

Abstract: This is the second part of a two-part article that examines 25 years of published research findings on end-user searching of online information retrieval (IR) systems. In Part 1 [Markey, 2007], it was learned that people enter a few short search statements into online IR systems. Their searches do not resemble the systematic approach of expert searchers who use the full range of IR-system functionality. Part 2 picks up the discussion of research findings about end-user searching in the context of current information retrieval models. These models demonstrate that information retrieval is a complex event, involving changes in cognition, feelings, andsor events during the information seeking process. The author challenges IR researchers to design new studies of end-user searching, collecting data not only on system-feature use, but on multiple search sessions and controlling for variables such as domain knowledge expertise and expert system knowledge. Because future IR systems designers are likely to improve the functionality of online IR systems in response to answers to the new research questions posed here, the author concludes with advice to these designers about retaining the simplicity of online IR system interfaces. © 2007 Wiley Periodicals, Inc.

Abstract: Artificial intelligence (AI) has been applied to the telecommunications industry for more than a decade. The purpose of this paper is to examine the application of AI in the telecommunications industry sector. Our research finds that AI's first main application in telecommunications is in the network management area. Expert systems and machine learning are the two AI techniques that have been widely used in telecommunications, while machine learning and distributed artificial intelligence are the two AI techniques which are most promising for the future. The research also finds that different AI techniques have their unique applications in the telecommunications industry.

Abstract: This paper addresses artificial intelligence (AI) applications in transportation Areas examined include AI methods, a brief history of AI, why AI is appropriate for transportation problems, and AI application areas This paper serves as an introduction to this circular which focuses on the following five paradigms: knowledge-based systems, neural networks, fuzzy sets, genetic algorithms, and agent-based models

Abstract: An expert system, also known as a knowledge based system, is a computer program that contains some of the subject-specific knowledge of one or more human experts. This class of program was first developed by researchers in artificial intelligence during the 1960s and 1970s and applied commercially throughout the 1980s. The most common form of expert systems is a program made up of a set of rules that analyse information usually supplied by the user of the system about a specific class of problems, as well as providing mathematical analysis of the problem(s), and, depending upon their design, recommend a course of user action in order to implement corrections. It is a system that utilises what appear to be reasoning capabilities to reach conclusions. This book presents important research on in this dynamic field.

Abstract: A business rules engine works with an optimization engine through various user interfaces to facilitate increased efficiency in retail space planning. Rules and models are built from templates that are stored in a repository. Business analysts and retail space planners both have access to the repository to develop models and rules. A project consists of a selection of rules and models from the repository along with selected data from various data sources. A scenario is created for the project by specifying constraints, parameters, and optimization objectives. The optimization engine processes the scenario and attempts to find an optimum solution. when a perfect solution (100%) cannot be found, the optimization engine evaluates the various criteria in the scenario and relaxes requirements until an acceptable solution is found. The output of the optimization engine can automatically be provided as graphical visualizations such as plan-o-grams, graphs, charts, and other forms.

Abstract: Expert System supported interactive product selection and recommendation. The invention assists an agent to interact with a customer and to provide selection and recommendation of available products and/or services that offer a workable solution for the customer. The invention allows for the use of agents of varying skill levels, including relatively low skill level, without suffering deleterious performance. From certain perspectives, an expert system employed using various aspects of the invention allows the agent to provide real time interaction with a customer and to provide a real time recommended solution to that customer. Many traditional approaches dealing in complex industries require that agent's have a high degree of skill and expertise. The invention allows even a novice agent to service a customer's needs without requiring a high skill level or up-front training that is often at the expense of the provider seeking to market its products and/or services.

Abstract: Efficient and effective response to the requirements of customers is a major performance indicator. Failure to satisfy customer requirements implies operational weaknesses in a company. These weaknesses will damage both the rights of customers and the reputation of the company. The traditional method of handling customer requirement for a machine tool manufacturer was dominated by manual process and subjective decision. In this study, we improved the operation process of handling customer requirement. The framework of a customer requirement information system (CRIS) for machine tool manufacturers was then analyzed, integrating rule-based fuzzy inference and expert systems, and a prototype system developed. The CRIS supports both customers and service personnel in providing a systematic way of fulfilling and analyzing customer requirements. The system was installed and operated in a machine tool manufacturer and the performance was found promising.

Abstract: In this paper, extended research upon the potentials of implementing distributed artificial intelligence technology to achieve high degrees of independency in distribution network protection and restoration processes is presented. The work that has already been done in the area of agent-based and/or knowledge-based applications and expert systems is briefly reviewed. The authors justify the need to distribute activities in contradiction to the centralized methodologies. A proper model of the real environment is introduced in order to define the designing parameters of a prototype agent entity, which is the part of a cooperative network-management system. The system's goal is to autonomously perform effective fault management upon medium-voltage power distribution lines. The structure of the agent entity is then described by means of the agent behaviors being implemented. The cooperative operations of the proposed system and its computer simulation are presented. Simulation results are being evaluated. Finally, general conclusive remarks are made.

Abstract: Technical analysis of stocks mainly focuses on the study of irregularities, which is a non-trivial task. Because one time scale alone cannot be applied to all analytical processes, the identification of typical patterns on a stock requires considerable knowledge and experience of the stock market. It is also important for predicting stock market trends and turns. The last two decades has seen attempts to solve such non-linear financial forecasting problems using AI technologies such as neural networks, fuzzy logic, genetic algorithms and expert systems but these, although promising, lack explanatory power or are dependent on domain experts. This paper presents an algorithm, PXtract to automate the recognition process of possible irregularities underlying the time series of stock data. It makes dynamic use of different time windows, and exploits the potential of wavelet multi-resolution analysis and radial basis function neural networks for the matching and identification of these irregularities. The study provides rooms for case establishment and interpretation, which are both important in investment decision making.

Abstract: Medical problems involve different types of variables and data, which have to be processed, analyzed and synthesized in order to reach a decision and/or conclude to a diagnosis. Usually, information and data set are both symbolic and numeric but most of the well-known data analysis methods deal with only one kind of data. Even when fuzzy approaches are considered, which are not depended on the scales of variables, usually only numeric data is considered. The medical decision support methods usually are accessed in only one type of available data. Thus, sophisticated methods have been proposed such as integrated hybrid learning approaches to process symbolic and numeric data for the decision support tasks. Fuzzy cognitive maps (FCM) is an efficient modelling method, which is based on human knowledge and experience and it can handle with uncertainty and it is constructed by extracted knowledge in the form of fuzzy rules. The FCM model can be enhanced if a fuzzy rule base (IF-THEN rules) is available. This rule base could be derived by a number of machine learning and knowledge extraction methods. Here it is introduced a hybrid attempt to handle situations with different types of available medical and /or clinical data and with difficulty to handle them for decision support tasks using soft computing techniques.

Abstract: Car failure detection is a complicated process and requires high level of expertise. Any attempt of developing an expert system dealing with car failure detection has to overcome various difficulties. This paper describes a proposed knowledge-based system for car failure detection. The paper explains the need for an expert system and the some issues on developing knowledge-based systems, the car failure detection process and the difficulties involved in developing the system. The system structure and its components and their functions are described. The system has about 150 rules for different types of failures and causes. It can detect over 100 types of failures. The system has been tested and gave promising results. Keywords—Expert system, car failure diagnosis, knowledge- based system, CLIPS.

Abstract: Research and practical literature that is relevant to the area of the application of software and intelligent automation applications on pollution minimisation and mitigation processes is analysed in this paper. Especially highlighted and studied are calculators, software, expert and decision support systems, fuzzy logic, and neural networks, which emerge as the most regularly used automation approaches for pollution analysis. The results of this paper's study provide a useful insight into creating one's own Decision Support System for Innovation (DSSI). DSSI differs from other systems in the use of new multiple criteria analysis methods that were developed by the authors. Different criteria characterising various kinds of pollutions are included in databases of the DSSI.

Abstract: An intelligent and adaptive web-based education system is presented. The system uses a hybrid AI approach, a combination of an expert systems approach and a genetic algorithm approach, to determine the difficulty levels of the provided exercises. The genetic algorithm is used to extract some kind of rules from the data acquired from the interactions of the students. Those rules are used to modify expert rules provided by the Tutor. In this way, feedback from the students is taken into account for determination of the difficulty levels of the questions/exercises. Experimental results show the validity of the method.

Abstract: We describe a new research effort for developing knowledge-based systems using a combination of methods from Software Engineering and Artificial Intelligence: software product-lines, experience factory, case-based reasoning, multi-agent-systems, and semantic web technology. We motivate our approach, shortly describe three different application scenarios, and provide our current ideas of how to implement our approach, which we call “collaborative multi-expert-systems” (CoMES).

Abstract: In this paper, a fuzzy logic (FL)-based expert system (ES) has been developed to predict the results of finite element (FE) analysis, while solving a rubber cylinder compression problem. As the performance of an ES depends on its knowledge base (KB), an attempt is made to develop the KB through three different approaches by using a genetic algorithm (GA). To collect the training data, two input parameters, namely element size and shape ratio are varied, while solving the said physical problem using an FEM package. The performance of the trained fuzzy logic-based expert system is tested for several test cases, differing significantly from the training cases. Results of these approaches are compared with those of FE analysis. Once developed, the ES is able to determine the values of parameters to be used in FE analysis, in order to obtain the results within a reasonable accuracy, at the cost of a much lower computation compared to that of the FEM package itself.