Paul Compton's home pageI am am an emeritus professor in the School of Computer Science and Engineering (CSE) at The University of New South Wales.
Most of my research for the last 30 years has been around the idea of building systems incrementally using a learning or knowledge acquisition stragegy known as Ripple-Down Rules.
Professor Paul Compton
School of Computer Science and Engineering
The University of New South Wales
Sydney 2052 Australia
mob 61 425375279
Rm 303 Computer Science Building (map ref k17)
Ripple-Down Rules summaryRipple-Down Rules (RDR) is a strategy of bulding systems incrementally while they are already in use. When a system does not deal with a case or situation correctly a change is made in such a way that the previous competence of the system is not degraded. The change is made simply and rapidly and the difficulty of making a change should not increase as the system develops. RDR can be categorised as a type of apprentice learning
Various industrial RDR systems have been developed for a range of applications. There have have been research proofs for a wide range of applications including: various types of classification problem, configuration or parameter tuning, text-processing, conversational agents, image processing, heuristic search, tuning genetic algorithms and multi-agent environments. There have also been machine-learning versions of RDR.
A number of researchers across various universities have been involved in this research. It seems current research is aimed at:
- extending the range of possible applications of RDR, particular with text processing and conversational agents
- further extensions to RDR algorithms
- ways of combining machine learning and RDR, in particular RDR systems that can recognise when a case is outside their competence
 Compton, P. and Kang B.H (2021) Ripple-Down Rules: the Alternative to Machine Learning, CRC Press (Taylor and Francis)
The central part of this book is three chapters providing detailed
worked examples for various types of RDR. The reason for these
chapters is that although RDR are very simple, people often assume
they must be doing something more complex. The examples were
developed using software available here.
This software is written in VBA and Excel, to make it easy for users
to play with RDR using their own data; it is fully featured, but not
appropriate for industrial application. The book also
discusses machine learning because of current interest in
learning. Machine learning algorithms are magical, but often
the data is simply not good enough for the magic to work. RDR
provides an alternative approach that avoids the need for a large
well-curated data set.
 Compton, P. (2013). "Situated cognition and knowledge acquisition research." International Journal of Human-Computer Studies 71: 184-190.
P., L. Peters, T. Lavers and Y. Kim (2011). “Experience
with long-term knowledge acquisition.” Proceedings of
the Sixth International Conference on Knowledge Capture, KCAP
2011, Banff, ACM. 49-56
Compton, P. and R.
Jansen (1990). "A Philosophical Basis for Knowledge
Acquisition." Knowledge Acquisition 2: 241-257.
G., P. Compton, R. Malor, A. Srinivasan and L.
Lazarus (1993). "PEIRS: a pathologist maintained expert system
for the interpretation of chemical pathology reports." Pathology
was the first RDR system in industrial (clinical use). Although a
large system with 2000 rules, it was limited by using single
 Ho, V. H., P. Compton, B. Benatallah, J. Vayssière, L. Menzel and H. Vogler (2009). “An Incremental Knowledge Acquisition Method for Improving Duplicate Invoice Detection.” Proceedings of the 25th IEEE International Conference on Data Engineering, ICDE 2009, Shanghai, IEEE. 1415-1418
This is one of a number of papers, where an RDR system is used to
correct the errors of another system, functioning as a sort of
wrapper. If the underlying system makes an error an RDR rule
is written. The conclusion from the underlying system is the
default conclusion, but is overridden if the RDR system provides a
different conclusion for the case. The underlying system might
have been developed by machine learning but potentially can be any
system that processes a case
page last updated 22/03/2021