KNOWLEDGE-BASED SYSTEMS DEVELOPMENT

As more organizations start to consider the benefits of knowledge-based applications, they also experience the growth pains of learning how to manage knowledge-based systems projects. This article provides a context for understanding the difficulties unique to a knowledge-based systems development project and describes one methodology that offers solutions to many of the management problems confronting those responsible for knowledge-based systems development and implementation efforts.     

Recent development of knowledge-based systems,methods and tools for One-of-a-Kind Production

In recent years, product knowledge has played increasingly significant roles in new product development process especially in the development of One-of-a-Kind products. Although knowledge-based systems (KBSs) have been proposed to support product development activities and new knowledge modelling methodologies have been developed, they are still far from complete. This area has become attractive to many researchers and as a result, many new knowledge-based systems, methods and tools have been developed. However, to the best of our knowledge, knowledge-based systems for product development have not been systematically reviewed, compared and summarized. This paper provides a comprehensive review on the recent development of KBS, methods and tools in supporting rapid product development. In the paper, the relevant technologies for modelling, managing and representing knowledge are investigated and reviewed systematically for better understanding their characteristics. The focus is placed on knowledge-based systems that support product development, and how product knowledge is identified, captured, represented and reused during the processes of One-of-a-Kind product development. The limitations and the future trend of KBS are presented in terms of how they can help One-of-a-Kind Production (OKP) companies.     

A review of conventional and knowledge based systems for machining price quotation

Price estimation for the preparation of quotations is critical process for companies that have to struggle to get orders by offering competitive pricing. For machining companies, said process is complex because of the large amount of variation that can occur. In this case, a manufacturing expert is generally in charge of this task. However, manufacturing experts have several other important tasks to which they must attend. The use of software systems that automate the estimation of costs and prices is common, knowledge-based systems are one of the primary alternatives for two fundamental reasons: they behave the same way an expert would and they save and maintain all knowledge within the company regardless of who has worked on them. This paper covers the principal cost and price estimation methods and reviews the knowledge-based systems that have been implemented in the area of machined part manufacturing. Recommendations as to how future knowledge-based systems for the estimation of this type of pricing should develop are also included.     

Cognitive,collaborative, conceptual and creative — Four characteristics of the next generation of knowledge-based CAD systems: A study in biologically inspired design

We envision that the next generation of knowledge-based CAD systems will be characterized by four features: they will be based on cognitive accounts of design, and they will support collaborative design, conceptual design, and creative design. In this paper, we first analyze these four dimensions of CAD. We then report on a study in the design, development and deployment of a knowledge-based CAD system for supporting biologically inspired design that illustrates these four characteristics. This system, called DANE for Design by Analogy to Nature Engine, provides access to functional models of biological systems. Initial results from in situ deployment of DANE in a senior-level interdisciplinary class on biologically inspired design indicates its usefulness in helping designers conceptualize design of complex systems, thus promising enough to motivate continued work on knowledge-based CAD for biologically inspired design. More importantly from our perspective, DANE illustrates how cognitive studies of design can inform the development of CAD systems for collaborative, conceptual, and creative design, help assess their use in practice, and provide new insights into human interaction with knowledge-based CAD systems.     

Applying rule-base anomalies to KADS inference structures

The literature on validation and verification of knowledge-based systems contains a catalogue of anomalies for knowledge-based systems, such as redundant, contradictory or deficient knowledge. Detecting such anomalies is a method for verifying knowledge-based systems. Unfortunately, the traditional formulation of the anomalies in the literature is very specific to a rule-based knowledge representation, which greatly restricts their applicability. In this paper, we show how the traditional anomalies can be reinterpreted in terms of conceptual models (in particular KADS inference structures). For this purpose, we present a formalisation of KADS inference structures which enables us to apply the traditional rule-base anomalies to these inference structures. This greatly improves the usefulness of the anomalies, since they can now be applied to a much wider class of knowledge-based systems. Besides this reformulation and wider applicability of the traditional anomalies, further contributions of this paper are a novel formalisation of KADS inference structures and a number of improvements to the existing formalisation of the traditional anomalies.     

Verifying ontological commitment in knowledge-based systems

An ontology defines the terminology of a domain of knowledge: the concepts that constitute the domain, and the relationships between those concepts. In order for two or more knowledge-based systems to interoperate—for example, by exchanging knowledge, or collaborating as agents in a co-operative problem-solving process—they must commit to the definitions in a common ontology. Verifying such commitment is therefore a prerequisite for reliable knowledge-based system interoperability. This article shows how existing knowledge base verification techniques can be applied to verify the commitment of a knowledge-based system to a given ontology. The method takes account of the fact that an ontology will typically be expressed using a different knowledge representation language to the knowledge base, by incorporating translation into the verification procedure. While the representation languages used are specific to a particular project, their features are general and the method has broad applicability.     

Rethinking competence systems for knowledge-based organizations

Existing competence systems are based on a rationalistic view of competence. While these competence systems might work in job-based organizations, we argue that in more dynamic settings, such as in knowledge-based organizations, the interest-informed actions that capture the emergent competencies of tomorrow require different types of information technology support. The main objective of this paper is to elaborate on the possibilities and implications of using interest-activated technology as a design rationale for competence systems. This paper is based on an action case study of an implemented interest-activated Intranet recommender system prototype at Volvo Information Technology AB in Gothenburg, Sweden. On the basis of how organizational members used this prototype to find information they were interested in, our research team was able to inquire into how personal interest, embodied in information-seeking activities, could be a means for identifying competence. Building on the relation between personal interest and competence, we discuss competence systems design and spell out explicit implications for managerial practice in knowledge-based organizations.     

Statistical language approach translates into success

Machine translation currently translates text from one language into another. However, work is under way on speech-to-speech translation. There are two kinds of machine translation: knowledge-based and statistical. Knowledge-based systems translate documents by converting words and grammar directly from one language into another. Rather than using the knowledge-based system's direct word-by-word translation techniques, statistical approaches translate documents by statistically analyzing entire phrases and, over time, "learning" how various languages work. The article examines the pros and cons of both systems, and predicts that statistical methods will become more popular, however the future will involve combining statistical and knowledge-based methods to create better systems.     

The role of assumptions in knowledge engineering

Problem-solving methods are means of describing the inference process of knowledge-based systems. In recent years, a number of these problem-solving methods have been identified that can be reused for building new systems. However, problem-solving methods require specific types of domain knowledge and introduce specific restrictions on the tasks that can be solved by them. These requirements and restrictions are assumptions that play a key role in the reuse of problem-solving methods, in the acquisition of domain knowledge, and in the definition of the problem that can be tackled by knowledge-based systems. In this paper we discuss the different roles assumptions play in the development of knowledge-based systems and provide a survey of assumptions used in diagnostic problem solving. We show how such assumptions introduce targets and bias for goal-driven machine learning and knowledge discovery techniques. © 1998 John Wiley & Sons, Inc.     

The development and utilization of the case-based help-desk support system HOMER

Current case-based reasoning (CBR) process models present CBR as a low-maintenance AI-technology and do not take the processes that have to be enacted during system development and utilization into account. Since a CBR system can only be useful if it is integrated into an organizational structure and used by more than one user, processes for continuous knowledge acquisition, utilization and maintenance have to be put in place. In this paper the shortcomings of classical CBR process models are analyzed, and, based on the experiences made during the development of the case-based help-desk support system HOMER, the managerial, organizational and technical processes related to the development and utilization of CBR systems are described.     

Knowledge capitalisation through case bases and knowledge engineering for road safety analysis

New generation knowledge-based systems should be fully integrated into their environment, by exploiting existing information sources, and should be flexible and easily extensible. This article describes the architecture of an organisational memory (OM) for road safety analysis. Starting from the design of a knowledge-based system, we show how we address knowledge capitalisation issues through the building of an OM. We present its main components and describe how knowledge engineering techniques can be exploited to build and enrich it. We then describe the major task that exploits the OM as decision support for site analysis. We also explain how domain knowledge can be exploited and capitalised using case-based reasoning and collaborative work.     

Toward an engineering science of knowledge-based design

In this paper, we present a framework for organizing, evaluating, and developing knowledge-based models of the design process. We argue that evaluation of a design process model can be carried out from three usefully distinguished perspectives: the knowledge it embodies; the functionality of the design process, from a problem-solving viewpoint; and the implementation of the design process as an actual program. This paper focuses on the first two perspective. We systematically introduce a set of basic functional components, and show how existing approaches or systems can be viewed as configurations of these components, in which domain knowledge has been incorporated. As we lay out this framework, we illustrate it in a simple way by using it to describe knowledge-based house floorplanners. We then complete our presentation by analysing a more complex knowledge-based system (DONTE) that designs circuits.     

Intelligent control

A framework for intelligent control is presented and different approaches to intelligent control are reviewed in light of this framework. The topics discussed include knowledge-based control, fuzzy control, neural networks, fault diagnosis, single loop control and distributed control. The key ideas behind these approaches are outlined and it is indicated how they may be used to make control systems with significantly improved capabilities.     

The role of the industrial engineer in developing expert systems

The most popular area of Artificial Intelligence application today is in expert systems. This paper contains a discussion of expert systems, otherwise known as knowledge-based systems and knowledge systems. The principal components of an expert system, and the evolution of expert systems are presented. The suitability of a task to an expert system is proposed. When a task is suitable for an expert system application, the system must be developed by a knowledge engineer. The methodology that the knowledge engineer must go through to develop an expert system is demostrated. Industrial engineers have formal training in many areas which can be useful when assumming the role of knowledge engineer. These areas of industrial engineering and how they are beneficial is discussed. What the future may hold in store is also pondered.     

Functional requirements for computer-based design support systems,derived from experimental studies

In this paper, we examine the functions that a computational system for knowledge-based design support may undertake. We present a set of functions that bring together previous approaches and allow us to locate the work that has been developed to enhance these systems concerning those functions. We describe some new proposals, based on experimental research work, for improving some of these functions so that they can be taken into account in the development of design support systems to help the designer or group of designers reach a suitable solution in a more effective way.     

Tutoring bishop-pawn endgames: An experiment in using knowledge-based chess as a domain for intelligent tutoring

Most research in computer chess has focused on creating an excellent chess player, with relatively little concern given to modeling how humans play chess. The research reported in this article is aimed at investigating knowledge-based chess in the context of building a prototype chess tutor, UMRAO, which helps students learn how to play bishop-pawn endgames. In tutoring it is essential to take a knowledge-based approach, since students must learn how to manipulate strategic concepts, not how to carry out large-scale lookahead searches.UMRAO uses an extension of Michie's advice language to represent expert and novice chess plans. For any given endgame, the system is able to compile the plans into a strategy graph, which elaborates strategies (both well formed and ill formed) that students might use as they solve the endgame problem. A strategy graph can be compiled off-line, where real-time performance is not important. Later, during tutoring, the strategy graph can be accessed quickly in order to understand a student's moves in terms of his or her strategies. With such understanding, UMRAO is able to provide appropriate knowledge-based feedback to the student. Anderson et al. have called this tutoring paradigm model tracing, but in the chess domain model tracing can be used without the need for immediate feedback that Anderson has required in his more complex abstract problem-solving domains. The chess domain thus allows experimentation with a variety of tutoring styles that range from immediate feedback to optional feedback, from strict tutor control of the feedback to student initiative in the choice of feedback. This points out UMRAO's most promising contribution: re-establishing chess as a vehicle for research in other areas of artificial intelligence, in this case intelligent tutoring systems. UMRAO also makes technical contributions to knowledge-based chess and to intelligent tutoring as well.     

Acquisition of large lexicons for practical knowledge-based MT

Although knowledge-based MT systems have the potential to achieve high translation accuracy, each successful application system requires a large amount of hand-coded lexical knowledge. Systems like KBMT-89 and its descendents have demonstrated how knowledge-based translation can produce good results in technical domains with tractable domain semantics. Nevertheless, the magnitude of the development task for large-scale applications with tens of thousands of domain concepts precludes a purely hand-crafted approach. The current challenge for the next generation of knowledge-based MT systems is to utilize on-line textual resources and corpus analysis software in order to automate the most laborious aspects of the knowledge acquisition process. This partial automation can in turn maximize the productivity of human knowledge engineers and help to make large-scale applications of knowledge-based MT an viable approach. In this paper we discuss the corpus-based knowledge acquisition methodology used in KANT, a knowledge-based translation system for multilingual document production. This methodology can be generalized beyond the KANT interlingua approach for use with any system that requires similar kinds of knowledge.     

A survey of languages for specifying dynamics: a knowledgeengineering perspective

A number of formal specification languages for knowledge-based systems has been developed. Characteristics for knowledge-based systems are a complex knowledge base and an inference engine which uses this knowledge to solve a given problem. Specification languages for knowledge-based systems have to cover both aspects. They have to provide the means to specify a complex and large amount of knowledge and they have to provide the means to specify the dynamic reasoning behavior of a knowledge-based system. We focus on the second aspect. For this purpose, we survey existing approaches for specifying dynamic behavior in related areas of research. In fact, we have taken approaches for the specification of information systems (Language for Conceptual Modeling and TROLL), approaches for the specification of database updates and logic programming (Transaction Logic and Dynamic Database Logic) and the generic specification framework of abstract state machines