Abstract: Fuzzy inference systems (FIS) are widely used for process simulation or control. They can be designed either from expert knowledge or from data. For complex systems, FIS based on expert knowledge only may suffer from a loss of accuracy. This is the main incentive for using fuzzy rules inferred from data. Designing a FIS from data can be decomposed into two main phases: automatic rule generation and system optimization. Rule generation leads to a basic system with a given space partitioning and the corresponding set of rules. System optimization can be done at various levels. Variable selection can be an overall selection or it can be managed rule by rule. Rule base optimization aims to select the most useful rules and to optimize rule conclusions. Space partitioning can be improved by adding or removing fuzzy sets and by tuning membership function parameters. Structure optimization is of a major importance: selecting variables, reducing the rule base and optimizing the number of fuzzy sets. Over the years, many methods have become available for designing FIS from data. Their efficiency is usually characterized by a numerical performance index. However, for human-computer cooperation another criterion is needed: the rule interpretability. An implicit assumption states that fuzzy rules are by nature easy to be interpreted. This could be wrong when dealing with complex multivariable systems or when the generated partitioning is meaningless for experts. The paper analyzes the main methods for automatic rule generation and structure optimization. They are grouped into several families and compared according to the rule interpretability criterion. For this purpose, three conditions for a set of rules to be interpretable are defined.

Abstract: As the authors note in the Preface, “Courses on Quality are generally taught in two different ways: ‘technique-oriented’ courses that emphasize statistical tools, and ‘management-oriented’ courses that stress the managerial aspects of quality. This book is intended as a main text for a managerial-oriented course on quality.” For the statistical approach, the authors refer you to their other book, Statistical Quality Control: Strategies and Tools for Continual Improvement (Ledolter and Burrill 1999). The book does an excellent job of achieving its objective. Through a large number of examples and case studies, it covers a wide range of managerial topics in 624 pages, six sections of 24 chapters. The sections are “Introduction to Quality,” “Process View of Quality,” “Management Issues in Achieving Quality,” “Stabilizing Quality,” “Improving Quality,” and “Conclusion— Optimism for the Future.” There is also an appendix with eight case studies. I was surprised that there were no mentions of the ASQ CertiŽ ed Quality Manager certiŽ cation since the book covers most, if not all, of the topics needed for that certiŽ cation. It also covers not just manufacturing quality but “all aspects of quality: quality of goods, services and information.” There are more services and information examples than in most comparable books. There are many examples in the text and many exercises and additional readings at the end of each chapter. Since the book was copyrighted in 1999, there are even exercises speciŽ cally asking for an Internet browser search. Although this part of the book is very up to date, many of the examples are not. Chapter 3 uses examples ranging from 1982–1993. Although they may be good examples, I wonder if the improvements noted have been maintained. The examples are also a good mix of Japanese and American companies— although with the current problems in Japan I would have like have preferred to see even fewer Japanese examples. Section IV on stabilizing quality has four chapters devoted to various statistical methods. Since this is not a statistically focused book, the topics are covered with a minimum of mathematics. However, they are covered in the depth appropriate for a management course. Interpretations are given, and shortcomings are noted. The book even covers new statistical topics like Cpm and Six-Sigma. In fact the entire book gives a very balanced description of various topics. Chapter 6 gives deŽ nitions of quality by Deming, Juran, Feigenbaum, Crosby, Ishikawa, and ISO 8402. Each deŽ nition includes a discussion. For example, the Deming discussion notes “some troublesome inconsistencies in Deming’s comments,” and Juran’s deŽ nition includes a 1993 modiŽ cation. It is good to see that all points of view are covered and each is critiqued. Overall this a very good, balanced, and comprehensive management book. Even the statistical quality control section is very well done. I recommend this as a college textbook or as a manager’s reference. It could even be used to supplement study for the ASQ CertiŽ ed Quality Manager.

Abstract: Increasing interest in power quality has evolved over the past decade. This paper surveys the literature for current applications of advanced artificial intelligence techniques in power quality (PQ). Applications of some advanced mathematical tools in general, and wavelet transform in particular, in power quality are also reviewed. An extensive collection of literature covering applications of fuzzy logic, expert systems, neural networks, and genetic algorithms in power quality is included. Literature exposing the use of wavelets in power quality analysis as well as data compression is also cited.

Abstract: The main idea of this book is to present novel connectionist archite ctures of neuro-fuzzy systems, especially those based on the logical approach to fuzzy inference. In addition, hybrid learning methods are proposed to train the networks. The neuro-fuzzy architectures plus hybrid learning are considered as intelligent systems within the framework of computational and artificial intelligence. The book also provides an overview of fuzzy sets and systems, neural networks, learning algorithms (including genetic algorithms and clustering methods), as well as expert systems and perception-based systems which incorporate computing with words.

Abstract: The present invention relates to information systems theories and expert systems theories. The present invention provides a process, apparatus and method for decision making, based on emulation of the human decision-making process using expert-generated primary bias values, wherein a primary bias value associates a particular alternative possibility of a possibility set with a particular query, and reflects the expert s conception of the relative degree of predictive value of the query for the particular alternative relative to other alternatives in the possibility set. In particular embodiments, the present invention provides a process, apparatus and method for providing a medical diagnosis or medical self-assessment.

Abstract: Fuzzy logic provides a mathematical formalism for a unified treatment of vagueness and imprecision that are ever present in decision support and expert systems in many areas. The choice of aggregation operators is crucial to the behavior of the system that is intended to mimic human decision making. The paper discusses how aggregation operators can be selected and adjusted to fit empirical data: a series of test cases. Both parametric and nonparametric regression are considered and compared. A practical application of the proposed methods to electronic implementation of clinical guidelines is presented.

Abstract: A novel concept for power quality study is proposed. The concept integrates the power system modeling, classifying, and characterizing of power quality events, studying equipment sensitivity to the event disturbance and locating the point of event occurrence into one unified frame. Both Fourier and wavelet analyses are applied for extracting distinct features of various types of events as well as for characterizing the events. A new fuzzy expert system for classifying power quality events based on such features is presented with improved performance over previous neural network-based methods. A novel simulation method is outlined for evaluating the operating characteristics of the equipment during specific events. A software prototype implementing the concept has been developed in MATLAB. The voltage sag event is taken as an example for illustrating the analysis methods and software implementation issues. It is concluded that the proposed approach is feasible and promising for real world applications.

Abstract: This paper presents a fuzzy expert system that forecasts the wind speed at a wind energy conversion system (WECS) site and the electrical power that will be generated. The user can define the forecast horizon, which can range from some minutes up to several hours ahead. After training, the system can make reliable wind speed forecasts in less than a second. The system implements wind speed and direction measuring stations that are installed around and in the WECS site. The stations send measurements via wireless modems to a central computer running the fuzzy expert system, which exploits any spatial correlation existing among the measuring stations' wind speed time series. For the training of the fuzzy expert system two genetic algorithm implementations were used and compared.

Abstract: Based on insights from research in information systems, information science, business strategy and organization science, this paper develops the bases for advancing the paradigm of AI and expert systems technologies to account for two related issues: (a) dynamic radical discontinuous change impacting organizational performance; and (b) human sense-making processes that can complement the machine learning capabilities for designing and implementing more effective knowledge management systems.

Abstract: In this paper, a collision avoidance system is developed using the expert system and action space search. Fuzzy theory is used to reason the degree of collision risk, and the A * search method is used to make an avoidance action plan. The action space searched by the ship is formed in the expert system using the marine traffic rules. Simulation results demonstrate that the collision avoidance system with the expert system takes more reasonable actions than the system without it.

Abstract: It is well known that effective prediction of project cost related factors is an important aspect of software engineering. Unfortunately, despite extensive research over more than 30 years, this remains a significant problem for many practitioners. A major obstacle is the absence of reliable and systematic historic data, yet this is a sine qua non for almost all proposed methods: statistical, machine learning or calibration of existing models. The authors describe their sparse data method (SDM) based upon a pairwise comparison technique and T.L. Saaty's (1980) Analytic Hierarchy Process. Our minimum data requirement is a single known point. The technique is supported by a software tool known as DataSalvage. We show, for data from two companies, how our approach, based upon expert judgement, adds value to expert judgement by producing significantly more accurate and less biased results. A sensitivity analysis shows that our approach is robust to pairwise comparison errors. We then describe the results of a small usability trial with a practising project manager. From this empirical work we conclude that the technique is promising and may help overcome some of the present barriers to effective project prediction.

Abstract: The main results of a research project spanning over several years are presented in this paper. The aim of the research was the improvement of the effectiveness of personnel assessment within a large Italian corporation operating in the research sector. The first step of the research consisted of the analysis of the raters’ behavior, so as to elicit the judgement categories and prototypes they used in the judgement formulation, based on the rating method adopted in the corporation. The second step consisted of improving the rating method using fuzzy logic. The corporate management tested the new rating method and found it more efficient and reliable than the previous one.

Abstract: Instance-based reasoning systems and, in general, case-based reasoning systems are normally used in problems for which it is difficult to define rules. Although case-based reasoning methods have proved their ability to solve different types of problems, there is still a demand for methods that facilitate their automation during their creation and the retrieval and reuse stages of their reasoning circle. This paper presents one method based on kernels, which can be used to automate some of the reasoning steps of instance-based reasoning systems. Kernels were originally derived in the context of support vector machines, which identify the smallest number of data points necessary to solve a particular problem (e.g., regression or classification). Unsupervised kernel methods have been used successfully to identify the optimal instances to instantiate an instance base. The efficiency of the kernel model is shown on an oceanographic problem. © 2001 John Wiley & Sons, Inc.

Abstract: A system for marketing within a complex product space. The system allows a portal merchandiser to support suppliers, dealers, and customers within a speciality dealership network created to distribute complex consumer products. The system comprises a central merchandising portal website deployed on the Internet connected to a plurality of dealership point of sale and inventory systems and a plurality of supplier inventory systems. A customer accessing the site is presented with a virtual inventory database with product availability and pricing established by merchandising rules set by the merchandising portal, the suppliers, and the dealer closest in physical proximity to the customer as determined by the customer's zip code. The customer is given extensive customer support throughout a “Learn Build Buy Support” process by having access to expert systems and live experts through the merchandising portal.

Abstract: Interactive trouble-shooting and customer help-desk support, both activities that involve sequential diagnosis, represent the majority of applications of case-based reasoning (CBR). An analysis is presented of the user-interface requirements of intelligent systems for sequential diagnosis. We argue that mixed-initiative dialogue, explanation of reasoning, and sensitivity analysis are essential to meet the needs of experienced as well as novice users. Other issues to be addressed by system designers include relevance and consistency in dialogue, tolerance of missing data, and timely provision of feedback to users. Many of these issues have previously been addressed by the developers of expert systems and the lessons learned may have important implications for CBR. We present a prototype environment for interactive CBR in sequential diagnosis, called CBR Strategist, which is designed to meet the identified requirements.

Abstract: This paper presents a practical prototype knowledge-based system, called IKMOULD, for mould design in the injection moulding process. It attempts to tackle the problem in a practical and integrative way, unlike the stand-alone and mathematical programs which have been developed in the past to solve only a part of the problem. A total quantitative and structured approach is not feasible in dealing with the complex and multirelated design problems generally involved in mould design. In this system, the computational module, the knowledge-based module and the graphic module for generating mould features are integrated within an interactive CAD-based framework. The knowledge base of the system can be accessed by mould designers through interactive programs so that their own intelligence and experience can also be incorporated with the total mould design. The approach adopted both speeds up the design process and facilitates design standardisation which in turn increases the speed of mould manufacture.

Abstract: System and method for creating measurement applications. The system includes a measurement task specifier for generating a measurement task specification (MTS) for a measurement task in response to user input; an expert system for analyzing and validating the generated MTS, and generating a run-time specification (RTS) for the measurement task; a run-time builder for analyzing the RTS, configuring one or more measurement devices according to the RTS, and generating a run-time which is executable to perform the measurement task. The system includes a storage system for storing the generated MTS, the generated RTS, and configuration information for one or more measurement devices. The expert system includes one or more measurement experts which analyze all or part of the MTS and populate complete or partial RTSs. The partial RTSs are iteratively populated by other experts to form complete RTSs. Competing RTSs may be assessed and a final RTS selected based upon user preferences.

Abstract: An environmental decision support system is a specific version of an environmental information system that is designed to help decision makers, managers, and advisors locate relevant information and carry out optimal solutions to problems using special tools and knowledge. The RAISON (Regional Analysis by Intelligent Systems ON microcomputers) for Windows decision support system has been developed at the National Water Research Institute, Environment Canada, over the last 10 years. It integrates data, text, maps, satellite images, pictures, video and other knowledge input. A library of software functions and tools are available for selective extraction of spatial and temporal data that can be analysed using spatial algorithms, models, statistics, expert systems, neural networks, and other information technologies. The system is of a modular design which allows for flexibility in modification of the system to meet the demands of a wide range of applications. System design and practical experiences learned in the development of a decision support system for toxic chemicals in the Great Lakes of North America are discussed.

Abstract: The field of road pavement engineering has seen an explosion of artificial intelligence–based applications since the late 1980s. Such applications are found at key stages of the decision process involved in pavement management. In the analysis phase, they perform pavement diagnosis and deterioration modeling tasks. In the design phase, they enable rehabilitation needs to be assessed and contribute to the identification and selection of maintenance actions. Finally, in the choice phase, they are applied for priority programming of rehabilitation and maintenance. They either represent alternative approaches to existing systems or collaborate to make the overall system more efficient. The purpose of this article is to summarize the findings of up-to-date research articles concerning the application of artificial intelligence to pavement management and to illustrate the potential such tools can offer to pavement engineers. Artificial intelligence techniques include expert systems, artificial neural networks, fuzzy logic, genetic algorithms, and hybrid systems.

Abstract: An intelligent tutoring system is able to diagnose and adapt to a student's developing knowledge and skills, to provide precise feedback when mistakes are made or the student becomes stymied, and to present new topics when the student is ready to learn. ALEKS (Assessment and Learning in Knowledge Spaces) is an example of one such system currently used for the assessment and learning of factual knowledge in arithmetic and algebra. The terms "assessment" and "learning" will be clarified later in this paper. A web-based system, in contrast to compact-disk-based, can easily be used for the monitoring and management of entire courses and even institutions. It is very inexpensive - no college site-licensing is required - only an access code purchased by the student for a nominal fee. As an intelligent tutor, this system "teaches" the student, and provides a new and engaging way for students to learn or review the fundamentals of arithmetic and algebra. The program discussed in this article was developed with support from the National Science Foundation by ALEKS Corporation, a Delaware company formed in 1996. 1. ALEKS BACKGROUND Intelligent tutoring systems are part of a new breed of instructional computer programs made possible through recent developments in computer memory and computational speed capabilities, new computer programming languages, and in research in human cognition and learning. Through expert system technology and artificial intelligence, they are able to carry on intelligent "dialogues" with students and flexibly adjust to the knowledge and skill level of individuals, as well as provide a variety of methods of representing and accessing information. They are able to make inferences about a student's current state of knowledge and based on that knowledge, provide a choice of topics that the student is ready to learn. Some systems are oriented to discovery learning, others are more didactic, or "teaching", oriented. Compared to earlier computer-assisted instructional tools, they are far more adaptive to individual students, and better matched with current goals in mathematics education. Intelligent tutoring systems have four basic components: expert knowledge, learner modeling, tutorial planning, and communication [1]. These divisions into components are abstract and do not necessarily reflect actual separations in the physical structure of a system. The expert knowledge component consists of the facts and ideas of the particular subject to be learned, what a specialist or "expert" in the subject would know. Often this is known in various forms (in math, say, in numerical, symbolic, or graphic form). The more the expert system component is able to represent these various forms, the better it represents real human capabilities. The learner modeling component consists of the system's ability to diagnose, in an ongoing, adaptive way, the developing knowledge and skill of the student. The tutorial planning component guides the student by presenting appropriate learning activities, providing feedback in cases of mistakes or a standstill in progress, encouraging successes, determining progress, and individually-tailoring review topics. The tutorial planning component works closely with the results of the learner modeling component. Lastly, the communication component interacts with the student through words or graphical interfaces, providing the overt "intelligence" and user-friendliness evident when using an intelligent tutoring system [1]. This part may also often include the ability of the student and the instructor themselves to communicate with each other, through built-in e-mail-like capabilities. This last feature makes online intelligent tutoring systems useful for web-based courses and distance-learning. National-Louis University students represent a very diverse community of cultural backgrounds, academic abilities, ages and especially in their limited historical access to higher education. …

Abstract: The design of systems for intelligent control of urban traffic is important in providing a safe environment for pedestrians and motorists. Artificial neural networks (ANNs) (learning systems) and expert systems (knowledge-based systems) have been extensively explored as approaches for decision-making. While the ANNs compute decisions by learning from successfully solved examples, the expert systems rely on a knowledge base developed by human reasoning for decision making. It is possible to integrate the learning abilities of an ANN and the knowledge-based decision-making ability of the expert system. This paper presents a real-time intelligent decision-making system, IDUTC, for urban traffic control applications. The system integrates a backpropagation-based ANN that can learn and adapt to the dynamically changing environment and a fuzzy expert system for decision-making. The performance of the proposed intelligent decision-making system is evaluated by mapping the adaptable traffic light control problem. The application is implemented using the ANN approach, the FES approach, and the proposed integrated system approach. The results of extensive simulations using the three approaches indicate that the integrated system provides better performance and leads to a more efficient implementation than the other two approaches.

Abstract: Groundwater and soil contamination resulted from LNAPLs (light nonaqueous phase liquids) spills and leakage in petroleum industry is currently one of the major environmental concerns in North America. Numerous site remediation technologies have been developed and implemented in the last two decades. They are classified as ex-situ and in-situ remediation techniques. One of the problems associated with ex-situ remediation is the cost of operation. In recent years, in-situ techniques have acquired popularity. However, the selection of the optimal techniques is difficult and insufficient expertise in the process may result in large inflation of expenses. This study presents an expert system (ES) for the management of petroleum contaminated sites in which a variety of artificial intelligence (AI) techniques were used to construct a support tool for site remediation decision-making. This paper presents the knowledge engineering processes of knowledge acquisition, conceptual design, and system implementation. The results from some case studies indicate that the expert system can generate cost-effective remediation alternatives to assist decision-makers.

Abstract: This invention is a computerized expert system for providing interior design by allowing a homeowner to enter interior design requirements and selecting interior design treatments according to the homeowner's design requirements so that a grouping of compatible interior design treatments is provided for the homeowner to be used in decorating the homeowner's home.