Enabling situated knowledge management for complex instruments by real-time reconstruction of surface coordinate system on a mobile device

We have developed an approach to implementing a system for managing situated knowledge for complex instruments. Our aim is to develop a system that guides a user through the steps for operating complex scientific instruments. A user manual is often inadequate support for a community of users, so direct communication with an expert is often required. One reason for this is that not all of the author’s expert knowledge was included in the manual, thus limiting the contents to explicit knowledge. This is a main concern of modern knowledge management practitioners who are attempting to design systems that consider both explicit and tacit knowledge. The key is to distribute explicit knowledge through interaction with the real world so that users can develop tacit knowledge as well as acquire explicit knowledge. We describe technical difficulties related to referencing the real world, which is required for interaction, and describe a novel approach to building a low-cost three-dimensional pointer for obtaining the required knowledge, which constitutes our preliminary result.     

Cooperation between expert knowledge and data mining discovered knowledge: Lessons learned

Expert systems are built from knowledge traditionally elicited from the human expert. It is precisely knowledge elicitation from the expert that is the bottleneck in expert system construction. On the other hand, a data mining system, which automatically extracts knowledge, needs expert guidance on the successive decisions to be made in each of the system phases. In this context, expert knowledge and data mining discovered knowledge can cooperate, maximizing their individual capabilities: data mining discovered knowledge can be used as a complementary source of knowledge for the expert system, whereas expert knowledge can be used to guide the data mining process. This article summarizes different examples of systems where there is cooperation between expert knowledge and data mining discovered knowledge and reports our experience of such cooperation gathered from a medical diagnosis project called Intelligent Interpretation of Isokinetics Data, which we developed. From that experience, a series of lessons were learned throughout project development. Some of these lessons are generally applicable and others pertain exclusively to certain project types.     

Using programming expertise for controlling software synthesis

Achieving efficient software implementations requires a great deal of knowledge, intelligence, and expertise on the part of programmers. One way to enhance software productivity is to incorporate the knowledge and skills of expert programmers into software synthesis systems to automate software development processes. Although many software synthesis systems have been developed, automatic control of synthesis remains a difficult problem. Understanding the role of expertise in software synthesis, and making it more explicit, can help us not only to gain autonomy in controlling the synthesis processes but also to better justify the design, implementations, selection of data structures or algorithms employed in constructing code. Our project aims at making synthesis as autonomous as possible by advances in intelligent control mechanisms to reduce user interaction in the synthesizer. In our earlier work, a blackboard control framework for controlling synthesis processes was introduced. This paper describes how the control framework language was designed and how knowledge in the knowledge bases of the framework was acquired and constructed. We present an example that shows how programming expertise can be used to increase the degree of autonomy in synthesis control, in particular by automating the selection of an appropriate data structure implementation.     

On two AI traditions

To understand the role of expert systems as a medium for transferring knowledge and skills within organisations requires an understanding of the nature of expertise within working life contexts. Central to this issue of transfer is the debate on the nature of tacit/implicit knowledge and the problem of formalising it in explicit form. This paper considers the British approach to the development of knowledge-based systems, which is regarded as being predominantly rationalistic, and compares it with the Scandinavian approach, which is regarded as being predominantly humanistic. The paper proposes that crucial to the debate on the transfer of knowledge and skills is the development of expert systems as communications media which aims at enhancing and sharing the knowledge and skills of users. A human-centred approach for the future is proposed which brings the above two traditions together thereby providing a developmental framework for knowledge based systems.     

Multidisciplinary modelling of biomedical systems

This paper describes general principles and example results of a new software tool being developed for physiologically-based modelling of biomedical systems within a multidisciplinary framework. The aim is to overcome some limitations of currently available software designed either for general purpose or for highly specialised modelling applications. In fact, general purpose tools usually impose explicit coding of mathematical model equations or non-intuitive system representations, whereas specialised software use domain-specific notations that allow efficient and convenient model building only for special classes of systems. The aim of the present study is to pursue intuitive representation of various, possibly interacting, types of biological systems described as interconnected physical components, such as mass and energy storage elements, active and passive transport or biochemical transformations. The presented software generates automatically the mathematical model equations that can be coded in different formats. This allows interoperability with other existing software, e.g. for numerical simulation, symbolic analysis or text processing. A multi-domain structural language has been defined for an intuitive, hierarchical and self-explanatory specification of physiological models. The proposed strategies may become useful for dissemination and integration of multidisciplinary modelling knowledge.     

A meta-model for knowledge configuration management to support collaborative engineering

This paper focuses on industrial design and simulation processes especially in automotive and aerospace areas. Designers use business models (called expert models) such as CAD (computed aided design) and CAE (computed aided engineering) models to optimize and streamline the engineering process. Each expert model contains information such as parameters, expert rules, mathematic relations (parametric models, for example) which are shared by several users and in several different domains (mechanical, thermal, acoustic, fluid, etc.). This information is exploited at the same time in a concurrent engineering context. It is the basis of an imperfect collaboration process due to the fact that existing tools do not manage encapsulated information well and are unable to ensure that parameters and rules are consistent (same value of parameters for example) throughout different heterogeneous expert models. In this context, we propose an approach to manage knowledge using configurations synchronized with expert models which enable designers to use parameters consistently in a collaborative context. Our approach is called KCModel (knowledge configuration model): it allows acquisition, traceability, re-use and consistency of explicit knowledge used in configuration.     

Expert systems and behavioral decision research

This paper reviews a part of the literature on behavioral decision research (policy capturing, psychophysics of numerical judgments and cognitive illusions) and examines implication for knowledge elicitation in expert systems. The literature on policy capturing demonstrates that simple and compact numerical models of expert knowledge can be built, but that experts are poor in verbalizing the knowledge expressed in them. The psychophysical literature indicates that numerical encoding of expert knowledge may be difficult and biased, but that it has definitive advantages over qualitative elicitation schemes: Numerical encoding forces hard throught, encourages precision, and allows to access a substantial computational apparatus. The literature on cognitive illusions suggests that the expert knowledge one elicits may be an illusion. The review concludes by recommending to use numerical judgments and explicit models by experts where possible, and to decompose the elicitation task in order to avoid cognitive illusions.     

A software engineering methodology for rule-based systems

Current expert systems are typically difficult to change once they are built. The authors introduce a method for developing more easily maintainable rule-based expert systems, which is based on dividing the rules into groups and focusing attention on those facts that carry information between rules in different groups. They describe a new algorithm for grouping the rules of a knowledge base automatically and a notation set of software tools for the proposed method. The approach is supported by a study of the connectivity of rules and facts in rule-based systems; it is found that they indeed have the latent structure necessary for the programming methodology. Recent experimental results also support the approach. In contrast to the homogeneous way in which the facts of a rule-based system are usually viewed, this approach shows that certain facts are more important than others with regard to future modifications of the rules     

Towards knowledge-based infolog specifications A case study of information engineering

A radically new approach to computing, the so-called knowledge-based information processing is achieving striking success in supporting activities needing logical power, human judgement, reasoning and expertise. It offers universal applicability for problem-solving, in particular in more complex tasks than those presently handled by computer systems. The current state of the art and future prospects for the development of a computer system which either performs expert tasks automatically or is used interactively by experts to increase their productivity are reviewed. The weak points we need to look at, namely the lack of guidelines for building such systems, and some dead ends are indicated. Some new results are expected by applying this key technology to our case study, the construction of a Systems Specification Support System (S4). Its knowledge base written in PROLOG captures and encodes human expertise about the INFOLOG model and INFOLOG specifications, which is made available via consultation to formulate specifications and, possibly, advice. The architecture of the knowledge base is presented by discussing its abstraction levels. This investigation provides also a methodology in structuring a systems analyst's knowledge about an application. This means how to find out the main kinds of objects, including their relationships, in some problem domain.