In this article we present a coherent framework for modelling reasoning processes in knowledge based systems. The aim of the framework is to integrate different lines of research and in particular, though not exclusively, the KADS approach and the Components of Expertise framework. We are especially concerned with enhanced facilities for domain modelling and with the notion of problem solving method. The resulting modelling framework, called the CommonKADS modelling framework, fits into a comprehensive methodology, called CommonKADS, that covers all aspects of knowledge based applications. In this article we first present a set of principles on which our modelling framework in founded. These are derived from a careful study of the different approaches. We then describe the modelling framework itself, illustrating it with an example. We also discuss various approaches to building models for a particular application using this framework.

Towards a unification of knowledge modelling approaches

Large amounts of knowledge are available in many knowledge bases for a variety of applications. This knowledge is however usually application specific, and thus not reusable. This paper discusses the problem of making knowledge shareable over applications and reusing it. Three principles are formulated that can form a basis for a methodology for designing sharable knowledge bases. The separation of domain and control knowledge, the explication of meta-models of the domain knowledge (ontologies), and the distinction between ontologies that submit to different classes of assumptions commitments are described as ways of achieving shareable and reusable knowledge bases.

Conceptual Modelling of Large Reusable Knowledge Bases

Developing software is like talking to Eskimos about snow

This paper consists of four condensed position papers given as an initial statement by five members of a panel session on Knowledge Acquisition and Machine Learning. This panel session was instigated by Yves Kodratoff in order to cover an issue that was raised in 1987 at the first workshop on knowledge acqusition for knowledge-based systems held at Reading (UK). It was at this meeting that those involved in machine learning and knowledge acquisition came to realise that a cultural gulf existed between two groups of people; people who seemed to be working in the same area. It has been the objective over the years at the meetings of both the European Working Sessions on Learning (EWSL) and the European Knowledge Acquisition Workshops (EKAW) to bridge the gap between these two cultures. This panel is intended to be a further step towards achieving this objective.

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Four stances on knowledge acquisition and machine learning

In this paper, we define a formalization of goals based on set algebra and we show how structural properties of domain relations and goals can be used to select problem solving methods in a library indexed by methods functionality. Our work is motivated by the need to reuse model components for knowledge engineering. We show how to construct a compound method from a goal specification and an abstract description of the domain knowledge. Finally we show that by modifying the required functionality, the same domain knowledge can be used for a different goal.

A Systematic Approach to the Functionality of Problem-Solving Methods

Each task that anyone, man or machine, might want to perform imposes some set of computational requirements on the performer. This is obvious—how could it be otherwise? But if you ask anyone what the computational requirements are that a class of tasks imposes, you surely won’t get a very good answer. As application programmers, we can expose the computational requirements of the task we just wrote a program to solve. But we typically do so by pointing to the program; this doesn’t provide much insight, either to ourselves or to others, into the requirements that other, similar tasks impose. From the perspective of one who simply wants an understanding of the computational requirements of a class of tasks, the application program over-commits.

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John McDermott

Papers

The World Would Be a Better Place if Non-Programmers Could Program