The CommonKADS Methodology
Andre Valente and John Kingston
Course Description
When knowledge is acquired for the construction of a knowledge based
system (KBS), it must be represented and structured in such a way that
it can be used to analyse existing structures and approaches, and to
produce a coherent design for a knowledge based system. The separation
of analysis and design is a key element in both accurate modelling of
expert knowledge in an implementation-independent manner (allowing the
possibility of reusing knowledge models between applications) and in
making well-justified decisions about the best implementation approach
for a particular problem. The CommonKADS methodology provides a
principled approach to representing, organising and transforming
acquired knowledge; in other words, it is an enabling technology to
support and guide the construction of knowledge based systems.
The goal of the tutorial is to introduce the audience to a disciplined
approach to developing knowledge based systems based on
the CommonKADS methodology. The course is intended for
knowledge engineers and other technical specialists interested
in methods for developing knowledge based systems. On
completion, the audience will understand the benefits of modelling
knowledge as an intermediate step between knowledge
acquisition and implementation; know how to produce a set of
knowledge models by following the CommonKADS methodology;
be able to identify the task types of potential and actual KBS
applications; and understand the basis of good design decisions.
An awareness of popular knowledge representation and inferencing
techniques is expected. Some knowledge of KBS development tools and a
small amount of programming experience is also desirable.
About the Lecturers
Andre Valente
is a computer scientist at the USC Information Sciences Institute. Prior
to his work at ISI he participated, at the University of Amsterdam, in
the design of the Common KADS methodology and the CommonKADS Library in
particular. He has also worked for major industrial corporations,
performing applied research and development in knowledge engineering. He
holds a Ph.D. (Computer Science) from the University of Amsterdam
(1995). His research interests are knowledge engineering, knowledge
acquisition, and planning.
John Kingston
is a Senior Computer Scientist within the Knowledge Engineering Methods
Group at the Artificial Intelligence Applications Institute (AIAI),
University of Edinburgh. He has developed several commercial knowledge
based systems, presented a range of commercial training courses, and
provided consultancy on knowledge based systems in the UK, Europe and
the USA. He also publishes frequently, and was awarded first prize for
Best Technical Paper at the BCS Expert Systems '93 conference for a
paper on applying the CommonKADS methodology.
higuchi@etl.go.jp
Last modified: Thu Feb 20 14:00:43 JST 1997