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Expert systems are being effectively applied to a variety of engineering problems. A growing number of languages and development tools are available for their building. Expert systems building tools (shells) are not so flexible as the high‐level languages, but they are easier to use. The problem is that there are too many development tools on the market today, no standards for their evaluation are available, so it is quite difficult to choose the ‘best’ tool for the developer's/user's needs. This paper is an attempt to review the situation on the confused market. Eighty‐six development tools are described in a table form for easy comparisons. Tools implemented on the AI machines only are not included in this survey.

Product-service system (PSS) has been attracting attentions of global manufacturing to providing high-value added services in addition to their traditional product development and manufacturing business. For this reason, it is of great importance to research PSS. The purpose of this paper is to establish a systematic strategy and a system tool for PSS design.

A requirement-driven product-service system (RdPPS) is developed using requirements analysis and knowledge management technologies. A framework is proposed to support RdPPS by providing tools and methods for requirement analysis and processing, formalization of PSS by ontology-based knowledge representation, reasoning method for PSS solution finding, and solution optimizing and assessing. Finally, the design support strategies for RdPPS are investigated to demonstrate the usability and functioning of the developed system.

Many conventional design methods did not consider the influence of customer requirements (CRs) during the planning phase of PSS design. Moreover, a broader range of knowledge is required to PSS design, since both products and services are considered.

This research provides a solid foundation for PSS, and promotes an effective means for PSS design.

A RdPSS is presented. CRs are considered during the design phase of PSS as well as both product and service knowledge.

This chapter introduces the efforts in the field of teacher education in the Czech Republic that focus on developing preservice teachers’ professional vision using a video-based e-learning environment – VideoWeb. First, the current developments in the field of education in the Czech Republic are presented. Next, preservice teacher education in the Czech Republic is briefly summed up. Drawing on this and on the description of the local context, the rationale for the approach is addressed – both the theoretical and the practical considerations. VideoWeb is an e-learning environment that uses video cases integrated into thematic modules in order to improve preservice teachers’ professional vision and help them orient themselves in the complexity of classroom situations. The implementation of VideoWeb was accompanied by research focusing on student teachers’ acceptance and evaluation of VideoWeb, on the benefits of working with VideoWeb they perceived, and also on the actual changes in their professional vision. The findings are briefly introduced. The chapter closes with a discussion of the assets and drawbacks of using VideoWeb in the way it is being implemented at the moment, what changes to our practices might be beneficial, and where our efforts might aim in the future.

Attempts to document how different forms of ignorance may evolve in different organizational dialogues and become embedded in organizational context. Develops the four primary forms of ignorance based on the research from social psychology, public opinion studies, legal studies, behavioral economics, and clinical psychology. The recognition of the historic interdisciplinary evolution of the concept of ignorance plays an important role in the knowledge economy and learning organizations. If management is not aware of the various latent forms of organizational ignorance, it is difficult to develop meaningful innovation programs for organizations in the twenty‐first century. Develops a framework to address the issue of “not knowing what one does not know” (i.e. ignorance of ignorance) that may be the biggest barrier for organizations to becoming an active participant in the knowledge economy.

The present paper reports on the development of SITE EXPERT: a prototype knowledge‐based expert system. It is an advisory system. SITE EXPERT is intended to be used for productivity improvement in construction and provides advice on: (1) the productivity of three basic operations of construction, i.e. pouring and placing of concrete, erection and removal of formwork, and fixing reinforcement; and (2) human resources and site layout as productivity factors. The system uses information from construction experts, text books, data recorded at construction sites and the engineer's own knowledge, as well as knowledge obtained by running simulation models. In the present paper, the development, operation and evaluation of the prototype system is described. The results of this prototype system development demonstrate that artificial intelligence methodologies provide powerful facilities for capturing information about construction processes and advising the practitioners of construction on productivity improvement within a computer format close to human reasoning.

Expert systems technology as an area of artificial intelligence is coming to the field of structural mechanics. A number of expert systems have been developed or are under development. This paper consists of two parts. A brief discussion of the basics of expert systems and their concepts is given in the first part. The second part reviews the prototype of expert systems developed as an aid for finite element analysis and design optimization. Twelve different expert systems are described. A partial list of books on expert systems in general is given in the Appendix.

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 map the evidence provided on the review types, and explain the challenges faced by classification techniques in sentiment analysis (SA). The aim is to understand how traditional classification technique issues can be addressed through the adoption of improved methods.

A systematic review of literature was used to search published articles between 2002 and 2014 and identified 24 papers that discuss regular, comparative, and suggestive reviews and the related SA techniques. The authors formulated and applied specific inclusion and exclusion criteria in two distinct rounds to determine the most relevant studies for the research goal.

The review identified nine practices of review types, eight standard machine learning classification techniques and seven practices of concept learning Sentic computing techniques. This paper offers insights on promising concept-based approaches to SA, which leverage commonsense knowledge and linguistics for tasks such as polarity detection. The practical implications are also explained in this review.

The findings provide information for researchers and traders to consider in relation to a variety of techniques for SA such as Sentic computing and multiple opinion types such as suggestive opinions.

Previous literature review studies in the field of SA have used simple literature review to find the tasks and challenges in the field. In this study, a systematic literature review is conducted to find the more specific answers to the proposed research questions. This type of study has not been conducted in the field previously and so provides a novel contribution. Systematic reviews help to reduce implicit researcher bias. Through adoption of broad search strategies, predefined search strings and uniform inclusion and exclusion criteria, systematic reviews effectively force researchers to search for studies beyond their own subject areas and networks.

Knowledge‐based systems, especially so‐called expert systems, which replicate the problem‐solving or decision‐making capabilities of human experts in specific areas, have recently gained considerable widespread interest. The advent of such systems emphasizes the critical role of employee competence, skills, knowledge and experience in an organization. This paper presents the salient features of knowledge‐based computing systems in modern office environments. In particular, I consider topics related to critiquing consultation systems and the possibilities of re‐using knowledge bases for training. The paper also discusses possible consequences, benefits, problems and other important issues in the area.

The primary objective of this research is to investigate the impact of task complexity on users’ performance in the context of using computer‐based technologies with auditing tasks. A laboratory experiment was performed with 121 undergraduate auditing students. The task complexity is defined in form of structured and unstructured tasks. The dependent variables were accuracy in solving problem and certainty of the correctness of solution. The results suggested that the effect of task complexity on accuracy in solving problems was significant for structured tasks. In addition, the significant effect was also found on certainty of the correctness of solution for structured tasks. The findings suggest the strategy for improving user performance in solving problem and certainty of the correctness of solutions toward a given task by giving users a structured task to perform. In addition, the findings also suggest the need for research on how to benefit from using computer‐based technologies in the context of solving structured task.