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.     

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.     

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     

ES技术在软件开发中的应用研究

专家系统（ＥＳ）的威力在于它的知识。该文主要阐述了ＥＳ作为新的先进软件技术表达软件程序中领域知识的思想，详细介绍了在实际的软件编程中用Ｃ／Ｃ＋＋实现合成的ＥＳ技术的方法，从而提高软件的智能化水平。文中列举了常规程序中不确定性知识的规则实现以及用Ｃ＋＋中的类构造框架结构的步骤。     

The bank of Scotland''s COMPASS—The future of bank lending?

This paper describes the background surrounding the need for risk analysis of commercial business within the domain of bank lending and of the development of an expert system for that task. Previous attempts at constructing expert systems in this area have either proved unsuccessful, software difficulties often being cited as the cause; or have stopped short of encapsulating all the relevant expertise. This paper considers the relevance of knowledge engineering to successful expert system construction. It reports on the development and structure of COMPASS, the Bank of Scotland's commercial lending adviser expert system which, by appropriate application of knowledge engineering, has succeeded in capturing and modelling the inherent risk of the Bank of Scotland's commercial lending process. The stages of its development are outlined; the knowledge elicitation process is described; knowledge articulation is examined from the perspective of the expert; the architecture of the system is explained; and the consultation procedure is described. In addition, reference is made to major attempts elsewhere to produce lending adviser expert systems; and the advantages; by-products, and long-term benefits of COMPASS are summarized.     

Performance assessment of intelligent distributed systems through software performance ontology engineering (SPOE)

In the computer science community there is a growing interest in the field of Ambient Intelligent Systems. This systems surround their human users with computing and networking technology unobtrusively embedded in their environment. This technology is aimed to provide the users with useful information and to take action to make the environment more convenient for them. As the number of users increases the resources that make Ambient Intelligence possible can be easily saturated making the system unstable and projecting an image of poor QoS to the users. The main goal of this paper is to provide the means for the Ambient Intelligent Systems to monitor themselves and take corrective action automatically if performance starts to drop. Our approach uses a Performance Ontology that structures the knowledge about Software Performance Engineering, and a reasoning engine that acts like an expert system with the Performance Ontology as its foundation. The case study at the end shows the applicability of the developed techniques.     

Classification Accuracy: Machine Learning vs. Explicit Knowledge Acquisition

This empirical study provides evidence that machine learning models can provide better classification accuracy than explicit knowledge acquisition techniques. The findings suggest that the main contribution of machine learning to expert systems is not just cost reduction, but rather the provision of tools for the development of better expert systems.     

A methodology for knowledge engineering using an interactive graphical tool for knowledge modelling

A basic feature of human nature is the propensity to construct boundaries which define territorial possession. Such assumed possessions are often jealously guarded by their owners, and a consequence of this primitive instinct is the emergence of subject specialists who exercise in-depth ‘lordship’ over their domain of expertise. For many years computer science has made attempts to relate to this phenomenon of expert intellectual property by developing mechanisms in software to emulate reasoning capability. Correspondingly this has resulted in the development of intelligent knowledge-based (or expert) systems, along with their attendant processes of knowledge elicitation, representation and exploitation. This paper attempts to define a context for knowledge engineering, the term being used to define spanning the void between domain expertise and the intelligent knowledge based system. It goes on to describe the systemic development of a particular solution to the knowledge engineering problem which is underpinned by a software environment called VEGAN (a Visual Editor for the Generation of Associative Networks). Many attempts have been made at bridging this gap, and VEGAN represents a significant aid to the knowledge engineering task, in the context of frame-based systems. Rather than attempt to create a unidirectional information path from expert to computer system (or knowledge engineer), VEGAN presents a common forum for discussion about, and exploration of, the expertise of the domain specialist. By doing so it helps the flow of information between the two parties. VEGAN represents an approach to a human-natured design of a software system which:

• 1.empathises with the ‘culpture’ of the expert,
• 2.provides a bridge between the expert and the computer system by shielfing the expert from the underlying complexity of the system,
• 3.aids the study of the organization of expertise, and thus induces further information.

     

Using Developer Activity Data to Enhance Awareness during Collaborative Software Development

Software development is a global activity unconstrained by the bounds of time and space. A major effect of this increasing scale and distribution is that the shared understanding that developers previously acquired by formal and informal face-to-face meetings is difficult to obtain. This paper proposes a shared awareness model that uses information gathered automatically from developer IDE interactions to make explicit orderings of tasks, artefacts and developers that are relevant to particular work contexts in collaborative, and potentially distributed, software development projects. The research findings suggest that such a model can be used to: identify entities (developers, tasks, artefacts) most associated with a particular work context in a software development project; identify relevance relationships amongst tasks, developers and artefacts e.g. which developers and artefacts are currently most relevant to a task or which developers have contributed to a task over time; and, can be used to identify potential bottlenecks in a project through a ‘social graph’ view. Furthermore, this awareness information is captured and provided as developers work in different locations and at different times.     

Developing maintainable expert systems using case-based reasoning

Abstract: Maintainability problems associated with traditional software systems are exacerbated in rule-based systems. The very nature of that approach — separation of control knowledge and data-driven execution — hampers maintenance. While there are widely accepted techniques for maintaining conventional software, the same is not true for rule-based systems. In most situations, both a knowledge engineer and a domain expert are necessary to update the rules of a rule-based system. This paper presents, first, an overview of the software engineering techniques and object-oriented methods used in maintaining rule-based systems. It then discusses alternate paradigms for expert system development. The benefits of using case-based reasoning (from the maintenance point of view) are illustrated through the implementation of a case-based scheduler. The main value of the scheduler is that its knowledge base can be modified by the expert without the assistance of a knowledge engineer. Since changes in application requirements can be given directly to the system by the expert, the effort of maintaining the knowledge base is greatly reduced.     

Knowledge map: An approach to knowledge acquisition in developing engineering expert systems

This paper addresses the critical issues of knowledge acquisition in developing knowledge-based expert systems for engineering tasks. First, it reviews the role of knowledge acquisition and its current practice in expert system development. Then, a new approach based on three stages of knowledge refinement is suggested to improve the process of knowledge acquisition. This approach, calledrule verification without rule construction, is proposed to allow knowledge engineers and domain experts to experience a more intimate and balanced role in developing intelligent systems. The communication tool developed for this concept is calledknowledge map, which provides a systematic way of indexing and quantifying a piece of knowledge in the problem space by defining important attributes as the axes of the map. This approach is demonstrated by constructing a twodimensional map for a knowledge-based engineering design system, IDRILL, which we are currently developing. Future expansions of this knowledge acquisition technique are summarized as the conclusions of this paper.This paper was presented in part at the 1986 ASME International Computers in Engineering Conference in Chicago, IL, and appeared in the proceedings of that conference.     

基于Multi Agent的图象理解

最近十多年来,具有专家系统外壳的图象理解软件的开发一直是一个重要的研究课题。然而,多数基于知识的图象理解系统或者局限于诸如分割之类的特定操作上,或者局限于像理解建筑物的航空图象这样特定的应用。由于这些系统的目标和知识结构的局限性,因而不能将它们推广到其它领域。此研究就是试图解决此问题：首先将Agent构造环境扩展为一个Muliti Agent的图象理解系统,而此系统是知识入口的开发工具,它为用户提供一个类似于专家系统外壳的界面。这样一方面能加速图象理解系统的开发,另一方面方便那些缺乏图象理解知识的人开发图象理解应用程序。