Search results

Previous approaches to decision support for project planning using rule‐based expert systems techniques have failed to make an impact in practice. This is primarily because of the complexity and large‐scale nature of construction information, and problems with expert systems including: knowledge acquisition; rule‐based knowledge representation; information storage (or memory); learning; and robustness. Case‐based reasoning is one area of current research which may hold the key to overcoming a number of these problems. In the present paper, previous related case‐based reasoning work is examined. The key factors which influence the formulation of construction project plans are identified. This knowledge is used to develop a conceptual framework within which previous planning experiences can be captured and reused in new situations as a means of providing system decision support in construction planning and control. A prototype system, CBRidge, developed to test and demonstrate the concepts within the framework is presented. The results are very encouraging and provide a sound basis for the further development of case‐based reasoning for construction planning in practice.

The primary objective of this research is to investigate the impact of task‐technology fit on users’ performance when using artificial intelligence systems for auditing tasks. Four artificial intelligence auditing systems, two problem‐solving programs, and four questionnaires were developed. A laboratory experiment was performed with 292 undergraduate auditing students. The results suggested that the effect of task‐technology fit on accuracy in solving problems was marginal for case‐based reasoning with unstructured tasks. No significant effect was found on problem‐solving accuracy for rule‐based reasoning with structured tasks. The task‐technology fit, however, marginally increased users’ certainty of the correctness of their solutions.

This paper presents a hybrid artificial intelligence (AI) system capable of integrating techniques of case‐based reasoning, rule induction and expert system, using them for knowledge acquisition and problem solving of selecting appropriate retaining wall systems at the project planning stage. The proposed hybrid system can eliminate the bottleneck of knowledge acquisition in developing a knowledge‐based system and improve the solution quality of the AI‐based system. Test results indicate that solutions generated by the proposed hybrid system are better than those generated by using a single technique.

Delivering a building project on time and under budget is still an increasingly complex and risky business. A host of new project delivery methods and management techniques have been promoted to help achieve this. Developing a project delivery strategy and contractual relationships is a particularly complex and large problem domain that requires diverse, highly sophisticated skills, expertise and knowledge. Expert knowledge and lessons learned in the construction phase of a building project hold a wealth of knowledge and expertise that can be often taken for granted. However, this body of knowledge is not systematically incorporated into the procurement phase of subsequent projects. Case‐based reasoning is a technology for problem solving based on recall and reuse of specific experiences; it offers techniques for acquiring, representing and managing the knowledge gained from previous experiences and augmenting a set of specific experiences with generalised knowledge. Outlines how current case‐based reasoning techniques support project delivery method decisions. Presents a case‐based framework for project delivery method selection. Finally, reports the findings of the first phase of the system development cycle.

This study aims to propose a blackboard approach using multistrategy machine learning student modeling techniques to learn the properties of students' inconsistent behaviors during their learning process.

These multistrategy machine learning student modeling techniques include inductive reasoning (similarity‐based learning), deductive reasoning (explanation‐based learning), and analogical reasoning (case‐based reasoning).

According to the properties of students' inconsistent behaviors, the ITS (intelligent tutoring system) may then adopt appropriate methods, such as intensifying teaching and practicing, to prevent their inconsistent behaviors from reoccurring.

This research sets the learning object on a single student. After the inferences are accumulated from a group of students, what kinds of students tend to have inconsistent behaviors or under what conditions the behaviors happened for most students can be learned.

Encouraging socio‐economic development in developing countries has resulted in many changes in the lifestyle of communities. Changes in dietary patterns are one of the main outcomes from the rapid socio‐economics advancement, for example excessive intake of fat, high‐protein diet (animal protein), salt and preservatives. Chronic diseases such as diabetes, coronary artery disease, hypertension and cancer are mostly related to diet. With the community becoming more nutrition and health conscious, one of the challenges faced is to make sure that the information and knowledge on diet and healthy lifestyle gets across to the community. This paper presents a model of web‐based diet system (WebDIET) that attempts to make diet information and menu plans that are customised to local preference more accessible via the use of Internet. The system is to be used by dieticians who serve as administrators and the public who are the end users. The dietary standard adapted in developing the system is Recommended Dietary Allowances (RDA) for Malaysia. The Malaysian Dietary Guidelines was also referred as it emphasises on Malaysian diet. The system consists of six modules namely Authentication Module, Menu Plan Module, Diabetic Menu Plan Module, Food Selection Module, Disease Info Module and Feedback Module. Diabetic menu plan module models the reasoning process employed by dieticians in suggesting menu plans. The planning task is solved using an artificial intelligence technique through the case‐based reasoning (CBR) approach. CBR, generally describes, the process of solving the current problem based on the proposed solution of similar problems in the past. Nearest Neighbour Algorithm was used to compute the similarities in weighted average. Tools used for the development of the system are Microsoft Visual Interdev, Microsoft FrontPage 2000, while HTML, VBScript and JavaScript are the scripting languages used to develop the system.

This paper aims to apply a contextual case-based reasoning (CBR) to a mobile device. The CBR method was chosen because it does not require training, demands minimal processing resources and easily integrates with the dynamic and uncertain nature of pervasive computing. Based on a mobile user’s location and activity, which can be determined through the device’s inertial sensors and GPS capabilities, it is possible to select and offer appropriate services to this user.

The proposed approach comprises two stages. The first stage uses simple semantic similarity measures to retrieve the case from the case base that best matches the current case. In the second stage, the obtained selection of services is then filtered based on current contextual information.

This two-stage method adds a higher level of relevance to the services proposed to the user; yet, it is easy to implement on a mobile device.

A two-stage CBR using light processing methods and generating context aware services is discussed. Ontological location modeling adds reasoning flexibility and knowledge sharing capabilities.

A fixture is a special tool used to accurately and stably locate the workpiece during machining process. Proper fixture design improves the quality and production of parts and also facilitates the interchangeability of parts, which is prevalent in much of modern manufacturing. The purpose of this paper is to combine the rule‐based reasoning (RBR) and case‐based reasoning (CBR) method for machining fixture design in a virtual reality (VR) based integrated system.

In this paper, an approach combining the RBR and fuzzy comprehensive judgment method is proposed for reasoning suitable locating features and clamping features. Based on the reasoning results, a CBR method for machining fixture design is then presented.

The paper finds that the proposed system is an efficient tool for machining fixture design.

The proposed system enables the designers to perform fixture design with automated fixture locating method reasoning and make a new fixturing solution quickly by referencing previous design cases.

A VR application for machining fixture design is presented.

An intelligent supplier relationship management system (ISRMS) integrating a company’s customer relationship management (CRM) system, supplier rating system (SRS) and product coding system (PCS) by the case based reasoning (CBR) technique to select preferred suppliers during the new product development (NPD) process 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, as well as the delay in delivery of goods of suppliers after order allocation, are greatly reduced. In addition, performance of suppliers can be monitored effectively.

This paper describes the application of case based reasoning (CBR) to decision support for design managers and engineers during the early phases of new product development projects, in a concurrent engineering environment. The paper discusses the rationale of using CBR, emphasising its suitability for ill‐defined, unstructured problems, in comparison with traditional knowledge‐based systems. The overall research approach is presented, the importance of case collection, case base maintenance and user training is highlighted and the pre‐requisites for effective use of the system are discussed. Finally, the benefits and costs of the CBR system, as perceived by the user companies, are discussed. The experimental nature of the approach is emphasised and it is shown that the industrial environment for which the system is designed and in which it is used has great bearing on its capability.