Showing papers in "Expert Systems With Applications in 2001"

The knowledge chain model: activities for competitiveness

Abstract: Today, there is a growing recognition by researchers and practitioners about the importance of managing knowledge as a critical source for competitive advantage. Various assertions about competitiveness through knowledge management (KM) are consistent with results of empirical studies and lessons learned on the knowledge highways and byways. In spite of these macro-level contentions and success stories, there has been little investigation of a systematic means for studying connections between KM activity and competitiveness. This chapter advances a knowledge chain model that identifies and characterizes KM activities an organization can focus on to achieve competitiveness. The model is analogous to Porter’ s value chain and is grounded in a descriptive KM framework developed via a Delphi-study involving international KM experts. It is comprised of five primary activities that an organization’ s knowledge processors perform in manipulating knowledge resources, plus four secondary activities that support and guide their performance. Each activity is discussed in detail, including examples. Evidence is provided from the literature illustrating each activity’ s role in adding value to an organization to increase its competitiveness through improved productivity, agility, reputation, and innovation. In conclusion, we present some observations about avenues for future research to extend, test, and apply the model in business practices.

Knowledge management and its link to artificial intelligence

Abstract: Knowledge management is an emerging area which is gaining interest by both industry and government. As we move toward building knowledge organizations, knowledge management will play a fundamental role towards the success of transforming individual knowledge into organizational knowledge. One of the key building blocks for developing and advancing this field of knowledge management is artificial intelligence, which many knowledge management practitioners and theorists are overlooking. This paper will discuss the emergence and future of knowledge management, and its link to artificial intelligence.

Intelligent lessons learned systems

Abstract: Lessons learned processes have been deployed in commercial, government, and military organizations since the late 1980s to capture, store, disseminate, and share experiential working knowledge. However, recent studies have shown that software systems for supporting lesson dissemination do not effectively promote knowledge sharing. We found that the problems with these systems are related to their textual representation for lessons and that they are not incorporated into the processes they are intended to support. In this article, we survey lessons learned processes and systems, detail their capabilities and limitations, examine lessons learned system design issues, and identify how artificial intelligence technologies can contribute to knowledge management solutions for these systems.

An expert system for multi-criteria decision making using Dempster Shafer theory

Abstract: This paper outlines a new software system we have developed that utilises the newly developed method (DS/AHP) which combines aspects of the Analytic Hierarchy Process (AHP) with Dempster–Shafer Theory for the purpose of multi-criteria decision making (MCDM). The method allows a decision maker considerably greater level of control (compared with conventional AHP methods) on the judgements made in identifying levels of favouritism towards groups of decision alternatives. More specifically, the DS/AHP analysis allows for additional analysis, including levels of uncertainty and conflict in the decisions made, for example. In this paper an expert system is introduced which enables the application of DS/AHP to MCDM. The expert system illustrates further the usability of DS/AHP, also including new aspects of analysis and representation offered through using this method. The principal application used to illustrate this expert system is that of identifying those residential properties to visit (view), from those advertised for ales through a real estate brokerage firm.

A stage model of organizational knowledge management: a latent content analysis

Abstract: This study develops an integrated management framework for building organizational capabilities of knowledge management. The framework consists of four major components of management: organizational knowledge, knowledge workers, knowledge management processes, and information technology. Based on the framework, this study proposes a stage model of organizational knowledge management encompassing the initiation, propagation, integration, and networking stages. Each of the four stages is differentiated in terms of its management goals, activities, and characteristics of the management components. To validate the proposed stage model, we conducted a latent content analysis of 21 knowledge management case reports. While the results do not validate the time sequence of each stage, they do reveal meaningful clustering of distinct case organizations in different knowledge management implementation stages.

Mining the change of customer behavior in an internet shopping mall

Abstract: Understanding and adapting to changes of customer behavior is an important aspect for a internet-based company to survive in a continuously changing environment. The aim of this paper is to develop a methodology which detects changes of customer behavior automatically from customer profiles and sales data at different time snapshots. For this purpose, we first define the three types of changes as emerging pattern, unexpected change and the added/perished rule, then, we develop similarity and difference measures for rule matching to detect all types of change. Finally, the degree of change is evaluated to detect significantly changed rules. Our proposed methodology can evaluate the degree of changes as well as detect all kinds of change automatically from different time snapshot data. A case study on an internet shopping mall for evaluation of this methodology is also provided.

Knowledge-based parametric design of mechanical products based on configuration design method

Abstract: Parametric modeling and configuration design methods are key technologies for mass customization in manufacturing. This paper describes the parametric modeling process of machine tool, and proposes a framework which parametrically models a machine tool assembly based on a design expert system. The concept of design unit, which is one level higher than functional feature, and the parametric modeling concept with functional features have been proposed. The domain knowledge in the knowledge-base has been mapped to the geometry of the CAD system. A design expert system to redesign assemblies of a machine tool has been implemented, because commercial CAD systems cannot handle the parametric design of assemblies. This system consists of a commercial expert system shell, a design knowledge-base, and a commercial CAD system with an API program. The API program interfaces the expert system with the CAD system through a GUI.

Mining association rules procedure to support on-line recommendation by customers and products fragmentation

Abstract: Electronic Commerce (EC) has offered a new channel for instant on-line shopping. However, there are too many various products available from a great number of virtual stores on the Internet for Internet shoppers to select. On-line one-to-one marketing therefore becomes a great assistance to Internet shoppers. One of the most important marketing resources is the prior daily transaction records in the database. The great amount of data not only gives the statistics, but also offers the resource of experiences and knowledge. It is quite natural that marketing managers can perform data mining on the daily transactions and treat the shoppers the way they prefer. However, the data mining on a significant amount of transaction records requires efficient tools. Data mining from automatic or semi-automatic exploration and analysis on a large amount of data items set in a database can discover significant patterns and rules underlying the database. The knowledge can be equipped in the on-line marketing system to promote Internet sales. The purpose of this paper is to develop a mining association rules procedure from a database to support on-line recommendation. By customers and products fragmentation, product recommendation based on the hidden habits of customers in the database is therefore very meaningful. The proposed data mining procedure consists of two essential modules. One is a clustering module based on a neural network, Self-Organization Map (SOM), which performs affinity grouping tasks on a large amount of database records. The other rule is extraction module employing rough set theory that can extract association rules for each homogeneous cluster of data records and the relationships between different clusters. The implemented system was applied to a sample of sales records from a database for illustration.

Web personalization expert with combining collaborative filtering and association rule mining technique

Abstract: Web personalization has been providing electronic businesses with ways to keep existing customers and to obtain new ones. There are two approaches for providing personalized service: a content-based approach and a collaborative filtering approach. In the content-based approach, it is not easily applied to web objects (pages, images, sounds, etc) which are represented by multimedia data type information. Collaborative filtering approaches have cold-start problem. More serious weakness of collaborative filtering is that rating schemes can only be applied to homogenous domain information. In this paper, we present a framework of personalization expert by combining collaborative filtering method and association rule mining technique to overcome problems that traditional personalized systems have. Since multimedia data type web object cannot be easily analyzed, we adopted a collaborative filtering method that considers each object as an item, and attempts a personalized service. Similar users of each domain object are found as the result of the collaborative filtering method. These similar users’ web object access data is used by apriori algorithm to discover object association rules.

Software agents for knowledge management: coordination in multi-agent supply chains and auctions

Abstract: A fundamental question that must be addressed in software agents for knowledge management is coordination in multi-agent systems. The coordination problem is ubiquitous in knowledge management, such as in manufacturing, supply chains, negotiation, and agent-mediated auctions. This paper summarizes several multi-agent systems for knowledge management that have been developed recently by the author and his collaborators to highlight new research directions for multi-agent knowledge management systems. In particular, the paper focuses on three areas of research: • Coordination mechanisms in agent-based supply chains. How do we design mechanisms for coordination, information and knowledge sharing in supply chains with self-interested agents? What would be a good coordination mechanism when we have a non-linear structure of the supply chain, such as a pyramid structure? What are the desirable properties for the optimal structure of efficient supply chains in terms of information and knowledge sharing? Will DNA computing be a viable tool for the analysis of agent-based supply chains? • Coordination mechanisms in agent-mediated auctions. How do we induce cooperation and coordination among various self-interested agents in agent-mediated auctions? What are the fundamental principles to promote agent cooperation behavior? How do we train agents to learn to cooperate rather than program agents to cooperate? What are the principles of trust building in agent systems? • Multi-agent enterprise knowledge management, performance impact and human aspects. Will people use agent-based systems? If so, how do we coordinate agent-based systems with human beings? What would be the impact of agent systems in knowledge management in an information economy?

Knowledge-based models for emergency management systems

Abstract: This paper proposes the use of advanced knowledge models to support environmental emergency management as an adequate response to the current needs and technology. A generic architecture embodying the knowledge pieces required to manage emergencies in different kinds of problem scenarios is described. Simulation models of the physical system, integrated as part of the knowledge architecture, are also claimed to be adequate, both from the point of view of the knowledge model calibration and the training of the emergency personnel as well. The feasibility of the approach has been demonstrated with the application of the generic model to a particular real world problem: the management of flood emergencies in the Jucar river basin area (Spain). This work was developed in the framework of ARTEMIS, a European Commission research project.

The cluster-indexing method for case-based reasoning using self-organizing maps and learning vector quantization for bond rating cases

Abstract: This paper presents a hybrid data mining model for the prediction of corporate bond rating. This model uses a new case-indexing method of case-based reasoning (CBR), which utilizes the cluster information of financial data in order to improve classification accuracy. This method uses not only case-specific knowledge of past problems like conventional CBR, but also uses additional knowledge derived from the clusters of cases. The cluster-indexing method assumes that there are some distinct subgroups (clusters) in each rated group. Competitive artificial neural networks are used to generate the centroid values of clusters because these techniques produce better adaptive clusters than statistical clustering algorithms. The experiments using corporate bond rating cases show that the cluster-indexing CBR is superior to conventional CBR and inductive learning-indexing CBR—a rival case indexing method.

Expert systems for knowledge management: crossing the chasm between information processing and sense making

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.

Multiagent-based knowledge networks

Abstract: In this article, we present an approach for the design and development of knowledge networks and corporate memories based on Multi-Agent Systems (MAS) technology. A corporate memory is conceptualized as a network of agents that collaborate to provide the users with knowledge services for both intranets and the Internet. Lessons learned from the introduction of knowledge management practices into organizations are presented. These lessons have influenced and driven the MAS approach to knowledge networks and corporate memories described in this paper. An implementation of the RICA system (Knowledge and Information Networks with Agents), which incorporates these ideas, is presented.

An intelligent decision support system for management of petroleum-contaminated sites

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.

Mixed-initiative synthesized learning approach for web-based CRM

Abstract: The issue of customer relationship management has emerged rapidly. Customers have become one of the most important considerations to new companies being built. Accordingly, customer retention is a very important topic. In this paper, we present a mixed-initiative synthesized learning approach for better understanding of customers and the provision of clues for improving customer relationships based on different sources of web customer data. The approach is a combination of hierarchical automatic labeling SOM, decision tree, cross-class analysis, and human tacit experience. The objective of this approach is to hierarchically segment data sources into clusters, automatically label the features of the clusters, discover the characteristics of normal, defected and possibly defected clusters of customers, and provide clues for gaining customer retention.

Maintaining case-based reasoning systems using a genetic algorithms approach

Abstract: The success of a case-based reasoning (CBR) system largely depends on an effective maintenance of its case-base. This study proposes a genetic algorithms (GAs) approach to the maintenance of CBR systems. This approach automatically determines the representation of cases and indexes relevant attributes to grasp the rapidly changing environment around the system. In this study, the proposed model is applied to stock market analysis. Experimental results show that the proposed model outperforms conventional CBR systems.

Intellectual capital: from intangible assets to fitness landscapes

Abstract: Intellectual Capital (IC) has been proposed by Edvinsson and Malone (Intellectual capital, Harper, 1997) as a technique for quantifying a company's intangible assets. A careful analysis can result in hundreds of variables, and extracting knowledge from these measurements can be difficult. We introduce a knowledge management technique called IC mapping that attempts to synthesize this data into a fitness landscape. Using the map, managers can query the surrounding landscape, view the company's trajectory across the landscape, and calculate what parameters need to be changed to reach new locations. IC mapping provides a novel knowledge management tool for understanding, managing, and representing a company's intangible knowledge assets.

A multi-agent intelligent system for efficient ERP maintenance

Abstract: The Enterprise Resource Planning (ERP) system is an enterprise-wide integrated software package designed to uphold the highest quality standards of business process. However, for the time being, when the business condition has been changed, the system may not guarantee that the process embedded in ERP is still best. Moreover, since the ERP system is very complex, maintaining the system by trial and error is very costly. Hence, this paper aims to construct a support system that adjusts ERP system to environmental changes. To do so, we adopt multi-agent intelligent technology that enables autonomous cooperation with one another to monitor ERP databases and to find any exceptional changes and then analyze how the changes will affect ERP performance. Moreover, Petri net is applied to manage the complexity and dynamics of agents’ behavior. To show the feasibility of the idea, a prototype agent system, ERP/PN, is proposed and an experiment is conducted.

Quasi-optimal case-selective neural network model for software effort estimation

Abstract: A number of software effort estimations have attempted using statistical models, case based reasoning, and neural networks. The research results showed that the neural network models perform at least as well as the other approaches, so we selected the neural network model as the estimator. However, since the computing environment changes so rapidly in terms of programming languages, development tools, and methodologies, it is very difficult to maintain the performance of estimation models for the new breed of projects. Therefore, we propose a search method that finds the right level of relevant cases for the neural network model. For the selected case set, the scale of the neural network model can be reduced by eliminating the qualitative input factors with the same values. Since there exist a multitude of combinations of case sets, we need to search for the optimal reduced neural network model and corresponding case set. To find the quasi-optimal model from the hierarchy of reduced neural network models, we adopted the beam search technique and devised the case-set selection algorithm. We have shown that the resulting model significantly outperforms the original full model for the software effort estimation. This approach can be also used for building any case-selective neural network.

Transferring auditors’ internal control evaluation knowledge to management

Abstract: Due to both regulatory and competitive forces, attention to an organization's internal controls has increased significantly in the 1990s. Although management is ultimately responsible for ensuring internal controls are adequate, managers often lack knowledge of internal control concepts. This study reports on an experiment testing an expert system developed to facilitate the transfer of internal control knowledge to management. Experimental results indicate that expert systems are viable aids for transferring internal control knowledge to managers whose work experience is outside of accounting and control systems.

An expert system for aided design of ship systems automation

Abstract: The paper presents a structure and functions of an expert system for aided design of ship systems automation. The system was developed on basis of a detailed analysis of the design process of ship systems automation. The system includes: knowledge bases regarding methods and procedures of ship systems automation design, databases of automated objects, control devices and elements, requirements of classification societies, and a subsystem for simulation investigations co-operating with the Matlab Simulink package and a knowledge base. In the creation of the system the shell expert system Exsys Developer was used. This system is characterised by a rule-oriented representation of knowledge, backward and forward chaining inference methods, various confidence modes to handle uncertain reasoning including fuzzy logic, and possibility of co-operation with other software and databases. The databases were made using the MS Access software.

A new intelligent SOFM-based sampling plan for advanced process control

Abstract: Sample measurement inspecting for a process parameter is a necessity in semiconductor manufacturing because of the prohibitive amount of time involved in 100% inspection while maintaining sensitivity to all types of defects and abnormality. In current industrial practice, sample measurement locations are chosen approximately evenly across the wafer, in order to have all regions of the wafer equally well represented, but they are not adequate if process-related defective chips are distributed with spatial pattern within the wafer. In this paper, we propose the methodology for generating effective measurement sampling plan for process parameter by applying the Self-Organizing Feature Map (SOFM) network, unsupervised learning neural network, to wafer bin map data within a certain time period. The sampling plan specifies which chips within the wafer need to be inspected, and how many chips within the wafer need to be inspected for a good sensitivity of 100% wafer coverage and defect detection. We finally illustrate the effectiveness of our proposed sampling plan using actual semiconductor fab data.

ALEES: an agricultural loan evaluation expert system

Abstract: Financial institutions such as banks have been active in the development of a variety of expert systems. Several banks have developed expert systems that capture the experience of their top loan officers in evaluating loan applications. The research literature reveals a number of criteria and measures that have been employed by researchers and lenders. While some consensus is apparent for several measures, the research clearly indicates that both quantitative and qualitative information must be employed in evaluating a loan proposal. Further, the research on rural lending has shown that subjective assessments play an important role in the evaluation of agricultural loans, but they have rarely been included as part of an expert system to appraise agricultural loan applications. Several agricultural loan evaluation systems are operational, but not all are ‘true’ expert systems. While they have all focused on analysing the borrower's financial position given the current market and economic conditions, most have not taken into account qualitative factors such as the character of the borrower. The purpose of this paper is to present an expert system that has been specifically designed and developed to incorporate both qualitative and quantitative assessments in agricultural loan evaluation. The development of the expert system is outlined; the knowledge acquisition process is described; system validation is explained, as is the loan officers’ perception of the expert system. An evaluation of several agricultural loan evaluation expert systems is also included in this paper.

A knowledge-based architecture for implementing military geographical intelligence system on Intranet

Abstract: Military geographical intelligence analysis in support of both tactical and strategic operation is a process that is associated with data gathering, processing, analyzing, and distribution. It is an important activity, carried out before the preparation of a military operation. A system that can assist in arranging support, exercise analysis training, and manage knowledge on military geographical intelligence have always been ultimate goals for both military intelligence units and intelligence officers alike. Therefore, it can be seen that research into the military geographical intelligence system is a critical and important research subject. This research investigates military geographical intelligence systems as a problem-solving procedure in terms of observing the military geographical intelligence operational procedure, generating situation analysis, and generating planning process. This paper intends to present a knowledge-based architecture that incorporates the function of case base, heuristic base, and rule base for implementing the military geographical intelligence system on Intranet.

Using KADS methodology in a simulation assisted knowledge based system: application to hospital management

Abstract: This paper presents a knowledge-based system for aiding in the decision-making process that is carried out in hospital management. There are a number of reasons that have led us to choose a tool such as this one: the amount of information generated in a hospital, its great interrelation and the need of heuristic knowledge for its processing. The KBS has been designed following the KADS methodology. KADS has allowed us to obtain a structured representation of the knowledge, which makes easier both the construction and the debugging of the knowledge base. As a starting point, the decision-making task has been decomposed in four subtasks: monitoring; diagnosis; prediction of the possible solutions for the stated problem; and design of the solution. The prediction task can only be performed through a simulation program where the dynamics of the hospital is modeled. This allows the system to detect the consequences of the application of different possible solutions. The co-operation between simulation and artificial intelligence has proven to be an adequate technique for dealing with the decision-making tasks that are involved with the management of complex organizations.

Incremental fuzzy supervisory controller design for optimizing the injection molding process

Abstract: In this paper a newly developed fuzzy supervisory control system for the injection molding process is presented The system performs automatic tuning of the machine operating points and reduces the amount of human effort needed for a complete multi-objective optimization of the machine settings The optimal tuning is facilitated by a priority policy based on the significance of defects The experimental results obtained from the application of the proposed fuzzy control architecture to a real industrial environment were very encouraging

Lessons learned from developing a mission-critical expert system with multiple experts through rapid prototyping

Abstract: The expert systems technology is ubiquitous among the mission-critical tasks in the defense area. Knowledge compiled through the military applications can serve as a remarkable source of learning opportunities for those in the civilian sector. In this paper, we describe the process of developing one such mission-critical military application, which contains an embedded expert system. A large number of experts simultaneously participated in numerous knowledge acquisition sessions to provide their expertise in air combat planning. The rapid prototyping methodology was employed as a vehicle to facilitate the knowledge acquisition process. By recounting the development process, we intend to share the lessons learned from this experience. These lessons are presented from three different perspectives, i.e. the knowledge engineers, the experts, and regarding the rapid prototyping methodology.

Personalization technology application to Internet content provider

Abstract: Personalization of Web pages relates closely to targeting the customer and increasing customer intimacy thereby leading to increased branding and, hopefully, consumer electronic commerce. Profiling individuals on the Web allows us not only to select which message to deliver to each individual, but also helps us learn about the needs and interests of each person. In this paper, a modified tracking analysis method to obtain user preference inclination was proposed and applied to simulating ICP (Internet content provider) Web site. SNT (browsing sequence, node, and time) data extracted from the simulating Web site database are used extensively for our analysis. The ability to track user browsing behavior down to individual mouse clicks has brought the vendor and end customer closer than ever before.

High Performance Knowledge Bases: four approaches to knowledge acquisition, representation and reasoning for workaround planning

Abstract: As part of the DARPA-sponsored High Performance Knowledge Bases program, four organisations were set the challenge of solving a selection of knowledge-based planning problems in a particular domain, and then modifying their systems quickly to solve further problems in the same domain. The aim of the exercise was to test the claim that, with the latest AI technology, large knowledge bases can be built quickly and efficiently. The domain chosen was ‘workarounds’; that is, planning how a convoy of military vehicles can ‘work around’ (i.e. circumvent or overcome) obstacles in their path, such as blown bridges or minefields. This paper describes the four approaches that were applied to solve this problem. These approaches differed in their approach to knowledge acquisition, in their ontology, and in their reasoning. All four approaches are described and compared against each other. The paper concludes by reporting the results of an evaluation that was carried out by the HPKB program to determine the capability of each of these approaches.