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 mades 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
This goal has been achieved and commercialised for knowledge-based systems for classification problems and there have been research proofs for a wide range of applications including: configuration or parameter tuning, text-processing, image processing, heuristic search, tuning genetic algorithms and multi-agent environments. There have also been machine-learning versions of RDR.
A number of researchers at UNSW and elsewhere have been involved in this research. Current research is aimed extending the range of possible applications of RDR and further integration with machine learning so that a system knows when it cannot deal adequately with a problem and needs further training and can discuss the situation with its trainer.
The focus of this work has mainly been knowledge-based system. The approach seems even more essential when one considers the increasing significance of preference-based systems, either personal or business preferences with applications across web services. The ultimate goal is a general software engineering solution whereby all systems can be easily evolved as requiremente evolve and further requirements emerge.
| 1. Compton, P. J., Horn R. et al (1989). Maintaining an expert system. Applications of Expert Systems, Quinlan JR (ed), Addison Wesley (PDF) |
| One of the early RDR papers outlining the knowledge acquisition process in RDR, for the real world expert system. |
| 2. Compton, P. J. and R. Jansen (1990). A philosophical basis for knowledge acquisition. Knowledge Acquisition 2: 241-257. (This paper first appeared in EKAW 89 PDF) |
| This was the second RDR paper published. It argues from a situated cognition perspective that experts can never explain how they reach a conclusion, rather they justify that a conclusion is correct, and provide this justification in a particular context. Therefore all knowledge acquisition must be incremental and case-based. |
| 3. Compton, P., Peters, L., Edwards, G., and Lavers, T.G., (2006 in press) Experience with Ripple-Down Rules. Knowledge-Based System Journal. (This paper first appeared in AI2005, the British SGAI conference PDF) |
| This paper documents the experience of a pathology laboratory using RDR to add clinical comments to patient biochemistry reports to assist GPs in patient management. The laboratory had built over 16,000 rules and processed over 6,000,00 patient reports. Knowledge acquisition was done by pathologists as a minor extension to their normal duties. |