Paul Compton's home page

I 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)

RDR starter papers

RDR software

industrial RDR systems and impact

RDR publications

Ripple-Down Rules summary

Ripple-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

Starter Papers

These following material should provide some introduction to RDR, while the book[1] attempts to cover all known RDR.   There a numerous other RDR papers by other authors as well as my coworkers and myself.  In particular in 2009 Debbie Richards published a paper covering 20 years of RDR research at this stage

[1]     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.

[2]     Compton, P. (2013). "Situated cognition and knowledge acquisition research." International Journal of Human-Computer Studies 71: 184-190.

This was one of 11 invited papers for a special issue of the journal to commemorate 25 years of knowledge acquisition research.  It summarises why RDR is different from other knowledge acquisition strategies, and presents some data on RDR performance.

[3]     Compton, 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

This paper presents more detailed data on some industry RDR systems. A version of this paper with minor corrections is available on the PKS web site

[4]     Compton, P. and R. Jansen (1990). "A Philosophical Basis for Knowledge Acquisition." Knowledge Acquisition 2: 241-257.

This was the second RDR paper, but is the key paper for establishing the philosophical basis for an RDR approach.  This paper first appeared in EKAW 89 PDF

[5]     Edwards, 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 25: 27-34.

This was the first RDR system in industrial (clinical use). Although a large system with 2000 rules, it was limited by using single classification RDR

[6]     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