Knowledge Acquisition最新文献

Over the past few years a certain amount of research has been done on ways of configuring problem-solving methods from smaller-grained components. Although frameworks for method configuration have been explored in previous studies, ways of developing and maintaining a library of reusable knowledge have not been fully integrated into the frameworks. The purpose of this paper is to provide a view of bridging the architecture of component assembling and the knowledge contents, on the basis of our experiences in the area of scheduling problems. First, existing component-oriented approaches are briefly reviewed. We then introduce the concept of a computer-aided knowledge engineering (CAKE) environment, which consists of not only processes for constructing knowledge systems, but also processes for developing knowledge libraries to be reused for the prospective systems. The content issues in a class of scheduling problems are pursued in terms of the representation primitives of the task and method, the task-specific components to be configured into problem-solving methods, and the process of obtaining those components. Taking account of the knowledge contents, we explore the architectural issues related to our development environment, and give running examples for the task analysis, component retrieval and component configuration. Finally, we compare related studies on reuse-oriented development environments.

Knowledge modelling is undoubtedly a major problem in knowledge acquisition. Drawing from industrial case studies that have been carried out, the paper lists some key problems which still dog knowledge modelling. Next, it critically reviews current knowledge modelling techniques and tools and concludes that these real knowledge acquisition issues are not tackled by them. We consider the spelling out of these problems and the fact that they are not addressed by current tools and techniques to be a major contribution of this paper. The paper strongly argues for knowledge modelling to be domain-driven, i.e. driven by the nature of the domain being modelled. The key argument in this paper is that ignoring the nature or characterization of the domain inevitably results in knowledge imposition rather than knowledge acquisition as domains get shoe-horned into some (current) set of models, representations and tools. After examining the nature of domains, the paper proceeds to outline an emerging hypothesis for knowledge modelling. It concludes with a specification of a tool suite for addressing the issues identified in this paper.

Laddering is a structured questioning technique derived from the repertory grid technique, enabling a hierarchy of concepts to be established. Previous empirical studies have demonstrated its utility for knowledge elicitation in two classificatory domains. In this paper, three experimental studies of laddering are described. In experiment 1, the technique was used in another classificatory domain, metallic corrosion, and the effects of repeated exposure to the technique and feedback, in the form of Pseudo-English Production Rules, were investigated. These variables had no effect on the productivity of the technique. Experiment 2 compared the laddering technique with three other techniques in a medical diagnostic domain. As in previous studies laddering was found to be the most productive technique despite the change in the type of domain about which knowledge was elicited. In experiment 3, the preferences of subjects interviewed using two versions of the laddering technique, "textual" and "graphical", were compared with those obtained when the subjects were interviewed using a computerised laddering tool. Although the "gain" obtained in the three conditions varied, the group of subjects did not prefer one type of laddering to another. The laddering tool used in the experiment was designed as one tool in an integrated Knowledge Engineering Workbench (KEW). The potential for synergy between the laddering tool and other knowledge acquisition techniques implemented within KEW is explored. Guidance and advice concerning the appropriate context to employ laddering within the knowledge acquisition process is provided.

Constructive induction is a means of improving classification accuracy in difficult domains by transforming a difficult domain into a form amenable to standard induction techniques by constructing new features. When performing constructive induction, though, a learning system faces a combinatorial explosion of potential features to construct, but only a small fraction of these will prove to be useful. The challenge lies in identifying enough of these useful constructed features to achieve sufficient accuracy while examining as little as possible of the space of potential constructed features.

This paper presents how apprenticeship techniques (Mitchell et al., 1985; Hall, 1988; Wilkins, 1988; Tecuci & Kodratoff, 1990) can be used to guide the feature construction process by focusing attention on weak areas of a learned knowledge base. The method used is to run a splitting algorithm (such as CART, PLS1, or C4.5) to build a knowledge base, employ apprenticeship techniques to detect and localize knowledge base deficiencies, use this information to construct new features, and then repeat the cycle as necessary (i.e. until a desired accuracy is reached). We show how this method improves accuracy on a range of classification problems and discuss why the combination of apprenticeship as a knowledge acquisition method and constructive induction as a machine learning method overcomes key weaknesses of each of these methods used separately.

How can people who are not trained in data analysis discover knowledge from a database of attribute-valued data? I address this question by presenting a man-machine approach to knowledge discovery called Attribute Focusing and its application to software production process control. Attribute Focusing utilizes an automatic filter to focus attention on that small part of a large amount of data which is interesting. A person studies that part in a manner which leads him to discover knowledge about the physical situation to which the data pertain. Specifically, the paper describes:

1. A model of interestingness of data based on the magnitude of data values, the association of data values and basic knowledge of the limits of human processing.

2. The use of that model of interestingness by people to discover knowledge.

3. The application of the Attribute Focusing approach to diagnose and correct the software production process.

Based on the results that have been observed, the paper concludes that man-machine approaches to knowledge discovery should be emphasized much more than has been in the past, and that Attribute Focusing is a powerful, practical approach to such discovery.

A primitives-based generic approach to perform knowledge acquisition is proposed. The approach is generic in the sense that it enables the user to construct domain-specific knowledge acquisition tools for specific tasks. It is also primitives-based since the construction of specific knowledge acquisition tools is based on a primitives kernel that contains problem solving primitives, acquisition primitives, interaction primitives, representation schemas and knowledge verification primitives. A generic knowledge acquisition shell is developed on the basis of this approach. It facilitates the development of proper specific knowledge acquisition tools for specific tasks through the construction of experimental knowledge acquisition tools. Furthermore, the shell is developed as an open architecture (i.e. separating the generic knowledge acquisition structure from specific knowledge acquisition structures) so that further enhancement can be done readily.