Developing a diagnostic system through integration of fuzzy case-based reasoning and fuzzy ant colony system

This study intends to propose a hybrid Case-Based Reasoning (CBR) system with the integration of fuzzy sets theory and Ant System-based Clustering Algorithm (ASCA) in order to enhance the accuracy and speed in case matching. The cases in the case base are fuzzified in advance, and then grouped into several clusters by their own similarity with fuzzified ASCA. When a new case occurs, the system will find the closest group for the new case. Then the new case is matched using the fuzzy matching technique only by cases in the closest group. Through these two steps, if the number of cases is very large for the case base, the searching time will be dramatically saved. In the practical application, there is a diagnostic system for vehicle maintaining and repairing, and the results show a dramatic increase in searching efficiency.     

Developing a distributed knowledge model for knowledge management in collaborative development and implementation of an enterprise system

Recently, enterprise systems have been extensively adopted to boost enterprise competitiveness. The development and implementation of enterprise systems is a knowledge intensive procedure, being related to enterprise processes and involving information, system and software engineering technologies. Consequently, knowledge management is required to enhance the effectiveness of enterprise system development and implementation, thus helping to increase industrial competitiveness.This study aims to develop a distributed knowledge model for knowledge management, capable of supporting the collaborative development and implementation of enterprise systems. This objective can be obtained by performing the following tasks: (1) modeling and characterization of the collaborative development and implementation process, (2) identification, analysis and modeling of involved knowledge, and (3) development of a distributed knowledge model for knowledge management related to the collaborative development and implementation of enterprise systems.     

A case-based system for process planning

Process planning is the phase of manufacturing that is concerned with the selection, and sequencing of manufacturing operations necessary to transform an initial stock material into a finished part. It is a tedious operation that requires highly skilled and experienced personnel of which there is currently great shortage. To disseminate the costly expertise and to reduce process planning times, which tend to add high costs to small batch production, many automated computer systems have been introduced and applied. All these systems, though, require the supervision of an experienced human being and fail to capture the knowledge and reasoning behind process planning decisions. The causes of the lack of an efficient automatic system for process planning are the unique characteristics of the domain. In contrast to other areas where knowledge-based and expert systems have been applied, process planning demands the solution of several different planning problems before a final solution is achieved. Every change introduced to the previous world model creates a completely new world model with its own, new constraints and preconditions that nedd to be satisfied. This, in turn, affects the applicability of the knowledge to the new model.

To provide solutions to the above problems we have designed a prototypical knowledge-based system that uses the high level, dynamic memory structures of MOPs (Memory Organization Packages), meta-MOPs and TOPs (Thematic Organization Packets) to rearrange its knowledge according to its experiences and to predict and avoid errors. To model the dynamic, sharable knowledge and the constantly changing world model a knowledge-representation language named TOLTEC has been designed and used in the system. Furthermore, we propose a novel planning method based on abstract plans and constraints that can solve planning problems in uncertain domains given incomplete information.     

Learning adaptation knowledge to improve case-based reasoning

Case-Based Reasoning systems retrieve and reuse solutions for previously solved problems that have been encountered and remembered as cases. In some domains, particularly where the problem solving is a classification task, the retrieved solution can be reused directly. But for design tasks it is common for the retrieved solution to be regarded as an initial solution that should be refined to reflect the differences between the new and retrieved problems. The acquisition of adaptation knowledge to achieve this refinement can be demanding, despite the fact that the knowledge source of stored cases captures a substantial part of the problem-solving expertise. This paper describes an introspective learning approach where the case knowledge itself provides a source from which training data for the adaptation task can be assembled. Different learning algorithms are explored and the effect of the learned adaptations is demonstrated for a demanding component-based pharmaceutical design task, tablet formulation. The evaluation highlights the incremental nature of adaptation as a further reasoning step after nearest-neighbour retrieval. A new property-based classification to adapt symbolic values is proposed, and an ensemble of these property-based adaptation classifiers has been particularly successful for the most difficult of the symbolic adaptation tasks in tablet formulation.     

Scenario-based knowledge representation in case-based reasoning systems

A scenario-based representation model for cases in the domain of managerial decision-making is proposed. The scenarios in narrative texts are converted to scenario units of knowledge organization. The elements and structure of the scenario unit are defined. The scenario units can be linked together or coupled with others. Compared with traditional case representation methods based on database tables or frames, the proposed model is able to represent knowledge in the domain of managerial decision-making at a much deeper level and provide much more support for case-based systems employed in business decision-making.     

Developing maintainable expert systems using case-based reasoning

Abstract: Maintainability problems associated with traditional software systems are exacerbated in rule-based systems. The very nature of that approach — separation of control knowledge and data-driven execution — hampers maintenance. While there are widely accepted techniques for maintaining conventional software, the same is not true for rule-based systems. In most situations, both a knowledge engineer and a domain expert are necessary to update the rules of a rule-based system. This paper presents, first, an overview of the software engineering techniques and object-oriented methods used in maintaining rule-based systems. It then discusses alternate paradigms for expert system development. The benefits of using case-based reasoning (from the maintenance point of view) are illustrated through the implementation of a case-based scheduler. The main value of the scheduler is that its knowledge base can be modified by the expert without the assistance of a knowledge engineer. Since changes in application requirements can be given directly to the system by the expert, the effort of maintaining the knowledge base is greatly reduced.     

Developing a BERT based triple classification model using knowledge graph embedding for question answering system

Applied Intelligence - The current BERT-based question answering systems use a question and a contextual text to find the answer. This causes the systems to return wrong answers or nothing if the...     

A case-based procurement advisory system for construction

The selection and use of an appropriate procurement system are fundamental to the success of a construction project. However, the procurement selection process involves the analysis of complex and dynamic criteria such as cost certainty, time certainty, speed, flexibility, etc. Procurement selection is, therefore, plagued with uncertainty and vagueness that is difficult to be represented by a generalized set of rules. In reality, decisions in procurement selection are usually derived from intuition and past experience. Case-based reasoning (CBR) appears to be an appropriate approach to meet the requirements of the procurement selection process because of the value of experiential knowledge. This paper reviews the practicality and suitability of a CBR approach for procurement selection through the development of a prototype case-based procurement advisory system. In this prototype system, procurement selection cases are represented by a set of attributes elicited from experienced procurement experts. The system is powered by a fuzzy similarity retrieval mechanism, which gives a greater accuracy than the normal similarity retrieval process. The results indicate that the CBR approach can suitably model the characteristics of construction procurement selection, and provide an indication of potential outcomes to any apparently suitable procurement methods.     

Creative design through knowledge clustering and case-based reasoning

New product design is inspired by the existing design. The clustering of similar design cases therefore enhances new product development (NPD). At the beginning of NPD, the success of creative design highly depends on the designers’ subjective judgments and try-and-error attempts due to its very obscure prospect. To facilitate an efficient approach for generating creative ideas, this paper proposes a new design method by integrating fuzzy relational analysis, case-based reasoning (CBR) and C-K theory. The proposed design method involves four specific sections: design criteria importance ranking; similarity measurement for design knowledge; knowledge clustering method for innovation and a step-by-step design algorithm. Moreover, a new battery buckling machinery is used as a empirical study to verify the workability of the proposed method. The contributed method shows its advantages to cultivate the inspirations from the existing design and generate creative design concepts from knowledge combination.     

Design of a RFID case-based resource management system for warehouse operations

In the past, the selection of resources to execute various warehouse operation services was done solely by experts. In this paper, a RFID-based Resource Management System (RFID-RMS) is designed to help users to select the most suitable resource usage packages for handling warehouse operation orders by retrieving and analysing useful knowledge from a case-based data warehouse for solutions in both time saving and cost effective manner. In addition, a pure integral-linear programming model using a branch and bound algorithm to define the optimum travel distance of forklifts is also developed and embedded in the proposed system. The proposed system, which is suitable for usage in a warehouse operation environment, enhances the effectiveness in formulating resource usage package and managing resource operation by integrating the Radio Frequency Identification (RFID), case-based reasoning (CBR) technologies and the programming model for forklift route optimization. Through applying RFID-RMS in the GENCO Distribution System, a multinational logistics company, the utilization of warehouse resources is expected to be maximized while work efficiency will be greatly enhanced.     

A strategy for evaluating a fuzzy case-based construction procurement selection system

System Verification and Validation (V&V) is an essential element in the development and implementation of any computer-based decision tools. The unique concepts of Case-Based Reasoning (CBR), such as the use of mega-knowledge and nearest matching have generated extra challenges to system developers to ensure that the system is built right and the right system is built. However, little attention has been attributed to verifying and validating a CBR system. Recently, a fuzzy CBR prototype known as CaPS has been developed for the selection of appropriate construction procurement systems. To ensure that the procurement system is acceptable to the procurement experts in the construction industry, a series of tests have been conducted with domain experts using real cases (stored in the case base) and projects (as scenarios for retrieval and comparison). This paper reports on the findings of the V&V that have been performed on CaPS. Techniques available for verifying and validating a CBR system are first discussed. The V&V procedures applied to the prototype system are subsequently outlined. The results confirm that the cases stored in CaPS are correct, consistent, and irredundant. More importantly, the solutions generated by CaPS are accurate and innovative, and these are necessary for today's construction projects.     

Design and implementation of a case-based reasoning system for marketing plans

Unstructured intangible experiences and knowledge are usually difficult to represent and instantiate, which engenders the hardship of knowledge transfer and sharing. Past marketing plans are such valuable documents containing strategic planning knowledge and experiences.Case-Based Reasoning (CBR), which consists of retrieving, reusing, revising, and retaining cases, has been proved effective in retrieving information and knowledge from prior situations and being widely researched and applied in a great variety of problem territories.This paper targets at designing a CBR architecture and a method that facilitate the sharing and retrieving of cases of great concern to the marketing personnel. After an intensive survey of CBR methods and applications, a CBR system embedding multi-attribute decision making method, which provides both overall similarity level and similarity level of each selected attribute, is proposed to enhance the adaptation of a new marketing plan. In addition, a multi-attribute gap analysis diagram is developed to visualize the similarity along with the gap between candidate and target cases, so as to better support interaction and group decision making in the process of strategically formulating a new marketing plan. The CBR system was implemented and successfully demonstrated on case retrieval of a telecommunication company.     

Developing a Web assisted knowledge construction system based on the approach of constructivist knowledge analysis of tasks

The purpose of this study is to develop a Web assisted knowledge construction (WAKC) system as an individual knowledge construction tool for Internet users. The system is based on the theory of constructivist knowledge analysis of tasks (CKAT). The CKAT integrates constructivist reflection cycle and knowledge analysis of tasks. The conceptual model of CKAT includes four different stages: knowledge objective, knowledge gathering, knowledge analysis, and task knowledge structure. In order to match these four stages, this research designs an assisted knowledge construction system that includes four systematic sub-functions: the keyword function, the URL resource function, the analysis function, and the construction function. After understanding users' perceptions toward the WAKC system, users have highly positive behavioral intention to use the system as a Web-based assisted knowledge construction tool.     

Incorporating domain-specific knowledge into a genetic algorithm to implement case-based reasoning adaptation

In case-based reasoning systems the adaptation phase is a notoriously difficult and complex step. The design and implementation of an effective case adaptation algorithm is generally determined by the type of application which decides the nature and the structure of the knowledge to be implemented within the adaptation module, and the level of user involvement during this phase. A new adaptation approach is presented in this paper which uses a modified genetic algorithm incorporating specific domain knowledge and information provided by the retrieved cases. The approach has been developed for a CBR system (CBEM) supporting the use and design of numerical models for estuaries. The adaptation module finds the values of hundreds of parameters for a selected numerical model retrieved from the case-base that is to be used in a new problem context. Without the need of implementing very specific adaptation rules, the proposed approach resolves the problem of acquiring adaptation knowledge by combining the search power of a genetic algorithm with the guidance provided by domain-specific knowledge. The genetic algorithm consists of a modifying version of the classical genetic operations of initialisation, selection, crossover and mutation designed to incorporate practical but general principles of model calibration without reference to any specific problems. The genetic algorithm focuses the search within the parameters' space on those zones that most likely contain the required solutions thus reducing computational time. In addition, the design of the genetic algorithm-based adaptation routine ensures that the parameter values found are suitable for the model approximation and hypotheses, and complies with the problem domain features providing correct and realistic model outputs. This adaptation method is suitable for case-based reasoning systems dealing with numerical modelling applications that require the substitution of a large number of parameter values.     

Developing a geoscience knowledge framework for a national geological survey organisation

Geological survey organisations (GSOs) are established by most nations to provide a geoscience knowledge base for effective decision-making on mitigating the impacts of natural hazards and global change, and on sustainable management of natural resources. The value of the knowledge base as a national asset is continually enhanced by the exchange of knowledge between GSOs as data and information providers and the stakeholder community as knowledge ‘users and exploiters’.Geological maps and associated narrative texts typically form the core of national geoscience knowledge bases, but have some inherent limitations as methods of capturing and articulating knowledge. Much knowledge about the three-dimensional (3D) spatial interpretation and its derivation and uncertainty, and the wider contextual value of the knowledge, remains intangible in the minds of the mapping geologist in implicit and tacit form.To realise the value of these knowledge assets, the British Geological Survey (BGS) has established a workflow-based cyber-infrastructure to enhance its knowledge management and exchange capability. Future geoscience surveys in the BGS will contribute to a national, 3D digital knowledge base on UK geology, with the associated implicit and tacit information captured as metadata, qualitative assessments of uncertainty, and documented workflows and best practice.Knowledge-based decision-making at all levels of society requires both the accessibility and reliability of knowledge to be enhanced in the grid-based world. Establishment of collaborative cyber-infrastructures and ontologies for geoscience knowledge management and exchange will ensure that GSOs, as knowledge-based organisations, can make their contribution to this wider goal.     

A case-based reasoning system for identifying failure mechanisms

Correctly identifying the mechanism responsible for a failure is a major step in failure analysis. Today, human experts normally perform this task. In the problem-solving process, human experts often recall similar cases to help identifying the mechanism involved. This has motivated the use of case-based reasoning to develop a computerized system for failure-mechanism identification in this study. Major issues and the methods applied are discussed. To determine its accuracy, the system is subsequently evaluated using historical cases, which are classified into two categories: standard and exceptional. The test results show that 100% accuracy can be achieved for standard cases, and that exceptional cases also attain accuracy as high as 71.25%. It is thus concluded that case-based reasoning is a viable approach for the identification of failure mechanisms.     

PROCASE: a case-based process planning system for machining of rotational parts

This paper introduces a case-based process planning system PROCASE which generates new process routines through learning from existing process routines. In contrast to traditional rule-based systems, the process planning knowledge of the PROCASE is represented in terms of cases instead of production rules. The planning basically comprises case retrieving and case adaptation rather than chaining applicable rules together to form process plans. The advantages are, first, the system is cheaper to build as it saves the expense of knowledge acquisition. Second, the system is able to advance its knowledge automatically through planning practice. Third, it is robust, because the reasoning is not based on pattern matching but similarity comparison. PROCASE has three modules: the retriever, the adapter and the simulator. It is supported by a feature-based representation scheme which naturally serves as the case indices for case retrieving and adaptation. The retriever uses a similarity metric to retrieve an old case which is the most similar case, among all old ones, to the new case. The adapter is then activated to adapt the process plan of the retrieved case to fit the needs for the new case. The simulator is used to verify the feasibility of the adapted plan. PROCASE is implemented on a Silicon Graphics IRIS workstation using C++ . An example is given to demonstrate how the process routine is generated by the system proposed by the authors.     

A case-based reasoning system for PCB defect prediction

The manufacturing process for a new Printed Circuit Board (PCB) design is often instable and might generate a number of defects during the complicated production process. Defects reduce the yield rate and increase the production costs. Although skilled engineers can predict the possible defect items for a new PCB product, this approach requires strong engineering experience and is time consuming. To conquer this problem, this research applies case-based reasoning (CBR) methodology to develop a defect prediction system for new PCB products. In the CBR system, each case is represented using the design specifications, defect items and corresponding costs. A vantage-based case indexing mechanism is developed to accelerate the case retrieval efficiency. In addition, a reasoning algorithm that considers the defect cost is proposed to infer the defect items that are interesting to PCB manufacturers. The system performance is analyzed to show the efficiency and accuracy of the proposed system. A practical implementation using a case-base provided by a PCB manufacturer is demonstrated.     

Impacts of case-based health knowledge system in hospital management: The mediating role of group effectiveness

With the rise of artificial intelligence, case-based health knowledge management systems (CBHKS) have been widely adopted in hospitals. CBHKS are data-driven intelligent platforms that integrate latest technologies, such as artificial intelligence and cloud computing. As an integral part of smart hospitals, CBHKS can support decision processes at different levels in hospitals. However, researchers have not yet clearly addressed how CBHBKS improves hospital management outcomes. Based on group effectiveness and leadership performance-maintenance theories, we develop a conceptual model to explain the role of CBHKS in hospital management. To test the research hypotheses in the conceptual model, we collected survey data from 214 doctors, and performed data analysis using partial least squares (PLS)-based structural equation modeling. The empirical testing results show that the CBHKS implementation significantly and positively influences group performance, group members’ satisfaction, group learning, and external satisfaction; and group members’ satisfaction and external satisfaction significantly and positively affect management performance and maintenance.