As well as the existing single classification approaches to this problem, there is also a multiple classification approach. Unlike the other methods which extended RDR, this approach was developed as an extension of MCRDR in particular. It is noted that in many ways a multiple classification domain is more suited to the features being discussed here, since it is known that some classifications are determined as a result of other classifications in these environments. It is also worth re-iterating that the Recursive RDR method was an example of this, but was developed using multiple single classification RDR systems presumably due to the lack of an available alternative at the time.
Repeat Inference MCRDR
The term Repeat Inference MCRDR is very poorly defined, and was perhaps born more out of discussion than actual implementation or publication. Of the two documents that have been identified which feature the term “Repeat Inference MCRDR”, Suryanto’s and Finlayson’s PhD theses, neither makes it clear where the term originated with Suryanto providing no reference at all (Suryanto 2005) and Finlayson providing a series of references which never themselves use the term (Finlayson 2008). Despite this, they are largely in agreement as to how they define the term themselves, and it does appear to have been born from publications by Compton and Richards in 1999 and 2000 in which a proposition for an extension to RDR was made in light of experiences with the Ion Chromatography (Mulholland 1995) and Sisyphus-I (Richards and Compton 1999) systems described earlier (Compton and Richards 1999; Compton and Richards 2000).
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The Repeat Inference MCRDR approach is essentially the same as a standard MCRDR approach in most regards, only it, like Nested RDR, allows the creation of intermediate classifications and modifies the inference process to handle this new feature (Compton and Richards 2000). This modification of the inference process is both its strength and its weakness as a method.
When performing an inference with Repeat Inference MCRDR the process is essentially similar to standard MCRDR except for three key features (Compton and Richards 2000).
1. The rules are inferred in strict chronological order of creation.
2. No retractions are allowed. A classification cannot be added then later removed.
3. The inference is recursively repeated, inserting the results to date as attributes of the case at each step, so long as changes are still being made to the result list.
By enforcing features 1 and 2 Repeat Inference MCRDR is able to side-step the issues of cyclic definitions which could otherwise plague the approach. However, this is also a somewhat limiting factor as the expert can become trapped with their past decisions, perhaps needing to make an exception to a past rule in order to be able to create the new rule they currently wish to make. As well as this, no interplay is possible between newer and older rules. That is, the newly added classification cannot in turn shut down a classification which was previously held to be true. Why should it hold that just because the expert created one rule before they created another rule that it is somehow more important? It would perhaps be preferable in some situations that the interplay between these classifications be preserved, so long as a final classification can be eventually resolved. However, it is conceded that in many or even most domains this level of freedom is probably not required, and that the Repeat Inference MCRDR approach would be perfectly suitable. It is perhaps only for the sake of ease of use and aesthetic elegance that one might argue against this approach, as it will not necessarily restrict the expert from reaching the outcomes they desire, but it may require the definition of more rules than might otherwise be necessary.
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General RDR
In 2004 a further extension and generalisation of the RDR approach was proposed, which was inclusive of the Repeat Inference MCRDR features described above, but also posited that a particular piece of knowledge did not have to be a rule but could instead be another KBS, or indeed any type of program, if the expert so desired (Compton, Cao et al. 2004). With this approach the hope is that far more complex systems will be able to be developed by joining together modular components that are good at particular sub-tasks.
Of course the obvious complication to such an approach is that a standardised input and output mechanism would need to be agreed upon whereby the given program expects input of the type that the broader KBS provides it and returns output of the type that the broader KBS expects, but this is not a complicated technical problem, but simply a matter of communication and agreement.
The Generalised RDR (GRDR) approach has been applied by combining multiple machine learning systems into a larger GRDR system for the task of detecting honeycombing in lung images (Singh and Compton 2005), and more recently to the task of complex multi-agent interactions in the simulated robo-cup soccer domain (Finlayson 2008).
4.3 Method
The approach suggested here is essentially covering much of the same ground as Repeat Inference MCRDR/GRDR and Nested RDR, but it approaches the problem from a different direction and attempts to solve configuration, allocation and planning style challenges within one enhanced knowledge base structure. It forgoes the restrictions of chronological ordering and disallowing retractions, instead opting for an approach which can allow a solution to gradually resolve, potentially after much interplay between competing classifications. Due to the resultant knowledge base structure and inference process, and the fact it is an extension of MCRDR, it has been named Multiple Classification Ripple Round Rules (MCRRR).