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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.

Reactive rules are used for programming rule-based Web agents, which have the ability to detect events and respond to them automatically and can have complex structure and unpredictable behavior. The aim of this paper is to provide an appropriate formal framework for analyzing such rules.

To achieve this goal, the authors give two alternative semantics for the basic reactive rules’ families which allow us to specify reactive rule-based agents and verify their intended behavior. The first approach expresses the functionality of production and event condition action rules in terms of equations, whereas the second methodology is based in the formalism of rewriting logic. Both semantics can be expressed within the framework of CafeOBJ algebraic specification language, which then offers the verification support and have their advantages and downsides.

The authors report on experiences gained by applying those methodologies in a reactive rule-based system and compare the two methodologies.

Finally, the authors demonstrate a tool that translates a set of reactive rules into CafeOBJ rewrite rules, thus making the verification of reactive rules possible for inexperienced users.

The purpose of this paper is to make research on causing mechanism of meteorological disaster as well as the components of meteorological disaster system and their semantic relationships. It has important practical significance due to the urgent need of further providing support for pre-assessment of influences of disastrous weather/climate events and promoting the level of emergency management.

This paper analyses the occurrence regulations and components of meteorological disasters and proposes the concept of meta-action. Ontology modelling method is adopted to describe the components and relationships among different parts comprising meteorological disaster system, and semantic web rule language is selected to identify the implicit relationships among the domain knowledge explicitly defined in ontology model. Besides, a case is studied to elaborate how to provide logic and semantic information support for comprehensive risk assessment of disastrous weather/climate events based on rule-based ontology reasoning method. It proves that ontology modelling and reasoning method is effective in providing decision makings.

This paper provides deep analyses about causing mechanisms of meteorological disasters, and implements information fusion of the components of meteorological disaster system and acquisition of potential semantic relations among ontology components and their individuals.

In this paper, on the basis of analysing the disaster-causing mechanisms, the meteorological disaster ontology (MDO) model is proposed by using the ontology modelling and reasoning method. MDO can be applied to provide decision makings for meteorological departments.

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.

The purpose of this paper is to propose a three-staged approach to configuration change management that uses a combination of complexity analysis, data visualization, and algorithmic validation to assist in validating configuration changes.

In order to accomplish the above purpose, the authors conducted a review of existing configuration management practices. This was followed by an in-depth case study of the configuration management practices of a major automotive OEM. The primary means of data collection for the case study were interviews, ethnographic study, and document analysis. Based on the results of the case study, a set of support tools is proposed to assist in the configuration management process.

Through the case study, the authors identified that the OEM used a configuration management method that largely represented the rule-based reasoning methods identified in the literature review. In addition, many of the associated challenges are present, primarily, the difficulty in making changes to the rule system and evaluating the changes.

The primary limitation is that the case study was based on a single OEM. However, the results are in line with other practices identified in the literature review. Therefore, it is expected that the findings and recommendations should hold true in other applications.

A set of configuration management tools and associated requirements are identified and defined that could be used to assist companies in the automotive industry, and perhaps others, in managing their option changes as they continue to move towards full mass customization of products.

The proposed approach for configuration management has not been seen in any other organization. The value of this paper is in the effectiveness of the proposed approach in assisting in the configuration change management process.

XML Schema is used to define schema of XML documents that have become standards for data exchange in various Web-based information applications. The main problem of XML Schema is that it emphasizes syntax and format rather than semantics and knowledge representation. Hence, even though having the advantage of describing the structure and constraining the contents of XML documents, XML Schema lacks the computer-interpretability to support knowledge representation for existing information systems. The purpose of this study is to propose role-mapping annotations for XML Schema (RMAXS) to integrate Semantic Web with XML Schema, which allows the facilitation interoperability between adjoining layers of the Semantic Web stack.

The XML, XML Schema, ontology, and rule can be completely integrated into a multi-layered intelligent framework (MIF) for XML-based applications in the current web environment. This work presents a semantic-role-mapping intelligent system, called SRMIS, based on the MIF. SRMIS consists of XML-based document repository, search engine, inference engine and transformation engine, which provides different approaches to present the various metadata and knowledge representations.

The traditional Semantic Web stack has three gaps between adjoining layers. The first gap, between the XML and XML Schema layers can be bridged with an XMLSchema-instance mechanism. The third gap, between the ontology and rule layers can be connected by building rules on top of ontologies. This study proposes RMAXS to couple the second gap, between the XML schema and ontology layers. The proposed multi-layered intelligent framework (MIF) adopts these coupling technologies to facilitate interoperability between adjoining layers. Therefore, the XML, XML Schema, ontology, and rule can be completely integrated into the MIF for intelligent applications in the web environment.

To demonstrate the SRMIS applications, this work implements a prototype that helps researchers to find interested papers.

This work presents a semantic-role-mapping intelligent system, called SRMIS, based on the MIF. SRMIS consists of XML-based document repository, search engine, inference engine and transformation engine, which provides different approaches to present the various metadata and knowledge representations. The proposed SRMIS can be applied in various application domains.

This paper aims to solve the major assessment problem in matching the satisfaction of psychological gratification and mission accomplishment pertaining to volunteers with the disaster rescue and recovery tasks.

An extended belief rule-based (EBRB) method is applied with the method's input and output parameters classified based on expert knowledge and data from literature. These parameters include volunteer self-satisfaction, experience, peer-recognition, and cooperation. First, the model parameters are set; then, the parameters are optimized through data envelopment analysis (DEA) and differential evolution (DE) algorithm. Finally, a numerical mountain rescue example and comparative analysis between with-DEA and without-DEA are presented to demonstrate the efficiency of the proposed method. The proposed model is suitable for a two-way matching evaluation between rescue tasks and volunteers.

Disasters are unexpected events in which emergency rescue is crucial to human survival. When a disaster occurs, volunteers provide crucial assistance to official rescue teams. This paper finds that decision-makers have a better understanding of two-sided match objects through bilateral feedback over time. With the changing of the matching preference information between rescue tasks and volunteers, the satisfaction of volunteer's psychological gratification and mission accomplishment are also constantly changing. Therefore, considering matching preference information and satisfaction at two-sided match objects simultaneously is necessary to get reasonable target values of matching results for rescue tasks and volunteers.

Based on the authors' novel EBRB method, a matching assessment model is constructed, with two-sided matching of volunteers to rescue tasks. This method will provide matching suggestions in the field of emergency dispatch and contribute to the assessment of emergency plans around the world.

This paper aims to propose an intelligent system that serves as a cost estimator when new part orders are received from customers.

The methodologies applied in this study were case-based reasoning (CBR), analytic hierarchy process, rule-based reasoning and fuzzy set theory for case retrieval. The retrieved cases were revised using parametric and feature-based cost estimation techniques. Cases were represented using an object-oriented (OO) approach to characterize them in n-dimensional Euclidean vector space.

The proposed cost estimator retrieves historical cases that have the most similar cost estimates to the current new orders. Further, it revises the retrieved cost estimates based on attribute differences between new and retrieved cases using parametric and feature-based cost estimation techniques.

The proposed system was illustrated using a numerical example by considering different lathe machine operations in a computer-based laboratory environment; however, its applicability was not validated in industrial situations.

Different intelligent methods were proposed in the past; however, the combination of fuzzy CBR, parametric and feature-oriented methods was not addressed in product cost estimation problems.

Modern prejudice was examined as a potential predictor of overestimating proportions of minority employees in gender-typed occupations. Strength of conjunction error was considered as an indicator of distorted perceptions of these proportions. Furthermore, the purpose of this paper is to investigate whether the association between modern prejudice and strength of conjunction error was weaker for gender-untypical than for gender-typical targets.

Modern prejudice was considered as a predictor of overestimations of black female employees in Study 1 (n=183) and black female older employees in Study 2 (n=409). Data were collected using internet-mediated questionnaires.

In Study 1, modern racism, but not modern sexism, was associated with greater strength of conjunction error when respondents were presented with gender-typical targets. In Study 2, using a sample scoring higher on modern prejudice than in Study 1, modern racism, but not modern sexism and modern ageism, was associated with greater strength of conjunction error, irrespective of target occupation. Furthermore, there was an unexpected association between lower sexism and greater strength of conjunction error for gender-typical targets, but not for gender-untypical targets.

The findings lend support to the ethnic-prominence hypothesis in that modern racism, but not modern sexism or modern ageism, was associated with greater strength of conjunction error. Furthermore, empirical evidence suggests that target non-prototypicality can dilute the effect of modern prejudice on strength of conjunction error.

This is one of the rare studies examining attitudes and conjunction error in a work-relevant context, thereby bridging the gap between social cognition and applied psychology.

The purpose of this paper is to first evaluate the effect of IT service management (ITSM) tools in a practical environment followed by sharing experience in developing management process modules in a service outsourcing model. In order to improve the fault correlation from business and user perspectives, the aim is to propose a framework to automate network and system alerts with respect to its business service impact and user impact for proactive notification to IT operations management.

Three years of quantitative analysis using real operational data were used to present the effect on ITSM tools adoption. For the proposed framework, it consists of a hybrid case‐ and rule‐based reasoning module and a new approach for fault mapping with business criticality and user activities.

Over the past decade there has been significant focus in the context of ITSM in the IT services operations industry. In the market of ITSM software tools, customer and operational processes are not sufficiently developed nor integrated with other management applications following IT services daily processes which make it difficult to correlate faults to business service impacts and user impacts. For any fault of the same severity level, traditional fault discovery and notification tools provide equal weighting from business and user points of view.

Most of the related works were done individually in the entire ITSM processes. Moreover, some works present the enabling technology for outsourced facilities management rather than IT operations management. Lack of research activity was noted in the areas of user and business impact correlation with service management.

This paper outlines the implications of implementing ITSM tools in outsourced IT operation. The business continuity planning also forms one of the critical factors to improve responsiveness in service management.

This paper illustrates the effect of ITSM tools adoption by analyzing real operation data. Central to the service‐oriented philosophy in ITSM, we introduce a framework to correlate with user and business elements. Inclusion of the dimensions of business and user impact in the fault correlation process could further improve service efficiency and user satisfaction.