Multiple classification ripple round rules: classifications as conditions
Bindoff, IK (2010) Multiple classification ripple round rules: classifications as conditions. PhD thesis, University of Tasmania.
The Ripple Down Rules (RDR) approach was developed by Compton and Jansen
(Compton and Jansen 1989; Compton and Jansen 1992) to effectively remove the
maintainability concerns of expert systems. This method was used to create an
advanced expert system to assist in the performance of medication reviews.
However, work in this area, although very successful, led to the realisation that the
RDR method did have its drawbacks, since with this method it was no longer
possible to define rules which were dependent on the presence or absence of a
classification or classifications.
Previously, attempts were made to address this, with Recursive RDR (Mulholland
1995), Nested RDR (Beydoun and Hoffmann 1997) and Repeat Inference MCRDR
(Compton and Richards 1999) all deserving acknowledgement in this regard.
However, all of these approaches had their own shortcomings. Recursive RDR
suffered problems with cyclic rule definitions, and was very domain specific
(Mulholland 1995). Nested RDR was concerned more with the idea of intermediate
classifications, rather than the more general problem of being able to define a rule
based on the presence/absence of a classification or classifications (Beydoun and
Hoffmann 1997; Beydoun and Hoffmann 2001). Repeat Inference MCRDR tackled
the general problem, but its approach at preventing cycles – to not allow the
retraction of assertions – fundamentally limits the scope of rules which can use
classifications as conditions. In addition to this, there is some minor concerns as to
the efficiency of the inference strategy, which simply repeatedly inferences the
knowledge base until no further changes to the outputs are detected (Compton and
Richards 1999; Finlayson 2008).
Having considered these various approaches, it was felt that a new method could be
defined which solved the problem, without need for such rigorous restrictions with
regards to how the method could be applied, and preferably with a more elegant
inference strategy. Presented in this thesis is the definition of a new method, entitled
Multiple Classification Ripple Round Rules which, the author feels, largely
achieves these goals. With this approach it is only necessary to revisit nodes which
might have been influenced by the addition/retraction of a given classification, and
cyclic rule definitions are managed by simply detecting when the expert is potentially defining one, and asking them to revise their rule. The method was
successfully applied to a complex configuration task, and further evaluated through
|Item Type:||Thesis (PhD)|
|Additional Information:||Copyright 2010 the Author|
|Deposited By:||UTAS ePrints Officer|
|Deposited On:||28 Apr 2011 15:02|
|Last Modified:||24 Jul 2012 14:17|
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