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The dynamic sampling instead of static sampling can more effectively utilize the inspection capacity for quicker excursion detection and increase the throughput of inspection machines without affecting the quality of inspection, so that achieve cycle time reduction. Accordingly, many researchers and semiconductor fabs start investigating the impacts of using dynamic sampling and there is currently a growing need for the dynamic sampling strategies in today''s highly competitive semiconductor industry. Meanwhile, the use of artificial intelligence (AI) for knowledge discovery has become more common in industrial and manufacturing process control systems and recent advances in technology, particularly in networking, and information processing, have made the implementation of dynamic process sampling feasible. In this paper the optimal dynamic sampling method and the associated decision process based on AI technique are proposed and the effectiveness of them is validated through actual data sets collected from a semiconductor fabrication line. Finally, we present an AI-based dynamic sampling planning system incorporated the proposed methodology, which possesses four sub-components: wafer bin map (WBM) data mart, optimal sampling method generator (OSMG), sampling knowledge, and sampling adaptation monitor. Our research results provide an effective solution to implement a successful dynamic process sampling. [Copyright &y& Elsevier]

The development of web-aware data mining systems has received a great deal of attention in recent years. It plays a key enabling role for competitive businesses in the E-commerce era. One of the challenges in developing web-aware data mining systems is to integrate and coordinate existing data mining applications in a seamless manner so that cost-effective systems can be developed without the need of costly proprietary products. In this paper we present an approach for developing an interoperable web-aware data mining system to achieve this purpose. This approach applies Remote Method Invocation and high level code wrapper of Java distributed object computing to address the issues of interoperability in heterogeneous environments, which includes programming language, platform, and visual object model. The effectiveness of the proposed system is demonstrated through the integration and enhancement of the two well-known standalone data mining tools, SOM_PAK and Nenet, and runs with the iris data and air pollution data. [Copyright &y& Elsevier]

Enterprise websites nowadays have become one of the most important conversation channels between the enterprise and its existing/potential customers (visitors). We envision that novel ways of managing visitor relationships will bring about loyalty from the existing customers and stimulate the interests in the enterprise from the potential customers. This paper is the first attempt to apply the traditional concept of CRM for managing visitor relationships, that is, visitor relationship management (VRM). In other words, visitors are differentiated by means of their different values and served with different relationship strengthening practices in accordance with the varying understanding of the visitors. This paper presents a methodology to understand visitors, to compute the values of those visitors, and to differentiate the services required for each of those visitors. In other words, to generate differentiated experience space for the visitors in the environment of an enterprise website. This methodology is a combination of dynamic web page contents, dynamic web page navigation, and dynamic web page portal, which are based on the techniques of information retrieval, (IR) clustering, and a novel valuing mechanism. This would enable an effective marketing campaign on behalf of the enterprise by separating the loyal visitors from the disloyal ones. [Copyright &y& Elsevier]

Case-based reasoning (CBR) shows significant promise for improving the effectiveness of complex and unstructured decision making. CBR is both a paradigm for computer-based problem-solvers and a model of human cognition. However the design of appropriate case retrieval mechanisms is still challenging. This paper presents a genetic algorithm (GA)-based approach to enhance the case-matching process. A prototype GA–CBR system used to predict customer purchasing behavior is developed and tested with real cases provided by one worldwide insurance direct marketing company, Taiwan branch. The results demonstrate better prediction accuracy over the results from the regression-based CBR system. Also an optimization mechanism is integrated into the classification system to reveal those customers most likely and most unlikely customers to purchase insurance. [Copyright &y& Elsevier]

Design of liquid retaining structures involves many decisions to be made by the designer based on rules of thumb, heuristics, judgment, code of practice and previous experience. Various design parameters to be chosen include configuration, material, loading, etc. A novice engineer may face many difficulties in the design process. Recent developments in artificial intelligence and emerging field of knowledge-based system (KBS) have made widespread applications in different fields. However, no attempt has been made to apply this intelligent system to the design of liquid retaining structures. The objective of this study is, thus, to develop a KBS that has the ability to assist engineers in the preliminary design of liquid retaining structures. Moreover, it can provide expert advice to the user in selection of design criteria, design parameters and optimum configuration based on minimum cost. The development of a prototype KBS for the design of liquid retaining structures (LIQUID), using blackboard architecture with hybrid knowledge representation techniques including production rule system and object-oriented approach, is presented in this paper. An expert system shell, Visual Rule Studio, is employed to facilitate the development of this prototype system. [Copyright &y& Elsevier]

This paper is concerned with proposing the fuzzy cognitive map (FCM)-driven inference amplification mechanism in the field of web-mining. As the recent advent of the Internet, most of the modern firms are now geared towards using the web technology in their daily as well as strategic activities. The web-mining technology provides them with unprecedented ability to analyze web-log data, which are seemingly full of useful information, but often lack of important and meaningful information. This indicates the need to develop an advanced inference mechanism extracting richer implication from the web-mining results. In this sense, we propose a new web-mining inference amplification (WEMIA) mechanism using the inference logic of FCM. The association rule mining is what we adopt as the web-mining technique to prove the validity of the proposed WEMIA. The main recipe of the proposed WEMIA is the three-phased inference amplification. The first phase is to apply the association rule mining, and the second phase is to transform the association rules into FCM-driven causal knowledge bases. The third phase is dedicated to amplifying the inference by developing the causal knowledge-based inference equivalence property, which was derived from analyzing the inference mechanism of FCMs. With an illustrative web-log database, we suggest results proving the robustness of our proposed WEMIA mechanism. [Copyright &y& Elsevier]

A global corporation''s supply chain usually consists of enterprises and manufacturers that are graphically dispersed around the world, whereby each company is involved in a wide variety of supply chain activities such as order fulfillment, international procurement, acquisition of information technology, manufacturing, and customer service. However, selecting suppliers based on accumulated experience is not both effective and scientific due to subjective judgment and lack of systematic analysis. Therefore, continuously tracking and benchmarking performance of suppliers and forming an appropriate supplier selection mechanism is one of the crucial activities in supply chain management. This paper presents an intelligent supplier management tool (ISMT) using the case-based reasoning (CBR) and neural network (NN) techniques to select and benchmark suppliers. The development of ISMT and how the CBR and NN techniques are used in benchmarking suppliers during the process of new product development in Honeywell Consumer Products (Hong Kong) Limited are presented. [Copyright &y& Elsevier]

With the fast growing and globally accepted e-learning technology, Computer-Assisted Learning (CAL) system, which can provide the individualized materials, becomes a matter of great importance. In this paper, we propose a CAL Expert System (CAL-ES), in which teachers can provide their teaching strategy as guidance to students'' learning. Because the knowledge hierarchy and the knowledge itself are both important in CAL domain, and the teaching strategy can be considered as rule-format, Two-phase Knowledge Acquisition is proposed to acquire the knowledge hierarchy and the rule-based knowledge by the cooperation of teachers and knowledge engineers. The prototype of CAL-ES is constructed of the mathematical teaching materials. The CAL-ES is useful for student learning and is easy for teachers maintaining. [Copyright &y& Elsevier]

The high maneuverability of modern jet fighters often subjects the pilots to high Gz acceleration. One of the adverse effects of Gz acceleration is the G-induced loss of consciousness. The current protection scheme includes a pressure mask that supplies the pilots with nominal positive pressure breathing and a nominal pressurized G-suit. This paper presents a novel approach based on a combination of expert knowledge, pilots'' anthropometric and physiologic data to generate control schedules of the G-suit and mask pressures of jet fighter pilots. [Copyright &y& Elsevier]

The prosperity of electronic commerce has changed the traditional trading behaviors and more and more people are willing to conduct Internet shopping. However, the exponentially increasing information provided by the Internet enterprises causes the problem of overloaded information, and this inevitably reduces the customer''s satisfaction and loyalty. One way to overcome such a problem is to build personalized recommender systems to retrieve product information that really interests the customers. For products that people may purchase relatively often, such as books and CDs, recommender systems can be built to reason about a customer''s personal preferences from his purchasing history and then provide the most appropriate information services to meet his needs. On the other hand, for those commodities a general customer does not buy frequently, for example computers and home theater systems, more appropriate are the kinds of recommender systems able to retrieve optimal products based on the customer''s current preferences obtained from the iterative system–customer interactions. This paper presents the above two kinds of recommender systems we have developed for supporting Internet commerce. Experimental results show the promise of our systems. [Copyright &y& Elsevier]

Machine learning can extract desired knowledge from existing training examples and ease the development bottleneck in building expert systems. Most learning approaches derive rules from complete data sets. If some attribute values are unknown in a data set, it is called incomplete. Learning from incomplete data sets is usually more difficult than learning from complete data sets. In the past, the rough-set theory was widely used in dealing with data classification problems. In this paper, we deal with the problem of producing a set of certain and possible rules from incomplete data sets based on rough sets. A new learning algorithm is proposed, which can simultaneously derive rules from incomplete data sets and estimate the missing values in the learning process. Unknown values are first assumed to be any possible values and are gradually refined according to the incomplete lower and upper approximations derived from the given training examples. The examples and the approximations then interact on each other to derive certain and possible rules and to estimate appropriate unknown values. The rules derived can then serve as knowledge concerning the incomplete data set. [Copyright &y& Elsevier]

The arrangement of courses at universities is an optimal problem to be discussed under multiple constraints. It can be divided into two parts: teacher assignments and class scheduling. This paper focused primarily on teacher assignments. Consideration was given to teacher''s professional knowledge, teacher preferences, fairness of teaching overtime, school resources, and the uniqueness of the school''s management. Traditional linear programming methods do not obtain satisfactory results with this complex problem.In this paper, genetic algorithm methods were used to deal with the issue of multiple constraints. As a global optimal searching method, the results of this study indicated that genetic algorithms can save significant time spent on teacher assignments and are more acceptable by the teachers. [Copyright &y& Elsevier]

Many classification tasks can be viewed as ordinal. Use of numeric information usually provides possibilities for more powerful analysis than ordinal data. On the other hand, ordinal data allows more powerful analysis when compared to nominal data. It is therefore important not to overlook knowledge about ordinal dependencies in data sets used in data mining. This paper investigates data mining support available from ordinal data. The effect of considering ordinal dependencies in the data set on the overall results of constructing decision trees and induction rules is illustrated. The degree of improved prediction of ordinal over nominal data is demonstrated. When data was very representative and consistent, use of ordinal information reduced the number of final rules with a lower error rate. Data treatment alternatives are presented to deal with data sets having greater imperfections. [Copyright &y& Elsevier]

This paper proposes a knowledge grid model for sharing and managing globally distributed knowledge resources. The model organizes knowledge in a three-dimensional knowledge space, and provides a knowledge grid operation language, KGOL. Internet users can use the KGOL to create their knowledge grids, to put knowledge to them, to edit knowledge, to partially or wholly open their grids to all or some particular grids, and to get the required knowledge from the open knowledge of all the knowledge grids. The model enables people to conveniently share knowledge with each other when they work on the Internet. A software platform based on the proposed model has been implemented and used for knowledge sharing in research teams. [Copyright &y& Elsevier]

Due to the complexity of the numerical simulation of flow and/or water quality, there is an increasing demand for integration of recent knowledge management, artificial intelligence technology with the conventional hydraulic algorithmic models in order to assist novice application users in selection and manipulation of various mathematical tools. In this paper, a prototype knowledge management system on flow and water quality is addressed to simulate human expertise during the problem solving by incorporating artificial intelligence and coupling various descriptive knowledge, procedural knowledge and reasoning knowledge involved in the coastal hydraulic and transport processes. The system is developed through employing Visual Rule Studio, a hybrid expert system shell, as an ActiveX Designer under Microsoft Visual Basic 6.0 environment since it combines the advantages of both production rules and object-oriented programming technology. The architecture, the development and the implementation of the prototype system are delineated in details. Based on the succinct features and conditions of a variety of flow and water quality models, three kinds of class definitions, Section and Problem as well as Question are defined and the corresponding knowledge rule sets are also established. Both forward chaining and backward chaining are used collectively during the inference process. A typical example is also presented to demonstrate the application of the prototype knowledge management system. [Copyright &y& Elsevier]

Management is aware that the value of firms is the ultimate measure of company performance. However, management has been using common accounting measures as an operating guide because the linkage between operational planning and value is vague and complex and, therefore, difficult to apply. Managers need to have clear targets and performance measures to track progress. This paper examines if a set of common accounting performance measures are linked tightly to the overall value of the firm. An abductive learning network (ALN) approach, an artificial intelligence technique, is used in this research because an ALN approach is non-parametric and can capture a subtle dynamic relationship between input and output variables, which is not obvious in linear statistical analysis. Empirical results show that an ALN model is very effective in synthesizing the value of the firm using six common accounting variables. The results also provide four strategic variables that can be used for devising strategic and operating plans to maximize the value of firms. [Copyright &y& Elsevier]

In this paper, we investigate ways to apply news information on the Internet to the prediction of interest rates. We developed the Knowledge-Based News Miner (KBNMiner), which is designed to represent the knowledge of interest rate experts with cognitive maps (CMs), to search and retrieve news information on the Internet according to prior knowledge, and to apply the information, which is retrieved from news information, to a neural network model for the prediction of interest rates.This paper focuses on improving the performance of data mining by using prior knowledge. Real-world interest rate prediction data is used to illustrate the performance of the KBNMiner. Our integrated approach, which utilizes CMs and neural networks, has been shown to be effective in experiments. While the 10-fold cross validation is used to test our research model, the experimental results of the paired t-test have been found to be statistically significant. [Copyright &y& Elsevier]

The purpose of this paper is to study how a multi-agent-based coordination mechanism can resolve conflicts among functional units in an enterprise. In the era of information technology, characterized by web technology, intelligent agents demonstrate the best interests of a functional unit when engaged in the highly delicate coordination process of an enterprise. Thus, behavioral problems that occur while coordinating conflicts among functional units can be avoided effectively. Since there are a number of functional units in an enterprise, the multi-agents are utilized to support the agent-based coordination of conflicts among several functional units. This paper proposes a new type of multi-agent-based coordination engine termed MACE in which the information exchange between multi-agents is properly controlled via a complicated algorithm. To illustrate the performance of MACE, we devised a cooperative organizational decision support system (CODSS) and incorporated MACE into CODSS, yielding MACE_CODSS. The combined case of production and marketing was introduced and tackled by MACE_CODSS, obtaining the proper coordination results. To verify the improved performance of MACE, we implemented three types of coordination DSS: Joint_CODSS, Separate_CODSS, and None_CODSS. With 12 simulation periods and 100 simulation runs, we obtained comparative coordination results for each coordination type. Statistical tests revealed that the proposed MACE_CODSS produced the most desirable coordination results. [Copyright &y& Elsevier]

To realize effective knowledge sharing in teamwork, this paper proposes a knowledge flow model for peer-to-peer knowledge sharing and management in cooperative teams. The model consists of the concepts, rules and methods about the knowledge flow, the knowledge flow process model, and the knowledge flow engine. A reference model for coordinating the knowledge flow process with the workflow process is suggested to provide an integrated approach to model teamwork process. We also discuss the peer-to-peer knowledge-sharing paradigm in large-scale teams and propose the approach for constructing a knowledge flow network from the corresponding workflow. The proposed model provides a new way to model and manage teamwork processes. [Copyright &y& Elsevier]

Classification is one of the important tasks in developing expert systems. Most of the previous approaches for classification problem are based on classification rules generated by decision trees. In this paper, we propose a new learning approach based on genetic programming to generate discriminant functions for classifying data. An adaptable incremental learning strategy and a distance-based fitness function are developed to improve the efficiency of genetic programming-based learning process. We first transform attributes of objects into fuzzy attributes and then a set of discriminant functions is generated based on the proposed learning procedure. The set of derived functions with fuzzy attributes gives high accuracy of classification and presents a linear form. Hence, the functions can be transformed into inference rules easily and we can use the rules to provide the building of rule base in an expert system. [Copyright &y& Elsevier]

This paper surveys a number of well-known expert systems in the domain of finance. The idea is to illustrate the span of typical expert systems in finance and to provide an insight into the approaches and techniques they employ. The systems considered in the paper are all working systems, and come from different fields of finance. Specifically, the systems considered are FINEVA (from the field of financial analysis), PORT-MAN (banking management), INVEX (investment advisory) and FAME (financial marketing). The paper also briefly presents DEVEX, an expert system for currency exchange advising in international business transactions. Financial institutions in less-developed and undeveloped countries that deal with currency exchange often face some specific problems that do not exist in well-developed regions of the world. DEVEX helps bank employees to cope with such problems. Since business transactions between developed countries and Third World ones represent an important part of the world''s financial affairs, the problems covered in DEVEX go beyond local financial institutions. [Copyright &y& Elsevier]

A workflow model is useful for business process analysis. A well-built workflow can help a company streamline its internal processes by reducing overhead. The results of workflow modeling need to be managed as information assets in a systematic fashion. Reusing these results is likely to enhance the quality of the modeling. Therefore, this paper proposes a document-based workflow modeling mechanism, which employs a case-based reasoning (CBR) technique for the effective reuse of design outputs. A repository is proposed to support this CBR process. A real-life case is illustrated to demonstrate the usefulness of our approach. [Copyright &y& Elsevier]

Case-based reasoning (CBR) is a problem-solving methodology commonly seen in artificial intelligence. It can correctly take advantage of the situations and methods in former cases to find out suitable solutions for new problems. CBR must accurately retrieve similar prior cases for getting a good performance. In the past, many researchers proposed useful technologies to handle this problem. However, the performance of retrieving similar cases may be greatly influenced by the number of cases. In this paper, the performance issue of large-scale CBR is discussed and a parallelized indexing architecture is then proposed for efficiently retrieving similar cases in large-scale CBR. Several algorithms for implementing the proposed architecture are also described. Some experiments are made and the results show the efficiency of the proposed method. [Copyright &y& Elsevier]

The application of knowledge-based control systems in the area of biotechnological processes has become increasingly popular over the past decade. This paper will outline the structure of the advanced knowledge-based part of the BIOGENES© control system for the control of bioprocesses such as the fed-batch Saccharomyces cerevisiae cultivation. First, a brief overview of all the tasks implemented in the knowledge-based level including process data classification, qualitative process state identification and supervisory process control is given. The procedures performing the on-line identification of metabolic states and supervisory process control (setpoint calculation and control strategy selection) are described in more detail. Finally, the performance of the system is discussed using results obtained from a number of experimental cultivation runs in a laboratory unit. [Copyright &y& Elsevier]

Knowledge has become to be considered as valuable strategic assets that can provide proprietary competitive advantages. It is more important for companies to distinguish themselves through knowledge management strategies. Without a constant creation of knowledge, a business is condemned to poor performance. However, it is still unclear how these strategies affect knowledge creation. Knowledge management strategies can be categorized as being either human or system oriented. This paper proposes a model to illustrate the link between the strategies and its creating process. The model is derived on the basis of samples from 58 Korean firms. The model depicts how companies should align the strategies with four knowledge creation modes such as socialization, externalization, combination, and internalization. It is found that human strategy is more likely to be effective for socialization while system strategy is more likely to be effective for combination. Furthermore, the survey result suggests that managers should adjust knowledge management strategies in view of the characteristics of their departments. [Copyright &y& Elsevier]

This paper describes the characteristics of an expert system that will make an intelligent interpretation of the quantitative data obtained following the usual validation process for intelligent systems. This expert system converts numerical data into information that will indicate whether the performance of the validated intelligent system is comparable to the performance of human experts from the domain taken as reference. The process is carried out in two parts: an algorithmic part which identifies the basic characteristics of the quantitative data; a heuristic part which analyses these characteristics so as to be able to draw conclusions as to the results of the system. [Copyright &y& Elsevier]

The paper describes I-Promise, an expert system for configuration design of complex protective materials. Configuration problem of I-Promise is a part of conceptual design of complex protective materials; hence, the context of conceptual design and configuration problems is briefly described before the detailed description of I-Promise, its problem domain, its design, and its application. Due to the complexity and sequential nature of its task, I-Promise is designed as a series of three specialized expert systems. I-Promise is a real-world system, built on top of the information system of an enterprise that produces and delivers complex protective materials. [Copyright &y& Elsevier]

In this paper, an expert diagnosis system is presented for interpretation of the Doppler signals of the heart valve diseases based on the pattern recognition. This paper especially deals with the feature extraction from measured Doppler signal waveforms at the heart valve using the Doppler Ultrasound. Wavelet transforms and short time Fourier transform methods are used to feature extract from the Doppler signals on the time–frequency domain. Wavelet entropy method is applied to these features. The back-propagation neural network is used to classify the extracted features. The performance of the developed system has been evaluated in 215 samples. The test results showed that this system was effective to detect Doppler heart sounds. The correct classification rate was about 94% for normal subjects and 95.9% for abnormal subjects. [Copyright &y& Elsevier]

Case-based reasoning (CBR) is a methodology for problem solving and decision-making in complex and changing business environments. Many CBR algorithms are derivatives of the k-nearest neighbor (k-NN) method, which has a similarity function to generate classification from stored cases. Several studies have shown that k-NN performance is highly sensitive to the definition of its similarity function. Many k-NN methods have been proposed to reduce this sensitivity by using various distance functions with feature weights.This paper proposes an analogical reasoning structure for feature weighting using a new framework called the analytic hierarchy process (AHP)-weighted k-NN algorithm. The paper also introduces AHP methodology for assigning relative importance in case indexing and retrieving. The AHP model is a methodology effective in obtaining domain knowledge from numerous experts and representing knowledge-guided indexing. The proposed AHP weighted k-NN algorithm has been shown to achieve classification accuracy higher than the pure k-NN algorithm. This approach is applied to bankruptcy prediction involves the examination of several criteria, both quantitative (financial ratios) and qualitative (non-financial variables). [Copyright &y& Elsevier]

Decision models and knowledge rules are widely used to assist in decision-making. They are common decision support devices that should be effectively managed in decision support systems. Research and development (R&D) project selection is a complicated and knowledge intensive decision-making process where decision models and knowledge rules play an important role. This paper presents a hybrid knowledge and model system, which integrates mathematical models with knowledge rules, for R&D project selection. The system is designed to support the whole decision process of R&D project selection and has been used in the selection of R&D projects in the National Natural Science Foundation of China (NSFC). [Copyright &y& Elsevier]

Knowledge acquisition is the chief constraint in constructing a rule-based knowledge system (RBKS). Knowing how to use the automatic method to elicit required knowledge is a key issue in implementing a RBKS in the experience-oriented construction domain. Rule induction (RI) is one of the fastest means of extracting rule-based knowledge from previous cases. This paper presents a model (AMASS) that integrates a RI approach, a RBKS, and a database management system to support automated knowledge mining and usage. A typical experience-oriented problem in construction domain, selecting a suitable retaining wall system in construction planning stage, is employed to demonstrate how to implement the AMASS model. [Copyright &y& Elsevier]

The integration of customer relationship management (CRM) and supplier relationship management (SRM) to facilitate supply chain management in the areas of supplier selection using a help desk approach has become a promising solution for manufacturers to identify appropriate suppliers and trading partners to form a supply network on which they depend for products, services, and distribution. In this paper, an intelligent customer–supplier relationship management system (ISRMS) using the case based reasoning (CBR) technique to select potential suppliers is discussed. By using ISRMS in Honeywell Consumer Product (Hong Kong) Limited, it is found that the outsource cycle time from the searching of potential suppliers to the allocation of order is greatly reduced. [Copyright &y& Elsevier]

Fish disease diagnosis is a complicated process and requires high level of expertise. Any attempt of developing a web-based system dealing with disease diagnosis has to overcome various difficulties. This paper describes a Chinese National Funded Research Project (863 project) aiming to develop a web-based intelligent diagnosis system for fish diseases. The paper explains the need for a web-based expert system, the fish diagnosis process and the difficulties involved in developing the system. The system structure and its components, such as database, knowledge base and image base and their functions are described. The system has over 300 rules and 400 images and graphics for different types of diseases and symptoms. It can diagnose 126 types of diseases amongst nine species of primary freshwater fishes. The system has been tested and is now in pilot use by fish farmers in the North China region. Some issues on developing web-based expert systems from the experience gained from the research are discussed. [Copyright &y& Elsevier]

A personalized product recommendation is an enabling mechanism to overcome information overload occurred when shopping in an Internet marketplace. Collaborative filtering has been known to be one of the most successful recommendation methods, but its application to e-commerce has exposed well-known limitations such as sparsity and scalability, which would lead to poor recommendations. This paper suggests a personalized recommendation methodology by which we are able to get further effectiveness and quality of recommendations when applied to an Internet shopping mall. The suggested methodology is based on a variety of data mining techniques such as web usage mining, decision tree induction, association rule mining and the product taxonomy. For the evaluation of the methodology, we implement a recommender system using intelligent agent and data warehousing technologies. [Copyright &y& Elsevier]

Agent based computing is generally intended for modeling and implementation of distributed complex problems. Despite the existence of many applications, the problem of rational engineering of multi-agent systems remains complex and difficult. The purpose of this paper can be summarized within two claims. First, we aim at providing an approach that gives some guidelines for specifying and designing multi-agent systems. Secondly, we focus on the formalisms as a language for describing the models produced in each development process phases. These seem to be straightforward, while the development of multi-agent systems is still done, in most cases, without using methods and formal modeling such as those generally used in object oriented software. We illustrate this approach by specifying an example based upon a specific agent architecture. [Copyright &y& Elsevier]

Image mining presents special characteristics due to the richness of the data that an image can show. Effective evaluation of the results of image mining by content requires that the user point of view (of likeness) is used on the performance parameters. Comparison among different mining by similarity systems is particularly challenging owing to the great variety of methods implemented to represent resemblance and the dependence that the results present of the used image set. Other obstacle is the lag of parameters for comparing experimental performance. In this paper we propose an evaluation framework for comparing the influence of the distance function on image mining by colour. Experiments with colour similarity mining by quantisation on colour space and measures of likeness between a sample and the image results have been carried out to illustrate the proposed scheme. Important aspects of this type of mining are also described. [Copyright &y& Elsevier]

Business rules give rise to an important set of requirements on any system being developed or procured for an enterprise. While most of the work done in this area focuses on identifying and documenting business rules, we have proposed a methodology that addresses several aspects of the business rules lifecycle: acquisition, deployment and evolution. The methodology assumes that business rules are expressed in terms of business concepts and corporate knowledge that are captured in a high level architecture. The architecture proposed consists of three interconnected components: the enterprise model, the business rules model and the decision support model. This approach permits a greater variety of rules to be specified while providing an opportunity to automate the production of deployable business rules. The ability to deal with the inconsistent and ambiguous rules is crucial in capturing the conflicting requirements placed on the operation of any large scale enterprise. This paper presents a flexible deployment of business rules, which not only supports decision making in the face of conflicting requirements, but also the evolution of those requirements in the face of changing regulatory environments, competitive markets and corporate goals. [Copyright &y& Elsevier]

Rule-based systems or production systems still have great importance in the construction of knowledge systems. In these systems, the domain expertise to solve a problem is encoded in the form of ‘if–then’ rules, enabling a modular description of the knowledge, thus facilitating its maintenance and updating. Although they have been extensively described in the Artificial Intelligence literature, their design process is at times repeated because of the lack of common software architecture and the restrictions offered by some off-the-shelf libraries and systems. This paper proposes a reusable architecture for rule-based systems described through design patterns. The aim of these patterns is to constitute a design catalog that can be used by designers to understand and create new rule-based systems, thus promoting reuse in these systems. Additionally, the use of the described patterns in the design of an intelligent tutoring system architecture is exemplified. [Copyright &y& Elsevier]

In this paper an integrated geographic expert database system is presented which takes advantage of the relational database methodology combined with a geographic information system (GIS) and an expert system (ES). The GIS illustrates the data that are stored in the database after collaboration with the embedded ES. The system is fully implemented and applied to an interesting problem in apiculture. [Copyright &y& Elsevier]

This paper describes a method for generating explanations in decision analytic contexts. Unlike other approaches, we use natural language generation techniques. The novelty of the work stems from the development of a library of text plans that structure the explanation messages conveyed. This makes our approach generic and easily adjusted to different contexts. In order to demonstrate the applicability of the method, we have developed a natural language generator that explains and justifies the advice of a decision support system for nuclear emergencies. The generator outputs two reports: a comparative report that explains the rationale behind the ranking of the alternatives and a sensitivity analysis report that gives an overall assessment of the decision model and describes the effect of varying a decision parameter. [Copyright &y& Elsevier]

Activity-based costing (ABC) has received extensive attention since it achieves improved accuracy in estimating costs, by using multiple cost drivers to trace the cost of activities to the products associated with the resources consumed by those activities. However, it has some problems. The first problem is that ABC does not have general criteria to select relevant cost drivers. Second, ABC assumes linearity between the uses of activities and the assigned quantities of indirect cost. When cost behavior shows a nonlinear pattern, conventional ABC may distort product costs. This paper proposes hybrid artificial intelligence techniques to resolve these two problems. Genetic algorithms are used to identify optimal or near-optimal cost drivers. In addition, artificial neural networks are employed to allocate indirect costs with nonlinear behavior to the products. Empirical results show that the proposed model outperforms the conventional model. [Copyright &y& Elsevier]

In the past several decades, many expert system applications have been developed and reported in the literature. Case studies of these applications typically include a detailed description of the problem domain, knowledge acquisition techniques used and also some indication of the relative success of the application. This paper presents the results of an extensive content analysis of more than 90 expert system applications in the field of production and operations management. The content analysis presented here focuses on describing the knowledge acquisition techniques used and also on the problem domain that the applications address. For years developers have speculated that certain types of problems such as planning, are more difficult, but may yield a higher-impact system. Likewise, developers and empirical KA researchers have tried to determine the conditions under which certain KA techniques will work better than others. Researchers have also speculated for years about which techniques might work best for different problem domains. By carefully analyzing this large body of case studies and operationalizing the notion of the ‘impact’ of the expert system application, we can now begin to make normative conclusions about which techniques and which problem domains seem to yield applications that have the highest impact on the respective organization. This is also the first study that will offer more tangible evidence regarding the possible linkages between problem domains and KA techniques in a more pragmatic manner than has been done previously. In an environment of shrinking IT budgets, the results of this study can provide managers with real guidance about what projects may have the biggest potential impact on their organization. [Copyright &y& Elsevier]

Training is a critical issue for operators responsible for the efficient operation of complex dynamic systems. Consequently, the number of utilities that have invested in simulator-based programs has grown and the necessity of extending the availability of simulator-based training, beyond the limited use of simulators has emerged. Trying to increase the training effectiveness of simulators, intelligent training systems (ITS) are being developed. The objective of an ITS is to increase the training effectiveness, providing a means to have additional training time. It benefits the utility by cutting down operational costs of a training simulator and improving the training methodology of the simulator. This paper describes an ITS designed for the operators'' training of fossil-fuelled electrical power plants. The system consists of a real time simulation of a 300 MW turbine from zero to rated speed, a computer model of a turbine operator expert and other components that allow for evaluation and coaching of a turbine operator engineer. The rationale for such a system is presented. The architectural design, procedure and approach taken in constructing such a system for practical application are described. Instructional content and strategies to teach a novice operator how to supervise and control the turbine startup are included. [Copyright &y& Elsevier]

Today''s economy runs on knowledge and more companies work assiduously to capitalize on knowledge support systems. Hypermedia can be used for effective coordination and sharing of knowledge. This paper proposes a methodology for capturing knowledge by the use of hypermedia model. This hypermedia model can link knowledge to data warehousing systems. The methodology consists of three phases: knowledge elicitation, hypermedia modeling, and system implementation. The emphasis is on systematic conversion of knowledge into hypermedia artifacts and data warehouse components. A real-life case for a medical data warehousing system is illustrated to demonstrate the usefulness of the proposed methodology. Our methodology is better able to help put the corporate knowledge into wider sharing. [Copyright &y& Elsevier]

To an increasing extent since the late 1980s, software learning methods including neural networks (NN) and case based reasoning (CBR) have been used for prediction in financial markets and other areas. In the past, the prediction of foreign exchange rates has focused on isolated techniques, as exemplified by the use of time series models including regression models or smoothing methods to identify cycles and trends. At best, however, the use of isolated methods can only represent fragmented models of the causative agents, which underlie business cycles. Experience with artificial intelligence applications since the early 1980s points toward a multistrategy approach to discovery and prediction.This paper investigates the impact of momentum bias on forecasting financial markets through knowledge discovery techniques. Different modes of bias are used as input into learning systems using implicit knowledge representation (NNs) and CBR. The concepts are examined in the context of predicting movements in the Japanese yen. [Copyright &y& Elsevier]

Remediation selection expert system (RSES) is a rule-based expert system which is used for the selection of remediation techniques for petroleum contaminated sites. In this paper, we describe a fuzzy logic-based subsystem: site characterization subsystem (SCSS). It is an enhancement of the RSES, which is used to analyse the hydraulic properties of contaminated sites. This paper focuses on an explanation on how to apply fuzzy set theory for identification of soil types and hydraulic properties of a contaminated site. To illustrate application of fuzzy set theory to the problem, two sample cases are presented in detail. [Copyright &y& Elsevier]

This paper reports on the development of a relational knowledge-based decision support system for urban planning in general and industrial site selection in particular. The system treats the concept of site suitability as a matching process, using decision tables (DTs). The proposed computer-based system is tested using the locational choice problem of an industrial company. The system has been given the acronym MATISSE: “Matching Algorithm, A Technique for Industrial Site Selection and Evaluation”. The knowledge base of the system was created by conducting a series of in-depth interviews supplemented with a detailed survey of the relevant literature. Using this information, a series of decision tables could be constructed using prologa95. In total, 90 crisp (sub)decision tables were constructed. This set of DTs can be used as a decision support system to select and evaluate potential sites, given a set of locational requirements. [Copyright &y& Elsevier]

Over the past decade there has been considerable experimentation with the modeling of software architecture than can provide a basis for description of the KBS architectures. As the field of the component-based systems (CBS) has matured, there has emerged among this community general consensus or ontology that determines a common foundation of concepts and concerns for architectural description. In this paper we propose a modeling technique which permits to describe a KBS architecture according to two CBS axis: the components architecture axis and the levels of granularity of components axis. The components architecture axis allows to see a KBS as a set of inter-related components for reuse purposes. The levels of granularity of components axis allows to describe the KBS components according to different levels of granularity for clarity and reuse purposes. We also see how the proposed architecture, called Y architecture, allows to see the KBS components as (re)usable components according to the for-reuse CBS engineering and the by-reuse CBS engineering. The for-reuse CBS engineering describes the way of constructing reusable components and the by-reuse CBS engineering the way to reuse pre-existing components for constructing specific applications. [Copyright &y& Elsevier]

To get the items that a buyer wants in an Internet auction, he must search for the items through several auction sites. When the bidding starts, the buyer needs to connect to these auction sites frequently so that he can monitor the bid states and re-bid. A reserve-price auction reduces the number of connections, but this limits the user''s bidding strategy. Another problem is equity between the buyer and the seller. Both the buyer and the seller should profit within proper limits. In this paper, we propose an auction agent system using a collaborative mobile agent and a brokering mechanism called MoCAAS (Mobile collaborative auction agent system), which mediates between the buyer and the seller and executes bidding asynchronously and autonomously. This reduces the network load more than with other auction-agents, offers more intelligent bidding, and increases the clear ratio. [Copyright &y& Elsevier]

One of the most important problems on rule induction methods is that they cannot extract rules, which plausibly represent experts'' decision processes. On one hand, rule induction methods induce probabilistic rules, the description length of which is too short, compared with the experts'' rules. On the other hand, construction of Bayesian networks generates too lengthy rules. In this paper, the characteristics of experts'' rules are closely examined and a new approach to extract plausible rules is introduced, which consists of the following three procedures. First, the characterization of decision attributes (given classes) is extracted from databases and the classes are classified into several groups with respect to the characterization. Then, two kinds of sub-rules, characterization rules for each group and discrimination rules for each class in the group are induced. Finally, those two parts are integrated into one rule for each decision attribute. The proposed method was evaluated on a medical database, the experimental results of which show that induced rules correctly represent experts'' decision processes. [Copyright &y& Elsevier]