Multiple Classification Ripple Round Rules: Classifications as Conditions
Creators: name: Bindoff, Ivan
Publisher: name: University Of Tasmania
Year: 2023
Subjects: Knowledge representation and reasoning

description: 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 isThe 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 fu