| As knowledge acquisition (KA) remains a bottleneck in the development of knowledge-based systems, methodologies and techniques are needed in both manual and automatic KA directions. This thesis produces results in three areas related to KA: (1) conceptual modelling of real-time expert systems; (2) concept learning from examples; and (3) case-based reasoning. In the area of conceptual modelling, we present a new methodology, ORTES, that analyses the acquired knowledge for building a real-time expert system and represents the analysed knowledge in an object-oriented formalism. ORTES tackles problems in both KA and object-oriented analysis (OOA). Most OOA methods are data-driven modelling approaches, in which analysis is mainly based on identifying and decomposing objects in the real-world, but ignores the issue of systematically specifying the system functionality. On the other hand, KA approaches usually center around modelling problem-solving strategies, but lack support for effectively connecting the system's functional and data components. ORTES is proposed to overcome these problems by providing guidelines for both object and task decomposition and by representing a system in terms of objects and their relationships. To support task decomposition, ORTES provides a generic task structure for real-time monitoring and control. To support object decomposition, ORTES supplies a classification scheme for identifying and organising the objects involved in a real-time control system. Methods for specifying objects and their relationships in an object-oriented context are also provided. To illustrate the modelling method, we present an application of ORTES to conceptual modelling of an expert system for monitoring and control of a water supply system.; Another way to overcome the knowledge acquisition bottleneck is to conduct automatic KA using machine learning. We present a new inductive learning system, ELEM2, that generates rules based on attribute-value pair selection and incorporates several new ideas to improve the predictive accuracy of induced rules. A heuristic function that represents the degree of relevance of an attribute-value pair is provided to evaluate attribute-value pairs. A rule of quality measure that is used for post-pruning generated rules is proposed based on a discussion among a number of alternatives.; Case-based reasoning (CBR) is another problem-solving and learning method that solves a new problem by recalling and reusing specific knowledge obtained from past experience. Due to the complementary properties of CBR and rule induction, integration of the two techniques appears advantageous. We propose a new integrated method, ELEM2-CBR, that makes use of a hybrid representation of rules and cases to solve both classification and numeric prediction problems. (Abstract shortened by UMI.)... |
